Marketplace blog articles

New in Microsoft Marketplace: April 30, 2026

Nikhil_Viswanathan — Thu, 30 Apr 2026 13:00:00 GMT

Learn about 155 new offers that went live in Microsoft Marketplace, a single destination to find, try, and buy cloud solutions, AI apps, and agents to meet your business needs.

Get it now in our marketplace
	ACT Security Platform: Act Security is a cloud infrastructure security platform that enforces zero-trust policies by unifying identity and network authorization in Microsoft Azure. It reveals access gaps, simulates policy changes safely, and enforces least-privilege access without agents. It proactively stops lateral movement and defends against AI-driven attacks in cloud environments.
	Alert Me – SharePoint Notifications: Alert Me is a SaaS tool that enhances Microsoft SharePoint notifications by monitoring document libraries, lists, and folders for changes. It sends immediate or scheduled alerts for created, modified, or deleted items, improving team visibility and response. It integrates securely with Microsoft 365, ensuring timely updates for critical content management.
	Bolt.new - AI-Powered Digital Product Creation : Bolt.new accelerates product delivery by combining AI coding agents with Microsoft Azure infrastructure, enabling teams to build, prototype, and deploy production-ready frontend code via natural language. It unifies design, validation, and development in-browser, reducing dependencies and rework while integrating seamlessly with existing Azure CI/CD pipelines for faster, secure software shipping.
	BUILD - Project Setup Automation: BUILD is a guided setup wizard that automates Jira or Azure DevOps project configuration in minutes. It creates boards, columns, workflows, custom fields, and hierarchy levels automatically, eliminating manual errors. Ideal for enterprises, it offers a one-time fee per project and supports advanced hierarchy beyond default levels.
	Charlo Studio - AI-Powered Tender Response Platform for Construction Teams : Charlo Studio is an AI-powered platform that streamlines tender response writing for construction and professional services. It maps requirements, leverages past proposals, and generates branded drafts for expert review. Secure, enterprise-grade, and Australian-hosted, it reduces hours of drafting to minutes, ensuring consistent, high-quality submissions.
	ConsoleSentinel: ConsoleSentinel is an autonomous QA platform that detects errors, security vulnerabilities, and accessibility issues pre-production. It offers AI-powered error classification, OWASP-aligned security scans, WCAG 2.1 validation, and real-time monitoring. Integrating with CI/CD pipelines, it provides remediation guidance to help teams deliver higher-quality software faster.
	Contracts 365 For Power Platform and Dataverse: Contracts 365 is a SaaS-based Contract Lifecycle Management (CLM) solution for companies that run on Microsoft 365. Utilizing AI-powered workflows, the solution enhances efficiency, compliance, and transparency with role-specific dashboards, automated notifications, and robust search, streamlining contract processes from request to renewal within the Microsoft Power Platform.
	Cyera Omni DLP Investigation Agent: This AI-powered tool from Cyera enables security teams to investigate and prioritize data loss prevention incidents across email, endpoints, cloud, messaging, and generative AI. Cyera Omni DLP Investigation Agent analyzes user behavior, detects patterns, integrates with Microsoft Purview and provides actionable risk reports with organizational context for efficient incident triage.
	DIRA ERP: Novel Cloud's DIRA ERP is an enterprise resource planning application designed for businesses in Africa. DIRA unifies finance, inventory, HR, and sales into one seamless platform, reduces operational friction, strengthens compliance and control, and helps teams shift from daily firefighting to predictable, data-driven growth. DIRA offers multi-currency support, transparent flat-rate pricing, and secure cloud hosting to simplify deployment and scale with demand.
	DMAIC: DMAIC (Define, Measure, Analyze, Improve, Control) provides a globally recognized framework used in Lean Six Sigma and operational excellence programs to improve performance. The DMAIC application, built on Dataverse and Microsoft Power BI, converts this proven method into a controlled, scalable system to increase project success rates, shorten delivery timelines, and provides full visibility into improvement initiatives within the Microsoft environment.
	Doctor 360 – Intelligent Agentic Healthcare Platform Powered by Microsoft Copilot: Doctor 360 from Tech Unicorn is an AI-powered healthcare platform on Microsoft Azure. It integrates clinical, HR, and operational workflows using Microsoft Copilot and offers EMR summarization, patient engagement, and compliance with global standards. Features include multilingual support, predictive analytics, and seamless interoperability for large health systems and ministries of health.
	eMove Multilanguage Edition: 5-Minute Movement Breaks in Outlook: eMove Multilanguage Edition integrates desk-friendly guided movement breaks with a light exercise band directly into Microsoft Outlook to help office, hybrid, and remote teams build healthier daily routines. The solution supports English, Finnish, French, German, Italian, Spanish, and Swedish, delivering consistent guidance for international teams across regions and time zones.
	Energy Assistant: MOOST is a software-only platform on Microsoft Azure that analyzes smart meter data to create detailed household energy profiles. It enables utilities to understand usage patterns, engage customers with personalized insights, identify assets like EVs and solar, and automate energy flexibility, enhancing efficiency without extra hardware.
	Ramp: Ramp is a financial operations platform that saves companies time and money by combining corporate cards, payments, vendor management, procurement, travel booking, and automated bookkeeping. Trusted by more than 55,000 businesses, it automates expense reports, controls spending, speeds up AP processing, enforces policies, and centralizes procurement efficiently.
	Fidelis Autotask Mirror: Autotask Mirror from Fidelis is a fully managed application on Microsoft Azure that continuously synchs your Autotask PSA data into an Azure SQL Server database. The solution creates relational tables and foreign keys that Microsoft Power BI detects automatically, enabling reporting on more than 200 entity types without API pagination, rate limits, or specialized Autotask API expertise.
	FinOpsly Value Control Platform for Cloud Data and AI: FinOpsly’s AI-native FinOps platform shifts enterprises from cost visibility to proactive governance across cloud, data, and AI spend. Using a multi-agent architecture with human oversight, the platform detects anomalies, forecasts spend and executes governed optimizations. Designed for finance, engineering, and IT teams, it bridges the gap between cost visibility and actionable optimization.
	FledgeWorks HRM Platform – Core HR, Talent Management & Workforce Productivity: FledgeWorks is an all-in-one platform for strategic, agile people management. It unifies HR, managers, employees, and talent in one accessible ecosystem. Scalable and modular, it streamlines workflows, automates tasks, and supports organizations of all sizes, enhancing onboarding, performance, communication, and compliance for seamless operations.
	Fyxer: Fyxer's AI-powered productivity platform automates email organization, drafts personalized replies, and delivers instant meeting notes. It streamlines scheduling, supports multiple inboxes, integrates with Gmail, Outlook, and HubSpot, and offers enterprise-grade security. Fyxer boosts efficiency and reduces administrative tasks helping users reclaim time and streamline communication across industries.
	Lesos Support Team: Support Team by Lesos AI automates IT support by analyzing and resolving ServiceNow tickets using AI. It integrates with Microsoft 365 for tasks like license management and MFA resets, supports on-premises operations, enforces security tiers, and offers an admin portal for monitoring and policy management, ensuring efficient, compliant IT service delivery.
	Medware Laudos UX: Medware Laudos UX is an AI-powered clinical management platform designed for healthcare professionals. It integrates electronic medical records and clinical management, enhancing organization, efficiency, and diagnostic accuracy. Ideal for clinics and hospitals, it addresses time constraints and information management challenges, ensuring precise medical reports and streamlined workflows.
	Meeedly Partner - Enablement - Direct: Meeedly is an enterprise meeting coordination platform that integrates with Microsoft 365 and Google Workspace to enable seamless cross-organization scheduling. It eliminates manual follow-ups and platform limitations by securely connecting different systems, providing real-time availability, and streamlining group meeting management for modern businesses.
	Mr. RIGANTI – AI Coding Agent and Code Review for Azure DevOps: Mr. RIGANTI is an AI coding agent for Azure DevOps that automates code reviews, feature implementation, and bug fixes within your pipelines. It ensures security, privacy, and compliance using Anthropic models. Ideal for teams invested in Azure DevOps, it offers flexible pricing and eliminates the need to migrate to GitHub.
	Novulo Insurance Solution: Novulo Insurance Platform is a scalable, cloud-native solution on Microsoft Azure that modernizes insurance operations. It offers composable architecture for flexible product configuration, policy administration, claims, and integrations. Supporting multi-market needs, AI readiness, and seamless external connections, it reduces costs and accelerates product launches for insurers globally.
	NXT AP: NXT AP for SAP automates accounts payable with AI-driven invoice data extraction, validation, and posting directly into SAP FI/MM. It supports multi-currency, exception handling, and real-time analytics, ensuring accuracy and compliance. Built on Azure, it offers scalable, consumption-based pricing, streamlining AP workflows for modern enterprises.
	PaperBolt PDF Tools: PaperBolt PDF Tools offers an intuitive platform for editing, merging, splitting, and converting PDFs. Ideal for professionals and students, it simplifies document management, enhances productivity, and supports various file types. Its user-friendly interface makes complex PDF tasks easy, providing a reliable solution for efficient digital document handling.
	Polish Companies Financial Insights: Access clean, structured Polish financial data instantly with this platform. It transforms complex government XMLs into ready-to-analyze tables, enabling granular filtering by year and industry. Ideal for market research, competitor analysis, and B2B lead generation, it simplifies data export for seamless integration into spreadsheets and dashboards.
	Quest Automated Data Product Factory: Quest's Automated Data Product Factory (ADPF) streamlines data product creation using AI, natural language input, and automated governance. It delivers trusted, governed data products in weeks, reducing costs and manual effort. ADPF offers trust scoring, self-service publishing, and compliance support, accelerating AI initiatives with improved data quality and efficiency.
	Secure Document Exchange: Confidencial's Secure Document Exchange (SDX) ensures secure, compliant sharing of sensitive documents with end-to-end encryption, granular access controls, and audit trails. It integrates with popular tools, offers user-friendly access without accounts, customizable templates, and supports regulatory compliance like HIPAA and GDPR, scaling for organizations of all sizes.
	SmartSpace.ai: SmartSpace.ai by CreativeQ enables enterprises to deploy, scale, and manage generative AI with secure, customizable workflows. The platform accelerates adoption by rapidly provisioning GenAI infrastructure, enabling multi-LLM and agent-based workflow builds, and enterprise integrations through APIs. Ideal for businesses seeking fast, flexible GenAI adoption, SmartSpace.ai maintains strong governance, privacy, and compliance within the customer’s environment.
	Time Machine Agent: Time Machine Agent from iLink Systems uses Microsoft Security Copilot’s AI to analyze months of historical data in Microsoft Sentinel, hunting and investigating threats. It reconstructs attack timelines across the Sentinel Data Lake and transforms insights into active detections, enhancing threat detection and response by uncovering hidden malicious activities over extended periods.
	UNICUS AI Business Information Agent: UNICUS AI Business Verification Agent provides real-time, verified business data from official global sources. It delivers standardized company details via APIs or exports, supporting onboarding, compliance, credit, and risk systems. Ideal for financial institutions, it ensures accurate, multi-source verification for KYB, due diligence, and regulatory compliance.
	UNICUS AI Financial Data Agent: UNICUS AI Financial Data Agent extracts, validates, and reconciles financial data from diverse documents and formats, supporting 100+ languages. It ensures audit-ready, standardized outputs aligned with global standards, detects inconsistencies, and enables high-volume processing for banks and financial institutions, enhancing credit risk analysis, compliance, and due diligence.
	Validata Cloud Enterprise: Validata Cloud Enterprise is an AI-powered SaaS for data validation and reconciliation across cloud and hybrid platforms. It detects missing, extra, and mismatched records, providing clear reports and SQL scripts. It ensures data accuracy, speeds issue detection, and allows control over discrepancy resolution without disrupting existing workflows.
	VaultGuard360: VaultGuard360 from Sentinel Vault Systems monitors Azure Key Vault certificates, secrets, and keys across subscriptions, preventing costly outages from expirations. It offers automated alerts, audit logs for compliance, secure read-only access, and integrates with DevOps pipelines. The solution deploys as an Azure managed application with a read-only security model and all data stays in your subscription.
	VMR DMS (Document Management System): VMR DMS by ViewMyRecords offers a secure, flexible document management system for industries like banking and healthcare. It enables digitization, secure sharing, workflow automation, and analytics with cloud or on-premises deployment. Features include access control, customizable forms, and professional support, ensuring efficient, compliant digital transformation and remote work readiness.
	Web Search and News API - Real-time Intelligence for AI Agents: You.com's APIs provide real-time, accurate web and news data with source citations, enabling AI products to access current information effortlessly. The platform delivers structured, AI-ready results at enterprise scale, enabling teams to integrate current information into LLMs and custom agents with minimal effort. It supports mission-critical deployments with enterprise-grade reliability and performance.
	ZINFI Partner Relationship Management (PRM): ZINFI Technologies' SaaS Unified Partner Management (UPM) platform helps vendors and channel partners drive predictable, profitable growth by streamlining the full partner lifecycle. The platform provides six core integrated solution — Onboard, Enable, Market, Sell, Incentivize, and Accelerate — covering PRM, marketing, incentives, and affiliate management globally in 26 countries.

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	SecureBaseline Cloud - All-in-One - Automated CIS Benchmarks Compliance for Linux Servers
	Red Hat Enterprise Linux 9.7 for Enterprise and Cloud Workloads RHEL 9.7 for Cloud, DevOps, and Enterprise Applications

Go further with workshops, proofs of concept, and implementations
	AI Center of Excellence (AI CoE) – Framework for Responsible Adoption of AI: Datco’s AI Center of Excellence provides a repeatable delivery framework to design, govern, and operationalize AI on Microsoft Azure. The experts from Datco will define the operating model (centralized, hub-and-spoke, federated, or hybrid), implement AI/GenAI governance for LLM, RAG, and agent solutions, and standardize MLOps practices, technical controls, and security baselines.
	Alteryx to Microsoft Fabric Migration: Sonata IntelliConvert will automate migration of Alteryx workflows to Microsoft Fabric, enabling real-time analytics, unified data governance, and AI integration. This service helps reduce cost, complexity, and manual effort while accelerating insights and compliance. Ideal for CIOs, IT leaders, and analysts seeking scalable, cloud-native, AI-ready enterprise data modernization.
	B2F Implementation on Power BI: B2F offers expert Microsoft Power BI consulting, delivering data modeling, dashboard development, and report automation. Their experts will help integrate Power BI with Microsoft Dynamics 365 Business Central and other ERPs, providing real-time analytics, KPI tracking, and centralized reporting. This service enhances business visibility, efficiency, and data-driven decision-making through tailored, scalable BI tools.
	Clinical Data Architecture – Data Quality and Semantic Foundation for Pharma and Biotech: DataArt’s Azure-native solution unifies fragmented clinical data for Pharma and Biotech, automating quality checks and harmonization across CROs. It accelerates regulatory compliance, reduces manual work, and enables AI-driven insights with Azure OpenAI integration, ensuring data integrity, semantic consistency, and scalable clinical trial analytics.
	CMMC Tenant Setup: This CMMC compliance configuration from 12:34 MicroTechnologies offers Windows 365 deployment, security baselines for Windows 11, Microsoft Defender, Edge, audit and logging, local policies, application hardening, and cloud controls. It supports Microsoft Intune, GPO, or manual deployment.
	Copilot for Microsoft 365: Workshop: All Covered, a division of Konica Minolta, offers Copilot for Microsoft 365 readiness workshop to help businesses maximize AI benefits by assessing readiness, identifying key use cases, and creating a deployment strategy. Tailored for IT and business leaders, this service includes expert guidance, technical evaluation, and practical plans to ensure successful Copilot adoption and improved productivity.
	CRM Envisioning: 2- to 3-Week Workshop: HBS will help organizations assess and plan migration from legacy CRMs to Microsoft Dynamics 365. This workshop delivers a business case with ROI, a migration roadmap, and demos of Dynamics 365 capabilities, enabling improved productivity, AI-powered sales, and enhanced customer experience.
	Digital Sovereignty and Regulatory Compliance Workshop: Reply's assessment will evaluate regulatory compliance and digital sovereignty in Microsoft 365 using Microsoft Purview Compliance Manager. This service reviews configurations, identifies gaps, and assesses risks related to data handling and governance. Deliverables include compliance posture, risk areas, regulatory mapping, and recommendations to support improved compliance and sovereignty initiatives.
	FPT Software: DevOps & DevSecOps on Azure with GitHub: This service from FPT Software standardizes and automates Azure DevOps pipelines using GitHub Actions and Infrastructure as Code. It enhances deployment speed, security, and reliability for cloud-native workloads. Features include CI/CD implementation, DevSecOps integration, monitoring, and technical documentation, providing a scalable, secure, and efficient Microsoft Azure DevOps platform.
	FY26 AKS Deployment with Private AKS Cluster: IFI Techsolutions will design and deploy a secure, private Azure Kubernetes Service (AKS) cluster with Zero Trust architecture, automated Terraform deployment, and integrated DevSecOps. This engagement strengthens governance and operational visibility while supporting compliance alignment for frameworks such as PCI-DSS, SOC 2, HIPAA, and ISO 27001.
	FY26 Azure VM Scale Set Infrastructure Automation: This Azure VM Scale Set solution from IFI Techsolutions offers secure, scalable, and production-ready infrastructure with automated CI/CD deployment, Terraform provisioning, and secure ingress via Application Gateway. It supports enterprise workloads, ensures compliance (PCI-DSS, SOC 2, HIPAA), and provides monitoring, governance, and flexible update strategies for efficient, consistent operations.
	Hable Copilot Adoption Framework: Implementation: This implementation by Cloud Direct drives AI adoption through targeted communications, role-based learning, and coaching. It assesses current workflows, delivers structured campaigns, and supports employees with practical guidance. This approach boosts confidence, ensures consistent use, reduces risks, and embeds Microsoft Copilot into daily tasks, creating a scalable, responsible adoption model.
	Implementation of Azure Local: QDS will deliver end-to-end Azure Local services by planning, deploying, and operating hybrid environments in customers’ on-premises or edge locations while applying eligible Azure credits. They support hybrid workloads like Azure Virtual Desktop, Azure Kubernetes Service, and Microsoft 365 Local integration. QDS will guide adoption through a structured approach with large, medium, and small implementation of selected use cases.
	Microsoft 365 Managed Service: Implementation: Microsoft 365 Managed Service by Mobile Mentor optimizes and secures Microsoft 365 environments through ongoing management, automation, and expert configurations. It enhances security, automates device and user management, improves visibility, and reduces costs by aligning tools and processes, ensuring organizations maximize their Microsoft 365 investment efficiently.
	Microsoft Intune Deployment and Modern Endpoint Management Services: Lightspire’s Microsoft Intune Deployment Service helps organizations modernize endpoint management by transitioning from legacy tools to secure, scalable cloud-based solutions. It supports Windows, macOS, iOS, and Android, offering assessment, design, and implementation. Hands‑on training, user onboarding documentation, and self‑service resources are included.
	Microsoft Purview Data Security Implementation: ProArch’s Microsoft Purview Data Security Implementation service configures and enforces data protection, classification, retention, and DLP policies in Microsoft 365. It reduces sensitive data exposure, enhances visibility, supports compliance, and prepares organizations for secure AI and Copilot use, delivering hands-on setup, validation, and knowledge transfer within a defined scope.
	Power BI Advanced Training: Antdata's advanced instructor-led Microsoft Power BI workshop will enhance your skills in data modeling, DAX calculations, and performance optimization. It focuses on best practices for scalable, maintainable reports and dashboards. Participants engage in hands-on exercises and real-world scenarios to improve analytical capabilities, report quality, efficiency, and business intelligence governance.
	Predictive Policing (Police AI Use Case): Telefónica Tech's AI-driven predictive policing accelerator helps police forces shift from reactive response to proactive, intelligence-led prevention. The solution unifies fragmented CAD, RMS, intelligence, and partner data into a secure, governed Azure analytics platform (Azure Databricks or Microsoft Fabric) and applies machine learning to identify hotspots, forecast demand, and optimize resources.
	SAP S/4HANA Data Modernization with Microsoft Fabric and AI: Credenca modernizes SAP S/4HANA data with Microsoft Fabric, enabling unified data ingestion, lakehouse architecture, and AI-ready analytics. It delivers scalable reporting, Power BI dashboards, predictive insights, and governance. Benefits include faster insights, improved supply chain planning, reduced manual efforts, and seamless Microsoft ecosystem integration for intelligent decision-making.
	Terminal exe: All Your BI delivers Terminal Data Infrastructure (TDI) as a managed service on Microsoft Azure by implementing, configuring, and operating Terminal.exe for container terminal operators. Terminal.exe, an Azure-based SaaS platform, unifies data from multiple systems into a governed KPI model. It offers managed deployment, cross-terminal benchmarking, and AI-driven analytics, enabling consistent insights and improved performance.
	Windows 365 Envisioning and Proof of Concept: Advania’s proof of concept/pilot will validate the Cloud PC experience for targeted personas, applications, and security controls in the customer environment. The service includes design, configuration, user onboarding, monitoring, and a final report with recommendations, adoption roadmap, and costs.

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Abacus Integration for Microsoft Teams

Acumens Defective Inventory Management

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AdaptPro Enterprise - Sapnity

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Agentmatic - Sovereign Agentic AI Platform

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Azure FinOps Services – Azure Cost Optimization Assessment

Azure Virtual Desktop Health Check

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Creation - Manufacturing Bill of Materials (BOM) Maintenance

Cybereinforce – Defender IOC Enforcement for Chrome, Firefox and Safari

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This content was generated by Microsoft Azure OpenAI, then revised by human editors.

Design predictable AI performance to scale selling through Microsoft Marketplace

Julio_Colon — Wed, 29 Apr 2026 16:34:57 GMT

Trade-offs in AI performance: latency, quality and cost

Imagine a software company launches a customer trial for its new AI assistant through Microsoft Marketplace. The trial begins smoothly — until more complex queries take longer than a few seconds to return a response. The cause isn’t model failure. It’s an unbounded Retrieval‑Augmented Generation (RAG) pipeline retrieving 50 documents per query before synthesizing an answer.

Latency increases. Runtime token usage expands. Trial‑stage infrastructure cost rises immediately.

This exposes the core runtime tradeoff in enterprise AI systems:

Latency ↔ Quality ↔ Cost

Improving response quality often increases retrieval depth. Increasing retrieval depth expands token usage. Expanded token usage drives both cost and latency upward.

This post is part of a series on building and publishing well-architected AI apps and agents in Microsoft Marketplace. The series focuses on AI apps and agents that are architected, hosted, and operated on Azure, with guidance aligned to building and selling solutions through Microsoft Marketplace.

You can always get curated step-by-step guidance through building, publishing and selling apps for Marketplace through App Advisor.

How traditional cost model assumptions break down for AI

In classic software models, you expect predictable runtime costs such as license allocations, storage, compute time, bandwidth consumption, etc. But in AI-powered systems, that stability gives way to new complexities driven by token-based cost structures. These costs scale in unexpected ways, depending on the length of generated outputs, the depth of information retrieval, the number of reasoning steps an agent performs, and how often external tools are invoked.

Consider the RAG pipeline scenario: retrieving five documents for a single query might create a 3,000-token prompt. If the pipeline instead pulls 50 documents, that prompt balloons to 15,000 tokens—before the AI even begins to infer an answer. And the unpredictability doesn’t stop there. Agent orchestration can introduce even more variability. Planning steps may stretch or shrink depending on the query, tool-calling systems might retry failed executions multiple times, and multi-branch workflows can run in parallel, all amplifying token consumption and cost.

Keep costs bounded without sacrificing quality

While unpredictable token usage and orchestration steps can quickly escalate infrastructure costs in AI-powered systems, design choices can prevent runaway expenses without compromising the quality of responses. To achieve this, engineers must balance procurement expectations set by pricing with real-time operational controls. For instance, use a multi-model tiered routing strategy to allow less complex queries to be handled by lightweight models, reserving advanced reasoning models for more demanding tasks. Combining this with token budgeting strategies—such as per-session caps and API Management token-limit policies—ensures that each interaction remains within defined boundaries.

Cost-aware orchestration paths become essential when running AI workloads across multiple tenants, especially when retries and multi-branch workflows threaten to multiply inference consumption. By calibrating runtime guardrails to performance and cost signals, AI systems can be designed to fail gracefully and predictably, preventing ambiguous and expensive failures. Ultimately, the goal is to deliver high-quality results at scale, maintaining control over both costs and performance as usage grows.

Achieving predictable latency: Business best practices across each layer

For enterprise AI systems, ensuring fast and consistent response times—while balancing quality and cost—is a top priority. Predictable latency requires intentional design at every layer of your architecture.

Interaction Layer: Set clear boundaries for incoming requests using Azure API Management rate‑limit and quota policies, such as rate-limit-by-key, scoped per subscription or tenant. These controls cap request throughput and request volume over time, preventing traffic spikes from overwhelming downstream AI services and ensuring consistent, predictable response behavior across tenants.
Orchestration Layer: Define and restrict system execution paths. Limit reasoning depth in workflows so complex operations don’t unexpectedly slow things down. This keeps your business processes running smoothly and predictably. At the API boundary, Azure API Management can enforce deterministic routing, retry limits, and timeout policies, while backend orchestration services such as Azure Durable Functions or Logic Apps manage multi‑step workflows with explicit bounds on execution depth and retries.
Model Layer: Choose models based on expected concurrency needs. Use fallback routing to redirect traffic during busy periods—so users don’t experience delays. Rely on Azure OpenAI Provisioned Throughput Units (PTUs) for steady baseline performance and enable PAYG overflow to handle temporary surges without sacrificing speed. Microsoft AI Foundry can be used to centrally manage model selection and routing policies, enabling consistent fallback strategies and governed use of multiple models across agents and workloads.
Retrieval Layer: Optimize your document indexing and narrow the scope of data being searched. This means users get relevant information faster, and your system avoids unnecessary slowdowns. Services such as Azure AI Search enable scoped, indexed retrieval over structured and unstructured content, while integrating with Azure Blob Storage or Azure Cosmos DB as source data stores to support predictable, low‑latency access for RAG‑based AI workflows.
Data Layer: Keep your compute and storage resources close together and aligned regionally. By minimizing cross-region data transfers, you reduce latency and boost reliability—critical for enterprise-grade AI.

Across every layer, publishers are responsible for designing bounded, predictable defaults, while customers govern configuration, scale, and operational posture—a clear separation that reduces friction, improves trial outcomes, and accelerates Marketplace adoption.

By applying these best practices decisively at every layer, software development companies can move beyond isolated optimizations and design AI solutions that behave predictably under real customer load. This approach enables customers to run meaningful trials, validate performance and cost assumptions early, and scale with confidence as demand grows. More importantly, it establishes a repeatable engineering foundation—one that supports faster iteration, clearer operational ownership, and successful commercialization through Microsoft Marketplace.

Design caching into your architecture from the start

Predictable AI performance relies on caching that’s intentionally designed into the architecture—not added after systems are already under load. In agent‑driven and retrieval‑augmented workflows, caching is foundational to controlling latency, stabilizing runtime costs, and keeping execution behavior consistent as usage scales.

Effective designs cache work wherever outcomes are deterministic. Request‑level and semantic caching reduce redundant inference when users submit identical or meaning‑equivalent queries, while Azure API Management paired with Azure Managed Redis enables governed reuse at the intent level. Retrieval pipelines benefit from embedding and retrieval caching, which avoids repeated vectorization and unnecessary search overhead. Within orchestration flows, tool‑level caching ensures stable responses for deterministic calls such as policy checks or configuration lookups, and agent plan caching allows reasoning paths to be reused without re‑incurring planning cost.

Caching must be paired with clear invalidation strategies—time‑based expiration, context‑aware refresh, and event‑driven updates—to preserve correctness and trust. In Marketplace deployments, multi‑tenant cache isolation and observability are essential. When caching is visible, governed, and intentional, it becomes a powerful enabler of predictable scale.

What’s next in the journey

With performance and cost under control, the next question is how your system behaves when something goes wrong. The next post explores API resilience and reliability patterns—because predictable performance only matters if your AI system continues to function through the inevitable failures that occur at Marketplace scale.

Key Resources

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New in Microsoft Marketplace: April 29, 2026

Nikhil_Viswanathan — Wed, 29 Apr 2026 13:00:00 GMT

Learn about 201 new offers that went live in Microsoft Marketplace, a single destination to find, try, and buy cloud solutions, AI apps, and agents to meet your business needs.

Get it now in our marketplace
	365SAMPulse: 365SAMPulse is a lightweight SaaS tool for small to midsize organizations that provides clear visibility into Microsoft 365 usage, licensing, and adoption. It offers real-time dashboards, renewal alerts, and health and governance insights, helping teams optimize spend, improve service adoption, and make informed renewal and investment decisions.
	AccessPoint - SaaS Subscription: AccessPoint is a SAR management solution for government and regulated enterprises, ensuring compliance with ATIA, FOIA, and GDPR. It offers full request lifecycle management, document redaction, role-based privacy, Microsoft Azure data sovereignty, Microsoft 365 integration, multilingual support, and pre-built regulatory configurations.
	AgentConnect: AgentConnect integrates Microsoft Copilot Studio with telephony, enabling AI agents to handle calls via SIP trunks or PSTN. It offers real-time transcription, multilingual support, call transfer, diagnostics, and admin controls. The solution requires an Azure subscription, SIP/PSTN provider, and Copilot Studio agent, with full data control.
	Aqua Plus Powered by FIDO AI: Aqua Plus uses advanced FIDO AI to detect and reduce water leakage in utility networks, helping businesses secure water supply, meet water-positive goals, and reduce emissions. It delivers verified, local, scalable water savings with rapid results and supports transparent reporting and risk management.
	Artemis Intelligence AI, Can!: AI, Can! enables instant deployment of secure, compliant, production-grade AI infrastructure on Azure, eliminating months of setup. It offers 10-minute provisioning, built-in DevSecOps, cost optimization, and runs entirely within your Azure tenant, making it ideal for startups and enterprises launching AI apps without infrastructure delays.
	Atatus: Atatus is an all-in-one observability platform offering real-time monitoring, distributed tracing, log management, Kubernetes insights, synthetic testing, and AI-powered anomaly detection. Available as SaaS or on-premises, it provides unified telemetry for faster issue resolution, performance optimization, and reliable application management across cloud and on-premises environments.
	B-Able: B-Able is a SaaS tool that evaluates business project financial viability and efficiency using key coefficients. It features scenario modeling with an interactive optimizer, visualizes gaps between necessary and expected values, and manages inputs like general info, historical data, and forecasts to support informed decision-making.
	Baazzu: Baazzu is an Azure-native integration platform offering a standardized, secure, and compliant foundation for building integrations. Delivered as a managed application within an organization’s Azure tenant, it enables faster onboarding, reduces design efforts, ensures consistent governance, and improves manageability with built-in monitoring.
	Betterfly QHSE and ISO Management Platform: Betterfly is a cloud-native SaaS platform unifying QHSE management, process automation, and AI analytics for ISO compliance. It replaces spreadsheets with a full audit lifecycle, BPMN process engine, AI-assisted document management, risk tracking, and real-time dashboards. Its AI copilot enables natural language queries, boosting efficiency and continuous improvement.
	BetterRoofingPro: BetterRoofingPro is an AI-powered roofing platform that streamlines lead generation, job management, scheduling, and finances in one system. It automates outreach, tracks projects, and integrates with QuickBooks, helping small to midsize contractors increase efficiency, improve coordination, and win more jobs while reducing administrative tasks.
	BI Pixie: BI Pixie enhances Microsoft Power BI by tracking user interactions invisibly, capturing clicks, filter use, and feedback without altering reports. It offers heatmaps, surveys, and security auditing to improve design and measure BI value. Features include data control, team access, and Microsoft Fabric integration.
	Bytello Share: Bytello Share enables flexible screen sharing with a floating window mode for multitasking. It supports Miracast, Airplay, and Chromecast without app installation. Users can share screens instantly via share.bytello.com and have full control over all connected devices from any authorized device or large screen.
	Cohesity NextGen Cloud Edition by ICT: ICT’s Cohesity NextGen Cloud Edition offers unified backup, recovery, and disaster recovery across on-premises, cloud, and SaaS environments. It provides cost-efficient, scalable data management with deduplication and compression, enhanced data resilience, and secure replication. Centralized multi-cloud control simplifies operations, ensuring business continuity and flexible, modern data protection.
	Cosine Enterprise Air Gapped: Cosine is an autonomous AI software engineering platform that accelerates development by delegating tasks like bug fixes, feature building, and testing to AI agents. It integrates with tools like GitHub and Azure DevOps; supports secure, air-gapped deployment; and modernizes legacy codebases, boosting enterprise engineering productivity and governance.
	Cyera DSPM Investigation Agent: Cyera DSPM Investigation Agent is an AI-powered tool for security analysts to assess data security across AWS, Azure, GCP, and SaaS. It identifies risky assets, analyzes security gaps, calculates risk scores, and provides actionable remediation guidance via Microsoft Security Copilot and Microsoft Sentinel integration, enabling efficient multi-cloud security investigations.
	Degreed Academies: Degreed Academies offers structured, cohort-based programs that rapidly build role-specific skills through curated resources, hands-on practice, and measurable outcomes. It accelerates time-to-proficiency from months to weeks, aligning learning with business goals and integrating with Degreed tools to drive efficient workforce transformation and skill mastery.
	Degreed Learning: Degreed Learning is an AI-powered platform that identifies skill gaps and delivers personalized, skill-focused learning pathways aligned with business priorities. It features intelligent search, adaptive recommendations, a built-in open library, and seamless Microsoft integration, enabling organizations to drive measurable workforce skill development without relying on external content.
	Degreed Maestro: Degreed Maestro is an AI-powered tool that accelerates skill development through personalized learning pathways, AI coaching agents, and real-world practice. It integrates with Microsoft AI tools and enterprise data to deliver tailored, interactive training, enabling employees to build critical skills quickly and effectively within their workflow.
	Degreed Skills Plus: Degreed Skills Plus is a skills intelligence platform that helps organizations define, measure, and manage workforce capabilities. It offers customizable taxonomies, benchmarks skills against industry data, identifies gaps, and integrates with learning and talent strategies, enabling data-driven workforce planning and development aligned with business goals.
	Discovery Platform Powered by BMC Helix: Diyar Discovery Platform by BMC Helix Discovery offers agentless hybrid IT asset and service discovery across on-premises and cloud environments. Deployed on Azure, it provides accurate, real-time configuration intelligence, automated CMDB updates, dependency mapping, and enhanced governance, improving service reliability, impact analysis, and audit readiness.
	Dynamic Icon: This Power BI custom visual offers more than 1500 dynamic icons to replace complex data with intuitive symbols, enhancing report clarity and aesthetics. It features true data-driven integration and helps users quickly understand KPIs and statuses, making reports visually engaging.
	EZII Attendance: This unified attendance system using biometric, geofencing, QR, and mobile tracking offers real-time dashboards, smart rostering, and payroll-ready reports. It features day-wise attendance grids, leave tracking, and compliance-ready summaries, ensuring accurate workforce management, seamless regulatory adherence, and efficient payroll processing without spreadsheets or guesswork.
	EZII Expense: EZII Expense combines more than 20 years of finance expertise with AI to streamline expense management. It offers digital claims, approvals, reimbursements, petty cash handling, real-time reconciliation, and comprehensive reports. Mobile-friendly with guided tutorials, it ensures compliance, speed, and ease for employees, managers, and admins in one unified platform.
	EZII Leave: EZII Leave streamlines workplace leave management with smart policies and seamless workflows. Employees can plan vacations, log short breaks, collaborate by assigning tasks before leave, and track compensatory off hours. This data-driven system enhances transparency and rewards extra effort, fostering a productive and supportive work culture.
	EZII Payroll: EZII Payroll offers a comprehensive, customizable platform for seamless payroll management, including automated pay runs, tax compliance, employee lifecycle workflows, loans, and variable pay. It supports all work contracts, real-time processing, detailed reporting, ESOP valuation, leave encashment, and bank integrations, ensuring efficient, compliant payroll operations.
	Ezra360 Student Accommodation: Ezra360 Student Accommodation streamlines student housing management for universities and providers. It offers tools for handling requests, room allocation, payment tracking, and communication. With an intuitive interface, it simplifies operations, ensures fair allocation, and improves housing services, enhancing the overall student accommodation experience.
	Gixo Legal and Compliance - AI Legal Document Generator: Gixo Legal and Compliance streamlines drafting contracts, policies, and compliance documents with 22 templates, multi-party support, and jurisdiction-aware wording. It offers OCR template matching, guided forms, editing tools, and multiple export formats.
	GOBI IQ - Enterprise AI and Orchestration: ActionIQ by GOBI is a secure, tenant-hosted AI platform that automates workflows across Microsoft 365, Dynamics 365, Salesforce, and more. It offers agentic AI for reasoning and task execution, pre-built integrations, and enterprise-grade security, enabling teams to boost productivity and operational impact.
	Healthy Plate Enterprise: Healthy Plate Enterprise is an AI-driven corporate wellness solution tailored for Asian diets. It simplifies meal logging with image recognition, promotes sustainable healthy habits, and offers management dashboards for tracking engagement and health metrics. Built on Azure, it ensures privacy and scalability and supports long-term wellness program success.
	House Resolutions Tracker Solution: House Resolution Tracker is a Microsoft Power Platform-based governance tool managing parliamentary resolutions from adoption to closure. It replaces manual processes with automated workflows, secure access, audit trails, and real-time analytics. The platform enhances transparency, accountability, and efficiency in resolution tracking, reducing compliance risks and enabling proactive oversight.
	iBEAM FormLift: iBEAM FormLift modernizes legacy Oracle Forms by combining AI automation with expert validation. It analyzes and converts forms, PL/SQL logic, and triggers into responsive web interfaces and secure back-end services using modern frameworks. This accelerates migration, preserves business logic, improves UX, and reduces reliance on legacy expertise.
	iBEAM Xamarin to .NET Multi-Platform App UI (MAUI): iBEAM Xamarin to .NET MAUI automates migration of legacy Xamarin.Forms apps to .NET MAUI, ensuring code accuracy with expert validation. It simplifies cross-platform maintenance, supports Azure environments, and reduces risk, making it ideal for enterprises modernizing apps, improving performance, and integrating cloud-native solutions.
	Intelligent Food Waste Management Solution: The Intelligent Food Waste Management Platform streamlines food waste reduction with modules for donor and beneficiary management, inventory tracking, logistics, and AI chat support. It offers real-time analytics, bilingual access, and secure cloud architecture, enhancing operational efficiency, transparency, and social impact for governments, NGOs, and food industry stakeholders.
	IntelyDoc: IntelyDoc is an AI-powered document processing platform that automates extraction, understanding, and management of PDFs, handwritten notes, and complex reports. It boosts accuracy, speeds review cycles, and reduces manual work for businesses in finance, healthcare, legal, and government, enabling scalable, efficient, and error-free document workflows.
	LiveCloud FinOps: LiveCloud FinOps is a cloud financial management platform offering unified visibility, cost governance, AI-driven optimization, and anomaly detection across AWS, Azure, and Oracle Cloud. It provides dashboards, budget tracking, and forecasting, helping organizations optimize cloud spending and improve financial control in multi-cloud environments.
	Mural: Mural is an enterprise visual workspace that enhances teamwork and productivity through intuitive collaboration tools. It integrates with Microsoft Teams, Outlook, and Azure DevOps. Features include voting, private mode, and Microsoft 365 Copilot integration for faster, smarter team collaboration.
	NXT EDGE: NXT EDGE is a scalable middleware solution enabling seamless, real-time bi-directional data exchange between SAP and external systems. It supports advanced document transformations, intelligent routing, and multi-format invoice processing. With robust error handling, centralized monitoring, and Azure-based security, it ensures efficient, flexible, and cost-effective SAP integration and automation.
	NXT eNET: NXT eNET is an SAP-integrated e-invoicing compliance gateway that simplifies global tax regulations. It supports multi-country formats, automated validation, real-time government submissions, and secure archiving. Powered by Azure, it offers scalable, transparent billing, ensuring efficient, compliant electronic invoice exchange and tracking across regions.
	NXT Mailbox Manager: NXT Mailbox Manager automates SAP accounts payable email workflows by intelligently capturing, classifying, and routing invoices. It integrates with Microsoft 365, uses AI for document recognition, and centralizes mailbox management. This accelerates processing, reduces overhead, and enhances visibility, all on secure Azure infrastructure with flexible, scalable deployment.
	KYC360 Onboarding: KYC360 Onboarding streamlines complex client onboarding with a no-code, scalable platform that reduces onboarding time by up to 80 percent. It offers dynamic forms, automated risk-based processing, real-time analytics, and seamless integration. The solution enhances customer experience, ensures compliance, and supports millions of cases with personalized, efficient digital workflows.
	OrbViz: OrbViz creates interactive, data-driven reports that combine narrative and visuals for better stakeholder understanding. It offers reusable templates, automated batch reporting, engagement analytics, and customer intelligence, and it integrates natively with Power BI, helping organizations uncover insights on trends, risks, and opportunities efficiently.
	PPT-Design Templates: PPT-Design is a powerful Microsoft PowerPoint add-in offering templates, icons, QR code and word cloud generators, stock photos, web screenshots, and AI text extraction. It supports nine languages, dark mode, social media content insertion, and team branding, enabling users to create stunning presentations in PowerPoint.
	Precisely Data Integrity Suite: The Precisely Data Integrity Suite offers modular, AI-powered tools to ensure accurate, consistent data. It enhances data quality, governance, and integration, enabling seamless connection of sources. With contextual enrichment and automated insights, it supports reliable data management and informed decision-making for improved business efficiency.
	Degreed Recurring Services: Degreed Recurring Services offers expert guidance to build and scale skills-based strategies, design learning journeys, and create AI-powered experiences. The services provide hands-on support, technical implementation, and measurement frameworks to align skill development with business outcomes, accelerating value and maximizing impact through Microsoft Marketplace integration.
	KYC360 Screening: KYC360 Screening offers a 3D risk-based approach to minimize false positives and enhance true match accuracy. It handles millions of names, integrates top global data sets, and provides real-time reporting, live adverse media monitoring, and a self-service data quality tool.
	Simply Send - Email API: Simply Send is a developer-focused email delivery API that supports transactional and marketing emails with seamless Azure integration. It offers secure key management, compliance, real-time analytics, webhook events, and auto-scaling infrastructure. Billing consolidates into Azure invoices, making it a reliable, scalable alternative to SendGrid and others.
	StarAI: StarAI is a secure, invitation-only platform combining internal messaging, AI-powered contact capture, private knowledge management, and group commerce. It eliminates fragmentation and third-party access risks, preserving organizational knowledge and enabling seamless collaboration, communication, and transactions—all within a single, sealed environment for enhanced security and efficiency.
	TeyaHealth: TeyaHealth is a healthcare solution that enhances patient care and operational efficiency through real-time monitoring, predictive analytics, and secure data management. It streamlines workflows, improves care coordination, and reduces administrative burdens, empowering medical professionals to deliver better outcomes and increased patient satisfaction in modern healthcare settings.
	Thales's Imperva DataSec Stack: This comprehensive cybersecurity solution features data discovery, classification, vulnerability assessment, user rights management, continuous activity monitoring, advanced data enrichment, behavior analytics, and automated compliance workflows. It safeguards sensitive information like PII, detects risks, and streamlines security operations for enhanced data protection.
	The Sales Coach Academy: This IT sales training offers AI-powered coaching within Microsoft Teams, providing instant, language-specific tips and updated content. It features webcam role-play with professional actors, self-analysis tools, gamified feedback, and leaderboards. Sales leaders track progress via analytics, ensuring continuous skill improvement and engagement through certifications and team competition.
	ToCumulus Zscaler as a Service: Zscaler as a Service offers secure, isolated zero trust security via a dedicated Azure instance. It provides enterprise-grade scalability and full automation, removing infrastructure management burdens. This enables your teams to focus on strategic goals while ensuring reliable business continuity and robust protection tailored to your business.
	ZADIG Smart Insights - Network Authentication Wi-Fi Diagnostics: ZADIG Smart Insights offers advanced Wi-Fi 802.1x diagnostics for enterprise networks, helping IT admins detect and analyze authentication failures. It provides detailed reports, identifies misconfigurations, and suggests fixes by analyzing telemetry and logs within a secure Microsoft Sentinel workspace, ensuring efficient troubleshooting of network sign-in issues.
Easily deploy virtual machine images
	Database for PostgreSQL 16 on Enterprise Linux 9 Database for PostgreSQL 16 on Rocky Linux 8 Database for PostgreSQL 16 on Rocky Linux 9 Database for PostgreSQL 16 on SUSE Linux Enterprise Server 15.7
	Rocky Linux from CIQ - Pro
	AG-MDM: The Next-Generation Agent Governed Master Data Platform
	Deploy Apps in Minutes - Secure Docker Server on Ubuntu 24.04 Ultra-Fast, Secure, and Optimized for Windows Server 2022 Workloads
	ChromaDB Supported and Secured by the Hossted Platform
	Docker Compose OAuthLib
	Backdrop CMS Cockpit CMS Falcon with NumPy and Pandas Redmine
Go further with workshops, proofs of concept, and implementations
	KT Secure Public Cloud: KT Enterprises' KT Secure Public Cloud offers high-performance cloud services with strict domestic data residency and security compliance. Based on Azure, it enforces local data storage, encrypts data at all stages, ensures customer data ownership, and provides isolated environments.
	Web App Migration Support Services: Persol Cross Technology consultants will investigate your existing apps and system environments to determine whether they are best suited for migration, renovation, or new builds. They will then use Azure services to meet your PaaS web system requirements.
	Azure Managed Services: Implementation: Opticca offers expert Azure cloud managed services with three tiers—Foundation, Standard, and Premium—providing proactive monitoring, security, cost optimization, and DevOps support. With more than 15 years of experience, fixed pricing, bilingual delivery, and Microsoft specialization, they ensure secure, efficient, and continuously improving Azure environments tailored to your business needs.
	B2F's Dynamics 365 Business Central Implementation: B2F offers tailored Microsoft Dynamics 365 Business Central implementations, covering analysis, design, data migration, custom development, localization, training, and support. With expertise in various industries, they ensure smooth transitions, develop industry-specific add-ons, and focus on enhancing operational efficiency, business value, and long-term system sustainability.
	Building Knowledge Platforms with Microsoft 365 Copilot: 2-Day Workshop: Dataciders' workshop helps organizations design a scalable knowledge platform using Microsoft 365 Copilot. It includes assessing current knowledge, defining use cases, creating architecture, addressing governance, and providing an implementation roadmap.
	Microsoft 365 Copilot Extensibility Discovery: 2-Week Workshop: Nexus Technologies' Microsoft 365 Copilot Extensibility Discovery Workshop helps organizations explore and design Microsoft 365 Copilot extensions. It includes workflow assessment, use case brainstorming, technical feasibility analysis, and implementation roadmaps. Nexus Technologies customizes, monitors, and optimizes integration to boost productivity and align business-IT goals.
	Microsoft 365 Copilot Strategy and Use-Case Value: 2-Day Workshop: Nexus Technologies' Microsoft 365 Copilot Strategy and Use-Case Value Workshop helps organizations identify and prioritize Microsoft 365 Copilot use cases aligned with business goals. Nexus Technologies customizes and implements Copilot to boost productivity, efficiency, and innovation, providing tailored scenarios, adoption insights, and a roadmap for successful integration and measurable value realization.
	Data Security Envisioning Workshop: Heartland Business Systems’ Data Security Envisioning Workshop uses Microsoft Purview to scan Microsoft 365 for sensitive data risks, insider threats, and compliance gaps. It provides a security check report, prioritized recommendations, and a compliance strategy, helping organizations understand and mitigate data exposure and insider risks effectively.
	element61 GenAI-enabled Analytics Migration: Ralph by element61 is an AI-driven data engineering tool that automates complex data platform migrations to modern architectures like Microsoft Fabric. It accelerates delivery by up to 70 percent and ensures quality with automated analysis, SQL generation, validation, and human review.
	HubSpot and Azure Logic Apps Integration: Automate HubSpot marketing workflows with Azure Logic Apps for real-time data sync, error handling, and scalable operations. IT Partner will help your organization benefit from secure, compliant integration supporting more than 700 systems, intelligent API management, and hybrid connectivity. This solution is ideal for marketing teams and enterprises seeking efficient, code-free automation with monitoring and expert support.
	HubSpot and Microsoft Power Automate Integration: IT Partner will help your organization automate marketing workflows by integrating HubSpot with Power Automate. Sync customer data, streamline campaign approvals, and connect more than 200 apps with real-time triggers, security, and GDPR compliance. Ideal for marketing teams seeking efficient, scalable operations and improved data integrity across platforms, the solution requires HubSpot Enterprise and Power Automate Premium.
	HubSpot and Microsoft Power BI Integration: IT Partner will automate HubSpot data syncing with Power BI for real-time marketing insights, enabling you to visualize KPIs, track ROI, and forecast using AI models. Features include secure OAuth 2.0 authentication, security, and pre-built dashboards, making the solution ideal for marketing teams seeking live campaign visibility and optimized spend with scalable, unified reporting.
	HubSpot and Microsoft Teams Integration: IT Partner's HubSpot and Microsoft Teams integration streamlines marketing by enabling real-time campaign alerts, one-click approvals, and embedded HubSpot data within Teams. It reduces app-switching, accelerates decision-making, and improves collaboration with secure workflows, customizable alerts, and centralized campaign visibility.
	International Dynamics 365 Implementation: Aricoma specializes in international Microsoft Dynamics 365 Finance and Supply Chain Management implementations, ensuring compliance with Czech and Slovak legislation. They offer local deployment, data migration, user training, and ongoing support, collaborating with global partners to deliver smooth, efficient rollouts tailored to local branches of international companies.
	Microsoft Intune - Modern Endpoint Proof of Concept: ProArch's proof of concept helps organizations evaluate Microsoft Intune for modern endpoint management. It includes policy design, device enrollment, application deployment, and security testing on Windows and macOS devices. The pilot validates secure onboarding, compliance, and provisioning, enabling a confident, risk-minimized transition to full Microsoft Intune deployment.
	Microsoft Fabric Data and AI Platform for Healthcare/Life Sciences: Credenca helps life sciences and pharma firms build AI-ready data platforms using Microsoft Fabric, unifying data from multiple systems into a governed lakehouse. It enables automated reporting, predictive analytics, Power BI dashboards, and compliance with Microsoft Purview, improving decision-making, demand forecasting, and field force visibility while ensuring scalability and security.
	Microsoft Fabric Data Architecture as a Service: Procure SQL offers expert Power BI consulting to accelerate deployment, fill skill gaps, and enhance analytics. Experienced architects will help you with architecture evaluation, requirements, strategy development, best practices, data modeling, performance tuning, and security—boosting adoption without long-term hiring or expanding permanent staff.
	Microsoft Fabric Implementation Support Service: Available in Japanese, System Support Inc's service facilitates comprehensive Microsoft Fabric adoption, integrating data platforms for advanced analytics and AI-driven decision-making. It offers hands-on training, proof of concept, secure architecture design, and ongoing operation support, enhancing data governance and enabling natural language analysis via Azure OpenAI.
	Power Automate for Business: 3-Day Proof of Concept: Clouds On Mars' proof of concept builds a custom Power Automate workflow for your chosen business process. It includes process analysis, agile development, integration with data sources like SharePoint, testing, and handover. The solution demonstrates the value of Power Automate, enabling digital transformation and process automation across your organization.
	Threat Protection Envisioning Workshop: The Threat Protection Envisioning Workshop by HBS helps security teams identify and prioritize active cybersecurity threats and vulnerabilities in Microsoft 365 and on-premises environments. Experts map risks to Microsoft security solutions, creating a tailored action plan. Thie engagement is ideal for security leaders seeking to strengthen defenses and close security gaps.

Contact our partners

Managed Extended Detection and Response and SOC by Netox

Acumens e-Tax Enterprise R2

Acumens Licensing and Subscription Management

Advaiya Progress Billing for Dynamics 365 Business Central

Advaiya Resource Scheduling App

Agentic AI Purchase Order Automation

AI Company Financial Briefing Agent for Commercial Banking

AI DevOps on Azure with GitHub

AI Readiness Assessment for Azure and Microsoft Copilot

AI Summary 365

Planogram Agent - AI-powered Field Assistant for CPG Retail Shelf Compliance

Alert Me - SharePoint Notifications

Alibaba Cloud Networking

Asset Maintenance Management for Dynamics 365 Business Central

AssistGO CRM Plugin

Aston AI Cash Collection

AWS Elastic Load Balancing for Microsoft Sentinel

BankSync 365

BE-terna Manufacturing

BI Pixie Dashboard

BitRecover Data Destruction Software

BitRecover NST to PST Converter Tool

Bitwarden Self-Hosted Server

BiznextApp Document Upload AI Agent

Blue Dragonfly FloQast Integration

Blue Dragonfly Sales Holds

Braintree Scheduler

Business PlanPilot - Business Idea SWOT Generator

Business Solutions Discovery

CentrixIQ for Manufacturers

Cigati MySQL Database Repair Tool

Cigati SQLite Database Recovery Tool

Cigati Zimbra to Outlook Migration Tool

Civica MDM - Master Data Management

ClaimSense

CodeFriends

Connector for Microsoft Planner to Jira

conRIO

Microsoft 365 Copilot Readiness Assessment

Core Drag and Drop

CorusHR

Cronofy Scheduling Agent

Deferred Payment by Infotek

DICOM Viewer for SharePoint

Dispatch & Logistics Samadhan

DRS Softech OLM to DOCX Converter

DRS Softech OLM to Gmail Converter

DRS Softech OLM to PNG Converter

duveka ClousAI

Dynamics 365 AI Agent Readiness and Activation Sprint

Dynamics 365 Warehouse Management System Performance and Archiving Assessment

MRP Express for Dynamics 365

eKYC Onboarding and Loan Processing Copilot Agent

External File Storage - Datto Workplace Connector

File Eraser

Filtered Table by Elwaha.net

Fleqtor Health - Patient Queue Management for Hospitals

Fluid Eye Fluid Manager

GiroCode

Harvest - Advanced Lot Traceability for Warehouse Insight

Hasib Conex

HPT's Microsoft 365 Copilot Readiness and Adoption Service

Power BI Inventory App for Dynamics 365 Business Central

Jira

Job Work - Subcontracting and Trading Management for Dynamics 365 Business Central

Kravia Collection

KSeF Additional Info

Lawxy Case Analyzer AI Agent

Lawxy Due Diligence Agent

Lawxy Intake Agent

Lawxy Legal Research AI Agent

Lawxy Legal Translation Agent

Lawxy Meta Data Extraction Agent

Legisway for Outlook

MacSonik PDF Encryption Tool

Material Shortage Calculation

MXValidator - Email Validation SaaS

OneAdvanced Legal Add-in for Outlook

PAXcel

Posted Invoice Approval

RecoveryTools for iCloud to PDF Converter

RecoveryTools PST to EMLX Converter

Revalgo Sales Order Automation

RIB Cosinus Construction Comfort Premium

RUX Rentals Dashboard

Safety LMS

Sendcloud Shipping Connector

Settle GL Entries

Sovereign Governance

Power Automate: 2-Hour Implementation Assessment

SSA PeopleOps Suite

Sunrise Companion

Synesis - AI FinOps for Azure

SysInfo EDB to PST Converter Tool

SysInfo Maildir to PST Converter

SysInfo Zoho Mail Backup Tool

SysTools MBOX Duplicates Remover

SysTools Parquet Merge

This content was generated by Microsoft Azure OpenAI, then revised by human editors.

Improve Marketplace outcomes with AI-powered listing recommendations in App Advisor

Dan-Langille — Tue, 28 Apr 2026 22:55:19 GMT

Over the past two decades working in the Microsoft partner ecosystem, I’ve seen how much effort it can take for small software development teams to navigate the path from building solutions to publishing to selling successfully enough to build a business around the products we work hard to build.

What stands out to me now, however, is how much that experience has become more streamlined and actionable. App Advisor is one of the clearest demonstrations of Microsoft’s commitment to helping software development companies succeed, and in many ways, it represents the pinnacle of that evolution.

Selling through Microsoft Marketplace has always been a high-impact growth channel for our team at Breakthru. But like many software companies, we’ve had to balance speed with precision—especially when it comes to optimizing listings that directly drive demand.

With App Advisor—and now AI-powered Marketplace listing recommendations—we’re able to continuously improve how we show up in Marketplace without introducing risk or guesswork. We have more control and clarity than we have ever had before.

Want to try the AI-recommendations for your listing? Go to aka.ms/ImproveMyListing

Why App Advisor became a core part of how we operate

I’ve been working in the Microsoft partner ecosystem for over 20 years. That’s a lot of technological changes. Over that time, however, Microsoft has never wavered from enabling partners with resources to build and grow. In fact, Microsoft has historically provided so much information, it was sometimes challenging to find the most relevant next step.

After all, knowing what to do is one thing, knowing how to do it correctly and when is another. App Advisor closes that gap. It doesn’t just point you in the right direction. It meets you where you are and helps you get to where you need to go next.

What makes App Advisor especially valuable for us is how practical and actionable it is. Instead of relying on external consultants or piecing together information across different resources, we can go directly to App Advisor and get clear, structured guidance.

That saves time, reduces ambiguity, and helps us execute with more confidence across everything from publishing to sales motions.

As it has evolved, it’s become more than a resource. It’s a system that helps you:

Understand how to build, publish, and sell effectively
Navigate Marketplace requirements without relying on external consultants
Improve your execution across the entire lifecycle

What’s new: AI-powered Marketplace listing recommendations

The latest addition—AI-powered Marketplace listing recommendations—builds directly on that foundation.

For us, Marketplace listings are a primary driver of inbound demand. In the Teams App Store, especially, visibility translates directly into installs, usage signals, and ultimately sales conversations.

That raises the stakes.

Any change to a listing—new messaging, updated screenshots, revised positioning—can impact uptick and performance. Historically, we were averse to making any changes. Now, that dynamic has changed.

With AI-powered recommendations, we know what performs well, and as a result we can easily:

Validate listing quality before and after updates
Identify gaps in areas like keywords, messaging, and structure
Make improvements without risking performance regression
Iterate more frequently with confidence

The ability to revalidate our listings and maintain a high-quality score as we evolve our messaging is incredibly valuable.

What this means in practice

This isn’t just about optimization. It’s about a different kind of operational fluency.

Instead of treating our Marketplace listings as a static asset, we treat it as dynamic, continuously improving service.

For example, this new App Advisor feature helped us identify that we needed to improve our key words. While our listings were strong in structure and clarity, optimizing key words would improve discoverability.

That’s the kind of targeted feedback that drives meaningful outcomes. It not only shortens the feedback loop significantly but also removes uncertainty from the process.

How this fits into the bigger picture

What stands out most is how this feature fits into the broader App Advisor experience.

It’s not a standalone tool. It’s part of a system designed to help software companies:

Build with Marketplace readiness in mind
Publish with clarity and compliance
Sell with stronger positioning and visibility

That continuity matters.

It ensures that what you build, how you position it, and how you present it in Marketplace are all aligned from the start.

If you’re already selling through Marketplace—or planning to—this is, in my twenty-plus-year experience with Microsoft, one of the fastest ways to improve outcomes without adding overhead.

Review your current listings and run them through the AI-powered recommendations. You’ll quickly see where to focus.

Ready to get started? aka.ms/AppAdvisor

Explore how to optimize your listing and improve Marketplace performance today: aka.ms/ImproveMyListing

Preeminent Technologies and Codemod offer transactable partner solutions in Microsoft Marketplace

Nikhil_Viswanathan — Tue, 28 Apr 2026 13:00:00 GMT

Microsoft partners like Preeminent Technologies and Codemod deliver transact-capable offers, which allow you to purchase directly from Microsoft Marketplace. Learn about these offers below:

SpaceBeacon: SB Flights & Hotels from Preeminent Technologies (doing business as SpaceBeacon) is a service of the SpaceBeacon AI-powered travel platform. SB Flights & Hotels lets you book your trip via voice commands. Tell Beacon Voice AI where you're going, when, and how you like to travel. Beacon then will search flights, find the perfect hotel, compare options, and book everything.

Codemod: Automate enterprise-scale code migration and modernization with Codemod, a tool comprised of task-specific micro-agents that orchestrate changes end to end. These bots use compiler-aware code graphs to de-risk and automate complex projects like security fixes, end-of-life upgrades, system updates, and software development kit migrations.

From co-sell ready to co-sell active: How to drive Microsoft seller engagement

trunalbhanse — Mon, 27 Apr 2026 23:07:51 GMT

About the author: Trunal Bhanse, CEO, Clazar, brings significant experience as a leader and an innovator across Airbnb and Confluent. At the latter, Trunal led a pioneering team of engineers scaling cloud marketplaces and billing infrastructures. Having witnessed the complexity first-hand, he launched Clazar to democratize cloud marketplace access for businesses.
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Selling with Microsoft is one of the most effective ways for software development companies to access enterprise pipeline at scale. Co-sell deals close around 30% larger and up to 2x faster than deals without Microsoft involvement. With over 500,000 partners in the ecosystem, the opportunity is real, and so is the competition for it.

The challenge is that most software companies focus on achieving co-sell status and then expect pipeline to follow.

What we see at Clazar, working with hundreds of software companies across Microsoft Marketplace, is that readiness is the starting point. The software companies who actually generate co-sell pipeline invest in the habits and operational processes that turn status into active seller collaboration.

This post shares five things those software companies do differently.

Why reaching Azure IP co-sell eligibility unlocks enterprise opportunity

The co-sell status ladder runs from in market to co-sell ready to Azure IP co-sell eligible. Most conversations focus on the first two steps, but the jump to IP co-sell eligible is where things meaningfully change.

At that tier, your solution appears inside Microsoft's internal sales systems, customers can apply purchases toward their Azure consumption commitments, and Microsoft sellers can bring your solution into enterprise accounts with full program backing. These three factors together accelerate deal velocity in a way that earlier tiers simply cannot.

Getting there requires either $100K USD in trailing 12-month Marketplace billed sales or Azure Consumed Revenue (ACR) or passing Microsoft technical validation. The most direct route is getting your first transactable deals through Marketplace so the Azure consumed revenue (ACR) clock starts running.

What high-performing software companies do differently

The software companies that build a consistent co-sell pipeline tend to share five common behaviors:

1. They build collateral for sellers, not just buyers

A marketing one-pager explains what your product does. That is not the same as content that helps a Microsoft field rep decide whether to bring you into a deal.

A seller asking about your solution wants to know one thing: why should I mention this at my next customer meeting?

Good seller-facing collateral answers that question directly. It covers which Azure services your solution pairs with natively, which customer segments you win in, what problem you solve in plain language, whether your offer is Microsoft Azure Consumption Commitment (MACC)-eligible, and who at your company to contact.

When a seller can see that picture quickly, they are more likely to act on it.

2. They treat their partner development manager (PDM) as a working partner

Your partner development manager is your primary connection to the Microsoft field. Most software companies reach out to their partner development manager when they need something. The ones who get more out of the relationship make it a two-way channel.

That means sharing pipeline updates, not just closes. It means asking for introductions to specific regional or vertical sellers rather than asking for general visibility. It means looping in the partner development manager when Microsoft is already present in an account and you are going into a pre-sales conversation.

When you close a co-sell deal, send a short win summary: customer segment, use case, Azure services involved, deal size, and time to close. That gives your partner development manager a story they can use internally and reinforces that supporting your deals pays off.

3. They respond to referrals quickly and consistently

Microsoft tracks how fast software companies respond to referrals, and response time is one of the clearest signals of operational readiness. The software companies who build strong co-sell relationships show up quickly on every referral, not just the ones that look promising.

A reasonable target is same business day acknowledgment and full qualification within 24 hours. Getting there requires Partner Center referral intake to feed directly into your CRM with automatic routing to the right account executive. The simpler that process is, the more consistently it happens.

It also helps to close the loop. When you accept a referral, let the referring seller know. When you close it, tell them that too. Sellers who feel kept in the loop refer more.

4. They submit outbound referrals, not just receive them

Co-sell works best when it goes in both directions. Many software companies receive referrals but do not submit them for deals they are already working.

Submitting outbound referrals in Partner Center increases your visibility to Microsoft sellers in relevant accounts, signals that you are an active partner, and sometimes surfaces information about whether target accounts have Azure consumption commitments.

Consistent referral activity creates a track record that makes future collaboration easier to initiate.

5. They actively use Microsoft Azure Consumption Commitments eligibility in sales conversations

Enterprise customers with Microsoft Azure Consumption Commitments (MACC) need to deploy their committed spend, and actively look for solutions that count toward it. If your offer is IP co-sell eligible, it qualifies, and that can be a meaningful lever late in a customer's contract period.

Most software companies who are MACC-eligible mention it somewhere in their documentation but do not actually use it in sales conversations. Training your sales team to bring it up, featuring it in your Marketplace listing description, and including it in your seller one-pager are all practical ways to put it to work.

The operational side

Everything above depends on having the right processes in place. Seller-facing collateral needs to be created and kept current.partner development manager relationships need a regular cadence. Referral intake needs to be connected to the CRM. MACC positioning needs to reach the sales team.

Co-sell programs often underperform, not because the strategy is wrong, but because these operational pieces are fragmented. Partner Center sits separate from the CRM. Referrals expire without anyone noticing. The partner development manager relationship runs through one person's inbox.

The software companies who generate repeatable co-sell pipeline have sorted out these operational basics with the right process and co-sell automation workflows.

Three things worth doing immediately

Read your co-sell one-pager from a seller's perspective. Does it answer the question: why should I bring this up in my next meeting? If not, that is where to start.
Pull your referral response times from the last 30 days. If your average is over 24 hours, fixing that process is the most immediate lever you have.
Schedule a working session with your partner development manager. Come with three recent wins, a specific region or vertical where you want introductions, and your 90-day pipeline target. Data makes those conversations more productive.

If you would like to learn more and get your questions answered, join the live webinar on May 7^th Turning co-sell readiness into real Microsoft Marketplace revenue - Microsoft Marketplace Community. If you are unable to attend the webinar, you can use the same link to view the recording on demand after the session.

_________________________________________________________________________________________________________________________________________________________________

About Clazar

Clazar is a cloud marketplace automation platform that helps software companies manage their cloud marketplace operations, including co-sell referral routing, private offer workflows, MACC tracking, and revenue reporting. We work with hundreds of software companies turning marketplace readiness into repeatable revenue.

If you are working through any of the challenges above and want to compare notes, reach out to our experts.

Design predictable usage-based billing for AI apps and agents selling in Microsoft Marketplace

Julio_Colon — Mon, 27 Apr 2026 21:07:09 GMT

Design predictable usage‑based billing for AI apps and agents selling on Microsoft Marketplace

Compared to traditional software, pricing and billing feel harder because of the range of AI functionality. They reason, they infer, call tools, process data, all, to complete tasks on the customer’s behalf.

If you’re building an AI app or agent to sell in Microsoft Marketplace, usage‑based billing needs to be designed with care, instrumented with intention, and explained in a way customers can trust. This post, along with App Advisor’s curated step-by-step guidance through building, publishing and selling apps for Marketplace, walks through how to do exactly that—without over‑engineering or surprising your customers later.

This post is part of a series on building and publishing well-architected AI apps and agents in Microsoft Marketplace. The series focuses on AI apps and agents that are architected, hosted, and operated on Azure, with guidance aligned to building and selling solutions through Microsoft Marketplace.

Why billing for AI systems is different

Traditional software pricing is usually tied to static entitlements, such as licenses, seats, fixed feature sets and/or a predictable runtime footprint. AI apps and agents don’t work that way. Their cost and value are driven by runtime behavior, such as:

How often a model is invoked
How many tokens are processed per request
How deep reasoning chains go
How frequently tools or APIs are called
How much data is accessed, transformed, or embedded

AI behaviors are subject to change based on the interpretation of prompts and subsequent outputs processed by agents and models. That variability is why pricing AI like traditional software often creates friction—margins erode and customers may lose trust. Pricing decisions should start with business value in mind, not the meter level.

Start with plan design before you define meters

Plans explain pricing. Meters enforce pricing. Your Marketplace plan is where customers learn what they are buying and how it works. Before you design a single metered dimension, your plan should clearly answer:

What AI behaviors are allowed
What usage is included
What usage becomes billable
What limits apply
How customers upgrade as they grow

An effective plan design typically considers several key factors, such as the distinction between public and private plans, the allocation of included usage versus charges for overages, the balance of base fees against variable consumption, and the provision of clear upgrade paths across different tiers. For instance, if you’re creating an AI support agent, a well-structured plan might offer up to 1,000 resolved conversations each month for a set monthly fee, with additional charges for any conversations beyond that limit and a higher tier that grants access to increased usage allowances. When customers can easily understand what is included, what triggers extra costs, and how they can upgrade as their needs grow, metering feels straightforward and fair. Conversely, when plan details are ambiguous, even accurately measured charges can seem arbitrary, leading to uncomfortable billing discussions.

Choose a billing model that matches how your AI behaves

When structuring your AI solution’s pricing, begin by evaluating the expected usage patterns and the business value your AI delivers. Actively consider the nature of your agent’s workloads, the variability of customer interactions, and the predictability of operating costs.

Flat Fee: Weigh the benefits of flat rate or subscription pricing. Opt for fixed monthly or annual fees when your AI solution operates within defined limits and usage remains consistent. This approach simplifies billing for customers and provides them with clear expectations. Subscription pricing works best for AI agents whose engagement is steady and whose costs don’t fluctuate dramatically.

Usage-based (metered): If your AI’s usage varies widely or scales rapidly, usage-based (metered) pricing is often preferable. This model aligns charges with actual consumption, ensuring customers pay only for what they use. To implement it, leverage Marketplace metering APIs to track and bill usage accurately. Consider usage-based pricing when customer demand is unpredictable or your AI’s operational costs increase with higher workloads.

Hybrid: For AI solutions that deliver ongoing baseline value but occasionally handle intensive tasks, hybrid models combine the strengths of both approaches. Offer a base subscription for predictable service, then layer in usage charges for overages. This structure is common for agents serving regular needs with intermittent spikes, enabling you to manage cost recovery while giving customers cost certainty.

Metering looks different depending on your offer type

As you move forward with your plan design and billing model, it’s important to recognize that metering varies significantly based on how your solution is delivered.

SaaS offers: Usage tracking is accomplished through Marketplace Metering APIs, allowing you to capture AI-driven activities such as agent task executions, workflow runs, document analysis, or token processing. Your metering should align closely with the customer’s subscription lifecycle, plan tiers, and the included usage, ensuring transparency and consistency as customers progress through different service levels.

Container-based offers: You might meter resources like nodes, cores, pods, or clusters—or even application-specific AI dimensions. Accurate attribution across tenants and deployments is crucial, so customers are billed reliably according to their actual consumption.

Virtual machine offers: Metering is generally linked to VM runtime or license usage. Although the granularity is often lower than SaaS solutions, billing remains contractually enforced, and publishers must ensure that measurements are dependable and align with customer agreements.

Azure Managed Applications: Metering should reflect solution management exclusively, while the underlying infrastructure costs are handled separately through Azure’s billing system.

For more about offer types, visit Marketplace Offer Types for AI Apps and agents: SaaS vs Managed App vs Containers.

Design metered dimensions customers can actually explain

As you refine your billing model for Marketplace offers, it’s vital to consider how your metered dimensions will be perceived and understood by your customers. The most effective dimensions reflect clear, customer-visible value rather than abstract internal system mechanics. For AI-driven solutions, this often means tracking tangible outcomes such as agent tasks executed, successful workflows completed, data objects processed, or AI-assisted actions performed.

Choosing these straightforward metrics not only makes invoices easier for customers to interpret but also strengthens your position during billing reviews by tying charges directly to business outcomes. For example, “documents analyzed” is a much clearer and more defensible metric than “token batches processed,” and “resolved workflows” resonates more with customers than “model invocations.” Ultimately, a strong metered dimension is one that a customer can easily explain to their finance or procurement teams. If the charge isn’t readily understandable, it’s a signal to revisit and refine your measurement approach.

Track and plan metrics using the Microsoft Marketplace metering service APIs

Under‑reporting impacts revenue. Marketplace enforces billing based on what you report. Once you've determined how your solution will be delivered and understood how metering varies by offer type, the next step is to ensure your billing model is both transparent and robust. This is accomplished by tracking your plan and meter metrics through the Microsoft Marketplace Metering Service APIs —a process that not only supports accurate billing but also builds customer trust.

Instrumenting usage at runtime is essential: you must reliably capture and report consumption, making sure each event is precisely recorded and associated with the correct subscription and plan. Aggregating this usage and sending it to the marketplace—whether hourly or daily, covering the previous 24 hours—ensures billing remains consistent and defensible.

Add metering guardrails to avoid cost surprises

As you implement usage-based metering for your Marketplace offers, it’s essential to build guardrails that protect both your business and your customers from unexpected costs. Metering is a critical component of your service reliability, directly influencing customer trust and the overall transparency of your billing model.

Ensuring your metering remains both dependable and customer-focused is crucial for maintaining trust and transparency. As you instrument your solution, take care to attribute usage precisely across multiple tenants, so every charge is accurately mapped to the correct customer and subscription. Additionally, aggregating usage on a consistent schedule—such as hourly or daily—not only supports predictable reporting but also helps customers better understand their consumption patterns. These practices lay a solid foundation for metering that supports both your business objectives and your customers’ needs, creating a seamless experience that aligns with the overall goals of your Marketplace offering. Marketplace-ready offerings typically feature:

Usage caps that set clear maximums, limiting exposure to unforeseen charges.
Soft limits with proactive alerts as customers approach their thresholds.
Hard limits to enforce plan boundaries and prevent overages beyond agreed levels.
Transparent usage dashboards, giving customers real-time visibility into their consumption.

For example, when a customer reaches 80% of their allotted usage, they receive an alert and can decide whether to upgrade their plan, pause usage, or proceed into overage with full awareness—eliminating surprise invoices at month’s end.

What’s Next in the Journey

After establishing robust billing and metering, the next step is to enhance your AI solution’s performance, optimize API workloads, and improve production observability—laying the groundwork for scalable, efficient, and reliable operations. These capabilities help keep AI systems cost‑effective and reliable as usage grows.

Key Resources

See curated, step-by-step guidance to help you build, publish, or sell your app or agent (no matter where you start) in App Advisor

Quick-Start Development Toolkit can connect you with code templates for AI solution patterns

Microsoft AI Envisioning Day Events 

How to build and publish AI apps and agents for Microsoft Marketplace

Get over $126K USD in benefits and technical consultations to help you replicate and publish your app with ISV Success 

Unlocking hard data estates: How Cloudera on Microsoft Marketplace brings AI to regulated industries

AlexW1490 — Mon, 27 Apr 2026 13:00:00 GMT

In this guest blog post, Alex Wagman, Global Cloud Alliance Manager at Cloudera, considers the data challenges of regulated industries and how Cloudera enables governed hybrid data and AI.

There’s a frustrating paradox facing chief information officers and data leaders in regulated industries today: The data with the most business value — customer records, transactions, operational intelligence — is often the data that’s least accessible for AI initiatives.

The data isn’t old or poorly managed. It’s difficult to access because it’s important. Compliance mandates, data sovereignty requirements, and regulatory constraints mean that your highest-value data estates can’t simply migrate to the cloud. Meanwhile, your cloud-native AI and analytics tools can’t easily reach back into these on-premises environments to access that data.

The result? AI initiatives stall. Modernization programs slow. Teams wait months for data access while competitors move faster. And the promise of AI-driven transformation remains frustratingly out of reach despite having all of the right cloud services in place because those services can’t work with all of your data.

The regulated enterprise reality

If you lead data and AI strategy for financial institutions, healthcare providers, or government agencies, this challenge is deeply familiar. Your environment looks nothing like the clean, cloud-first architectures described in most AI vendor presentations.

Instead, you’re managing:

Large-scale data estates that can’t move to the cloud because of regulatory requirements, data gravity, or the sheer cost and risk of migration.
Fragmented governance in which security, access control, and compliance policies are inconsistent across on-premises and cloud environments.
Compliance mandates that require you to demonstrate complete data lineage, maintain data sovereignty, and enforce fine-grained access controls.
Legacy modernization pressure to move away from expensive, inflexible systems while maintaining business continuity.

You may also have invested in the powerful AI and analytics services of Microsoft Azure: Azure Machine Learning, Azure OpenAI Service, Power BI, Fabric. These tools work brilliantly for data that lives in Azure. But they don’t solve a fundamental challenge: Some of your most valuable data doesn’t live there, and it can’t.

Many organizations initially believe their Azure-native tools will be sufficient. But hybrid isn’t just “cloud plus on-premises.” True hybrid means delivering the same modern experience, with the same governance, across every environment where data lives. Hybrid complexity and inconsistent governance — not lack of cloud services — commonly block AI operationalization in regulated enterprises.

The Cloudera + Microsoft solution

Cloudera on Microsoft Marketplace addresses numerous challenges, combining its hybrid data and AI platform with the trusted cloud infrastructure of Microsoft Azure to make high-value data, wherever it is (on-premises, on public cloud, on private cloud), available for enterprise AI wherever companies want to run it. This is Cloudera’s unique “data anywhere, cloud anywhere, AI anywhere” approach. Customers gain a unified, governed data foundation that enables modern AI and analytics access to all data, wherever it lives.

A consistent platform everywhere: Cloudera’s cloud-anywhere platform runs consistently across datacenters, Azure, and multi-cloud environments. Teams use the same tools and workflows wherever data lives, so engineering, streaming analytics, machine learning, and lakehouse workloads work the same way on-premises and in Azure. This consistency eliminates the operational burden of managing different tools and different skill sets for different environments. Your teams work the same way, regardless of where data resides.

Unified governance across hybrid environments: Cloudera’s Shared Data Experience (SDX) provides a unified security and governance layer with consistent access control, security policies, metadata management, and compliance auditing across every private and public cloud environment, including Microsoft Azure. For regulated enterprises, you’re no longer managing fragmented security models or trying to reconcile different governance frameworks across on-premises and cloud. This unified governance extends to Cloudera Data Lineage, which provides end-to-end visibility across your entire hybrid data estate. When auditors ask where data comes from, how it’s been transformed, and who has accessed it, you have complete, automated answers rather than relying on manual documentation or institutional knowledge.

Secure, private AI on Azure: Cloudera also enables private AI — the ability to run AI and machine learning workloads on your most sensitive data without exposing it to public APIs or third-party models. This approach allows teams to take advantage of Azure’s infrastructure, including optimized compute resources, while maintaining full control of their data. Sensitive information remains within a governed environment, supporting data sovereignty requirements, regulatory compliance, and confident operational use of AI.

A practical path forward

For regulated enterprises, the path to enterprise AI doesn’t require wholesale cloud migration or accepting compromised governance. It requires a platform designed for the reality of regulated organizations: valuable data stored across hybrid environments, strict compliance requirements, and the need for modern AI capabilities everywhere.

Organizations managing petabyte-scale data in industries like financial services, healthcare, and government are already running mission-critical AI workloads on this architecture, accessing data estates that cloud-native tools alone couldn’t reach.

To learn more about how Cloudera is enabling governed hybrid data and AI for regulated industries, read The Evolution of AI report. To explore Cloudera’s solution on hybrid data modernization, go to the Microsoft Marketplace listing.

Integrate Marketplace commerce signals to enforce entitlements in AI apps

Julio_Colon — Thu, 23 Apr 2026 15:37:31 GMT

How fulfillment and entitlement models differ by Microsoft Marketplace offer type

AI apps and agents increasingly operate with runtime autonomy, dynamic capability exposure, and on‑demand access to tools and resources. That flexibility creates a new challenge for software companies: enforcing commercial entitlements (what a customer is allowed to access or use at runtime) correctly after a customer purchase through Microsoft Marketplace. Marketplace is the system of record for commercial truth, but enforcement always lives in your application, agent, or deployed resources.

This post explains how Marketplace fulfillment and entitlement models differ by offer type—and what that means when you’re designing AI apps and agents that must respond correctly to subscription state, plan changes, and cancellations.

You can always get a curated step-by-step guidance through building, publishing and selling apps for Marketplace through App Advisor.

Why AI apps and agents must integrate with Marketplace commerce signals

Microsoft Marketplace is the commercial system of record for:

Tracking purchase and subscription state
Managing plan selection and plan changes
Signaling cancellation and suspension

AI apps and agents, by contrast, operate in environments where decisions are made continuously at runtime. They expose capabilities dynamically, invoke tools conditionally, and often operate without a human in the loop.

That mismatch makes static enforcement insufficient, including:

UI‑only checks
Configuration‑time gating
Prompt‑based constraints

Marketplace communicates commercial truth, but it does not enforce value. That responsibility always belongs to the publisher’s application, agent, or deployed resources. Correct integration starts with understanding what Marketplace provides—and what your software must implement.

What Marketplace provides—and what publishers must implement

Before diving into APIs or offer types, it’s important to separate responsibilities clearly.

Marketplace provides authoritative commercial signals, including:

Subscription existence and current state
Plan and entitlement context
Licensing or usage boundaries associated with the offer

Marketplace does not:

Enforce your business logic
Control runtime behavior
Automatically limit feature or resource access

Publishers are responsible for translating Marketplace signals into:

Application behavior
Agent capabilities
Resource access boundaries

That enforcement must be deterministic, auditable, and aligned with what the customer actually purchased. How those signals surface—through APIs, deployment constructs, licensing context, or metering—depends entirely on the fulfillment and entitlement model of the offer.

How fulfillment and entitlement models differ by offer type

Microsoft Marketplace supports multiple offer and fulfillment models, including:

SaaS subscriptions
Azure Managed Applications
Container offers
Virtual machine offers
Other specialized Marketplace offer types

Each model determines:

How a customer receives value
Where commercial signals appear
Which integration mechanisms apply
Where entitlement enforcement must occur

Some offers rely on Marketplace APIs. Others rely on deployment‑time enforcement, resource scoping, or usage constraints. There is no single integration pattern that applies to every offer. Understanding this distinction is essential before designing entitlement enforcement for AI apps and agents.

Marketplace integration responsibilities by offer type

This section is the technical anchor of the post. Marketplace APIs are not universal; they apply differently depending on the offer model.

SaaS offers

SaaS offers integrate directly with Microsoft Marketplace through the SaaS Fulfillment APIs.

These APIs are used to:

Activate subscriptions
Track plan changes
Enforce suspension and cancellation

In this model, Marketplace communicates subscription lifecycle events, but it does not enforce access. The publisher must:

Map Marketplace subscriptions to internal tenants
Maintain a durable subscription record
Enforce entitlements at runtime

For AI apps and agents, that enforcement typically happens in orchestration logic or tool‑invocation boundaries—not in the UI or prompts. SaaS Fulfillment APIs are the primary mechanism for receiving commercial truth, but the application remains responsible for acting on it.

Container offers

Container offers deliver value as container images and associated artifacts, such as Helm charts. In this model, the publisher is shipping a deployable artifact—not an application endpoint or API managed by Marketplace.

Marketplace provides:

Entitlement to deploy the container image
Optional usage‑based billing and metering
Ability to deploy to an existing AKS cluster or to a publisher configure one

Enforcement occurs at:

Deployment time, by controlling access to images
Runtime usage, through configuration and limits
Metered dimensions, when usage‑based billing applies

For AI workloads packaged as containers, entitlement enforcement is typically embedded in the runtime configuration, resource limits, or metering logic—not in Marketplace APIs.

Virtual machine offers

Virtual machine offers are fulfilled through VM image deployment.

In this model:

Fulfillment is based on VM deployment
Licensing and usage are enforced through the VM lifecycle
Subscription state is less event‑driven but still contractually binding

While there is no SaaS‑style fulfillment callback, publishers must still ensure that deployed workloads align with the purchased offer. For AI solutions delivered via VM images, enforcement is tied to licensing, configuration, and operational controls inside the VM.

Azure Managed Applications

For Azure Managed Applications, fulfillment is enforced through the Azure Resource Manager (ARM) deployment lifecycle.

In this model:

A Marketplace purchase establishes deployment rights
Resources are deployed into a managed resource group
Operational boundaries are defined by ARM and Azure role assignments

Publishers enforce value through:

Deployment behavior
Resource configuration
Lifecycle management and updates

For AI solutions delivered as managed applications, entitlement enforcement is tied to what is deployed and how it is operated—not to an external subscription API. Marketplace establishes the contract, and Azure enforces access through infrastructure boundaries.

Other offer types

Other Marketplace offer types follow similar patterns, with varying degrees of API involvement and deployment‑time enforcement. The key principle holds: Marketplace establishes commercial rights, but enforcement is always implemented by the publisher, using the mechanisms appropriate to the offer model.

Designing entitlement enforcement into AI apps and agents

Entitlements must be enforced outside the model. Large language models should never be responsible for deciding what a customer is allowed to do.

Effective enforcement belongs in:

The interaction layer
The orchestration layer
Tool invocation boundaries

Avoid:

UI‑only enforcement
Prompt‑based entitlement logic
Soft limits without auditability

AI agents should request capabilities from deterministic services that already understand subscription state and plan entitlements. This ensures enforcement is consistent, testable, and resilient.

Handling plan changes, upgrades, and feature tiers

Plan changes are common in Microsoft Marketplace. AI capability must align continuously with:

The active subscription tier
Purchased dimensions or limits

Common examples include:

Agent autonomy limits
Tool or connector access
Rate limits
Data scope

Feature gating must be deterministic and testable. When a plan changes, your application or agent should respond predictably—without manual intervention or redeployment.

Failure, retry, and reconciliation patterns

Marketplace events are not guaranteed to be:

Ordered
Delivered once
Immediately available

AI apps must handle:

Duplicate events
Missed callbacks
Temporary Marketplace or network failures

Reconciliation processes protect customers, publishers, and Marketplace trust. Periodic verification of subscription state ensures that runtime enforcement remains aligned with commercial reality.

How Marketplace API integration affects readiness and review

Marketplace reviewers look for:

Clear enforcement of subscription state
Clean suspension and revocation paths

Strong integration leads to:

Faster certification
Fewer conditional approvals
Lower support burden after launch

Correct enforcement is not just a technical requirement—it’s a Marketplace readiness signal.

What’s next in the journey

Once entitlement enforcement is solid, the next layer of operational maturity includes:

Usage‑based billing and metering architecture
Performance, caching, and cost optimization
Observability and operational health for AI apps and agents

Key resources

See curated, step-by-step guidance to help you build, publish, or sell your app or agent (no matter where you start) in App Advisor

Quick-Start Development Toolkit can connect you with code templates for AI solution patterns

Microsoft AI Envisioning Day Events

How to build and publish AI apps and agents for Microsoft Marketplace

Get over $126K USD in benefits and technical consultations to help you replicate and publish your app with ISV Success

Microsoft Marketplace 101: Insights from high performing software development company sellers

Lee_Corbett — Wed, 22 Apr 2026 22:01:48 GMT

Microsoft Marketplace is probably the one topic that can give 'AI' a run for its money in terms of the number of mentions on LinkedIn, on cloud hyperscaler blogs, and in Partner Ecosystem presentations. But ask the average SaaS seller what a marketplace is, and you’ll probably get a different answer every time. That’s because marketplaces have become more than just a listing catalog, it’s now a pervasive commerce platform across the entire B2B Software purchase process. Jay McBain @ Omdia described Cloud Marketplaces as the "epicenter of partnerships, co-selling and co-innovation” and we see that play out at Microsoft.

The combination of Microsoft's co-sell model and Microsoft Marketplace has created the platform for modern procurement and modern partnerships. Today, Microsoft Marketplace:

Impacts all personas and incentives – from customers commercial incentives, to software company program sale incentives, channel partner benefits, and hyperscaler seller compensation models
Enhances B2B purchase processes for customers – Streamlining supplier onboarding, to internal FinOps, to governance and guardrails on purchasing
Brings agility to the sales process for software companies and channel partners – from pricing and quoting, budget allocation, billing & payments, right through to co-selling and go-to-market.

With this level of impact, it’s not surprising that Microsoft Marketplace represents a significant growth opportunity for software companies. Customers are increasingly transacting through Marketplace as they align Azure commitments with third-party software purchases and adopt a more “marketplace-first” procurement approach. Research shows 69% of partners report larger deals and 75% close deals faster (Omdia), reinforcing the role of Marketplace as a strategic channel for enterprise software procurement.

So, if you are a seller at a Microsoft SaaS software company and you want to take advantage of this opportunity, here are my five top learnings for high performance software sellers!

This article assumes you are running your SaaS software platform on Microsoft Azure and you are already transactable in Marketplace. If you aren't, there are plenty of great resources for software companies to help get you there, and I have summarized them here: New Software Company Partner Guide.

LEARNING 1 - Understand the customer benefits of Microsoft Marketplace and weave it into your customer proposal:

This is about understanding the bigger picture of Marketplace for Microsoft customers, as the benefits go way beyond an individual transaction. The average enterprise is managing over 600 applications (Source - Zylo) and >60% of these applications being purchased outside of IT. Marketplace is a great way for customers to scale and bring efficiency across their B2B Software estate, giving them:

Rapid access (find, try, buy) to software and AI innovation they need to run their business,
Governance and guardrails for each purchase – so you are never compromising control and compliance.
Streamlined procurement – onboarding suppliers in minutes using their Microsoft Agreement, invoicing and payment terms,
Improved cloud economics – aligning the cloud consumption with your software company spend can unlock additional P&L impacting benefits,

If you need to get up to speed on these aspects, check out: Microsoft Marketplace 101 - Top Customer Questions.

LEARNING 2 - Understand how Microsoft partners can help you scale and close customer opportunities:

Microsoft has always been a partner led organization. Today we have 500,000 partners globally ranging from a small start-up in a garage, to the largest Global Systems Integrator. These partners play different roles in our customers helping them transform and compete in the AI era through Microsoft Cloud & AI technologies.

The great news for you is that we have empowered Microsoft channel partners and sales partners to sell Marketplace solutions. So, think about how you can use channel partners to (i). expand your reach, (ii). drive new opportunities or (iii). accelerate deal closure. We have a range of different models to support your channel led motions in our customers, including:

Multi-party private offers (MPO) - Partner led sales agents for individual transactions.
Resale enabled offers (REO) - Nominated and enabled partner representative by geo.
Cloud solution provider (CSP) resellers - to scale into SMB/Corporate Accounts via a managed service provider

For each opportunity, understand who the incumbent Microsoft partner is, and how you can use these mechanisms to accelerate your deal. Or better still, if you are looking to explore a more proactive partnership, check out the 1000s of Partners that are building Marketplace practices to support customers and software companies. Examples include Bytes, Softcat, CDW, Trustmarque, Crayon, Computacenter etc.

LEARNING 3 - Accelerate your deal closure by including Microsoft programs and incentives into your customer proposal:

Here are the most commonly used accelerators used by software company sellers to close deals:

If you are marketplace transactable:

Marketplace Rewards - Azure Sponsorship Credits - A performance related benefit that provides up to $400k in Azure Sponsorship Credits. These credits can be used as a deal sweetener with your Marketplace transaction and passed on to the customer for consumption. Up to 40% of transactions involve these credits today.

If you are IP co-sell ready:

Azure Benefit Eligible - This enables customers to decrement their Microsoft Azure Consumption Commitments (MACC) with eligible Marketplace purchases. By aligning their cloud consumption with their software company spend, it can unlock additional P&L impacting benefits. More than 85% of MACC customers buy via Marketplace.
Microsoft IP co-sell incentive - This enables Microsoft sellers to get paid on the license value sold via Microsoft Marketplace. It also allows eligible Marketplace transactions to contribute to retiring Microsoft seller quota, helping accelerate deal alignment between partners and Microsoft field teams.

If you have a Certified Software Designation (CSD):

Marketplace Rewards - Azure Sponsorship Credits - If you come CSD Certified, Microsoft extends this benefit to $1M. These credits can be used as a deal sweetener with your Marketplace transaction and passed on to the customer for consumption.
Customer Migrate & Modernize Incentive - This partner incentive is used to drive migrations to Microsoft Azure. Cash incentives vary based project size, but Microsoft will pay up to $200k per opportunity for NEW customer migrations to Microsoft Azure.

High-performing software companies and sellers will combine these incentives into their proposals to help drive customer acquisition success.

LEARNING 4 - Know your Microsoft customer

Three things I urge every software company Seller to do as part of their Marketplace and co-sell conversations:

Know your customer 1 - Understand the customer's Marketplace Propensity Score to help prioritize your opportunities

Marketplace Propensity Scoring (combined with your co-Sell conversations) has proven a powerful tool for prioritizing pipeline and accelerating sales cycles.
Offered as a Marketplace Rewards benefit, propensity scoring provides you with a score that ranks your pipeline prospects by their likelihood (0 to 100) to transact through Marketplace. The score is based on a number of factors including their commercials, existing spend and transaction volumes, relationship metrics etc.
Access the benefit at: Microsoft Marketplace Rewards

Know your customer 2 - Enable more tailored offers for customers by updating your MEDDIC, MEDDPICC or BANT qualification questions with these Marketplace qualifiers:

Are they a Microsoft customer? This means they have agreed Microsoft terms and conditions to enable Marketplace transactions.
Are they a Managed Microsoft Customer? This opens up the possibility of selling with Microsoft.
What Microsoft segment/industry are they in? This opens up sales play alignment, industry go-to-market etc.
How do they buy Microsoft Azure today? This helps navigate potential commercials, incentives and programs, purchase routes and the role of channel partners.
Does the customer have a Microsoft Azure Consumption Commitment (MACC)? This could unlock the MACC benefit of Azure Benefit Eligible which allows customers to decrement marketplace purchases.
What is the status of their Microsoft Azure Consumption Commitment (MACC)? Are they ahead or behind of their consumption commitment? Is it coming up for renewal? This is all about getting access to planned Azure budgets at the right time.
Is there an incumbent Microsoft partner in the customer and what role do they play? It is forecasted that 60% of ALL marketplace sales will involve a channel partner, so get them involved early in the process.

Know Your Customer 3 - Use the Partner Center referral process to engage incentivized Microsoft sellers and gain customer intelligence:

Submitting your referrals via Partner Center will enable you to engage incentivized Microsoft sellers directly. For most Microsoft sellers, Microsoft Marketplace and co-sell deals are critical for how they will make their Azure quota. Help could include:

Navigate and increase your visibility with the customer's buyers
Gain insights on the customer's Azure commitments,
Understand the partner landscape in the customer
Potentially find ways to combine sales motions to drive bigger/better outcomes for all.

Build this discipline with every opportunity is a key characteristic of a high-performance software company seller.

LEARNING 5 - Celebrate and amplify Microsoft co-sell/Marketplace success

The best Microsoft Marketplace and co-sell partners treat Microsoft as a customer. They market to the Microsoft sales and go-to-market teams the success they are driving, which in turn is driving further engagement and activity.

Work with your aligned Microsoft Partner Development Manager to create a regular drumbeat of communication or use your Marketplace Rewards GTM benefits to amplify your success with the Microsoft sales teams.

Need more help?

Speak to your Partner Development Manager or aligned engagement manager
Check out App Advisor for a guided experience on the journey for software development companies.

Let me know your feedback or if you have any additional recommendations. Thanks

Lee Corbett - EMEA ISV Recruit & Grow Lead

When cloud apps become a weak link: How FortiAppSec Cloud in Microsoft Marketplace bridges the gap

srijarallam — Wed, 22 Apr 2026 13:00:00 GMT

In this guest blog post, Srija Reddy Allam, Cloud Security/DevOps Architect, Fortinet, discusses the increase of attacks targeted at web applications and APIs and how FortiAppSec Cloud in Microsoft Marketplace provides a layer of adaptive security to address the challenge.

Picture this: A global e-commerce company is gearing up for its biggest holiday sale of the year. Their storefront and application programming interfaces (APIs) are hosted on Microsoft Azure, taking advantage of the platform’s ability to scale across regions and keep the lights on when traffic skyrockets. Azure does its part by providing a secure, resilient foundation, but, as with any cloud service, it’s the responsibility of the customer to protect their apps and data that sit on top.

When the sale kicks off, traffic pours in. Alongside real shoppers come automated bots trying stolen usernames and passwords on the login page. It’s not an Azure issue; it’s simply the reality of today’s internet, where attackers go after applications directly.

That’s where FortiAppSec Cloud integrated with Azure steps in. FortiAppSec Cloud filters out the noise by blocking credential-stuffing bots, securing APIs, and making sure legitimate customers get through without friction. The company doesn’t have to panic about spinning up extra servers or manually blocking traffic; the defenses scale automatically, right alongside Azure.

The result is shoppers stay online, carts don’t get abandoned, and the brand’s reputation is protected. With Azure as the foundation and FortiAppSec Cloud adding that extra layer of defense, the company can focus on what matters most: giving customers a smooth holiday shopping experience.

Why cloud applications are under siege

Over 70 percent of breaches today involve attacks against web applications or APIs. Azure makes it easier than ever to host, scale, and distribute applications globally, giving organizations the agility they need to innovate. With this expanded reach, however, comes greater visibility to attackers, making it essential to pair the scalability of Azure with robust, adaptive application security.

Attack methods keep evolving:

SQL injection (SQLi) and cross-site scripting (XSS): Exploiting insecure inputs to inject malicious SQL commands or scripts that steal data or hijack sessions
Credential stuffing: Automated bots hammering login pages with stolen credentials
API abuse: Exploiting unsecured API management endpoints
Advanced bots: Scraping, hoarding inventory, or flooding checkout pages
Zero-days: Exploiting flaws before patches are released or app updates are applied

Traditional web application firewalls (WAFs) and security appliances can’t keep up in a multi-region and agile-based cloud deployments. A single misconfiguration can create a blind spot big enough for attackers to slip through.

8 ways FortiAppSec Cloud stands out

Fortinet created FortiAppSec Cloud to eliminate complex attacks that target applications and APIs. Delivered as a cloud-native WAF as a service, it integrates seamlessly with Azure environments while protecting applications and APIs globally.

Here are eight ways it stands out for Azure customers:

Cloud-native WAF: No appliances to deploy, patch, or manage. FortiAppSec Cloud delivers WAF as a service, instantly protecting Azure-hosted web apps and APIs without added infrastructure complexity.
AI-powered threat detection: Leveraging artificial intelligence and machine learning (AI/ML), FortiAppSec Cloud identifies anomalous traffic patterns in real time, helping stop zero-day attacks before they cause downtime in cloud-hosted environments.
AI-powered security assistant: FortiAI-Assist, built into FortiAppSec Cloud, adds context to every alert by showing which service is affected, indicating how critical it is, and ranking it by real risk. The assistant reduces noise in daily operations by handling alert triage and investigation. It finds anomalies, summarizes logs, and explains findings in plain language. This helps administrators respond faster, make better decisions, and stay focused on the issues that matter most.
Advanced bot protection: Azure enables businesses to deliver high-performing, customer-facing applications at scale. To strengthen these experiences, FortiAppSec Cloud adds advanced bot mitigation that goes beyond IP blocking, using behavioral analysis and challenge-response techniques to separate human users from malicious automation like credential stuffing, scalper bots, and scrapers.
API protection: Organizations rely on Azure API Management to securely publish and manage APIs. FortiAppSec Cloud complements this by adding API discovery, schema validation, and OWASP API-based attack prevention, ensuring business-critical APIs remain secure and resilient against evolving threats.
Global server load balancing (GSLB): The global infrastructure of Azure provides unmatched reach and reliability. FortiAppSec Cloud complements this by offering GSLB capabilities that intelligently route legitimate traffic to the healthiest Azure region, optimizing performance and availability while reinforcing resilience against attacks.
Threat analytics: Enterprises often deploy workloads across multiple Azure regions for scale and redundancy. FortiAppSec Cloud provides centralized, single-pane-of-glass visibility into attempted attacks, blocked traffic, and compliance reporting, helping teams streamline security operations.
Easy Microsoft Marketplace deployment: FortiAppSec Cloud is natively available in Microsoft Marketplace and eligible to help companies burn down their committed cloud spending, making it easy for organizations to subscribe, deploy, and integrate. With seamless Azure billing and rapid deployment into existing environments, security and agility go hand in hand.

Get started with a free 30-day trial

Azure enables organizations to move faster, scale globally, and serve customers at digital speed. But cloud adoption also expands the attack surface, putting web applications and APIs at risk.

FortiAppSec Cloud, available through Microsoft Marketplace with a free 30-day trial, addresses this challenge head-on. With AI-powered detection, advanced bot defense, GSLB for Azure global resilience, and unified visibility, it allows organizations to secure Azure-hosted applications without slowing down innovation.

Cloud adoption shouldn’t mean compromising security. With FortiAppSec Cloud on Azure, enterprises can defend their apps against today’s most advanced threats while delivering fast, reliable digital experiences everywhere.

Design tenant linking to scale selling on Microsoft Marketplace

Julio_Colon — Wed, 22 Apr 2026 15:36:37 GMT

Designing tenant linking and Open Authorization (OAuth) directly shapes how customers onboard, grant trust, and operate your AI app or agent through Microsoft Marketplace. In this context, consent refers to the explicit authorization a customer tenant grants—via OAuth—for a publisher’s application or agent to access specific resources and perform defined actions within that tenant. This post explains how to design scalable, review‑ready identity patterns that support secure activation, clear authorization boundaries, and enterprise trust from day one.

Guidance for multi‑tenant AI apps

Identity decisions are rarely visible in architecture diagrams, but they are immediately visible to customers. In Microsoft Marketplace, tenant linking and OAuth consent are not background implementation details. They shape activation, onboarding, certification, and long‑term trust with enterprise buyers.

When identity decisions are made late, the impact is predictable. Onboarding breaks. Permissions feel misaligned. Reviews stall. Customers hesitate. When identity is designed intentionally from the start, Marketplace experiences feel coherent, secure, and enterprise‑ready.

This article focuses on how to design tenant linking and consent patterns that scale across customers, offer types, and Marketplace review—without rework later.

You can always get a curated step-by-step guidance through building, publishing and selling apps for Marketplace through App Advisor.

This post is part of a series on building and publishing well-architected AI apps and agents in Microsoft Marketplace. The series focuses on AI apps and agents that are architected, hosted, and operated on Azure, with guidance aligned to building and selling solutions through Microsoft Marketplace.

Why identity across tenants is a first‑class design decision

Designing identity is not just about authentication. It is about how trust is established between your solution and a customer tenant, and how that trust evolves over time.

When identity decisions are deferred, failure modes surface quickly:

Activation flows that cannot complete cleanly
Consent requests that do not match declared functionality
Over‑privileged apps that fail security review
Customers who cannot confidently revoke access

These are not edge cases. They are some of the most common reasons Marketplace onboarding slows or certifications are delayed.

A good identity and access management design ensures that trust, consent, provisioning, and operation follow a predictable and reviewable path—one that customers understand and administrators can approve.

Marketplace tenant linking requirements

A key mental model simplifies everything that follows, separate trust establishment from authorization.

Tenant linking and OAuth consent solve different problems.

Tenant linking establishes trust between tenants
OAuth consent grants permission within that trust

Tenant linking answers:

Which customer tenant does this solution trust?

OAuth consent answers:

What is this solution allowed to do once trusted?

AI solutions published in Microsoft Marketplace should enforce this separation intentionally. Trust must be established before meaningful permissions are granted, and permission scope must align to declared functionality. Making this explicit distinction early prevents architectural shortcuts that later block certification.

Throughout the rest of this post, tenant linking refers to trust establishment, not permission scope.

Microsoft Entra ID as the identity foundation

Microsoft Entra ID provides the primitives for identity-based access control, but the concepts only become useful when translated into publisher decisions.

Each core concept maps to a choice you make early:

Home tenant vs resource tenant
Determines where operational control lives and how cross‑tenant trust is anchored.

App registrations
Define the maximum permission boundary your solution can ever request.

Service principals
Determine how your app appears, is governed, and is managed inside customer tenants.

Managed identities
Reduce long‑term credential risk and operational overhead.

Understanding these decisions early prevents redesigning consent flows, re‑certifying offers, or re‑provisioning customers later. Marketplace policies reinforce this by allowing only limited consent during activation, with broader permissions granted incrementally after onboarding.

Importantly, activation consent is not operational consent. Activation establishes the commercial and identity relationship. Operational permissions come later, when customers understand what your solution will actually do.

OAuth consent patterns for multi‑tenant AI apps

OAuth consent is not an implementation detail in Marketplace. It directly determines whether your AI app can be certified, deployed smoothly, and governed by enterprise customers.

Common consent patterns map closely to AI behavior:

User consent
Supports read‑only or user‑initiated interactions with no autonomous actions.

Admin consent
Enables agents, background jobs, cross‑user access, and cross‑resource operations.

Pre‑authorized consent
Enables predictable, enterprise‑grade onboarding with known and approved scopes.

While some AI experiences begin with user‑driven interactions, most AI solutions in Marketplace ultimately require admin consent. They operate asynchronously, act across resources, or persist beyond a single user session. Aligning expectations early avoids friction during review and deployment.

Designing consent flows customers can trust

Consent dialogs are part of your product experience. They are not just Microsoft‑provided UI.

Marketplace reviewers evaluate whether requested permissions are proportional to declared functionality. Over‑scoped consent remains one of the most common causes of delayed or failed certification.

Strong consent design:

Requests only what is necessary for declared behavior
Explains why permissions are needed in plain language
Aligns timing with customer understanding

Poor explanations increase admin rejection rates, even when permissions are technically valid. Clear consent copy builds trust and accelerates approvals.

Tenant linking across offer types

Identity design must align with offer type; a helpful framing is ownership:

SaaS offers
The publisher owns identity orchestration and tenant linking. Microsoft Marketplace reviewers expect this alignment, and mismatches surface quickly during certification.

Containers and virtual machines
The customer owns runtime identity; the publisher integrates with it.

Managed applications
Responsibility is shared, but the publisher defines the trust boundary.

Each model carries different expectations for control, consent, and revocation. Designing tenant linking that matches the offer type reduces customer confusion.

When consent happens in Marketplace lifecycle

Many identity issues stem from unclear timing. A simple lifecycle helps anchor expectations:

Buy – The customer purchases the offer
Activate – Tenant trust is established
Consent – Limited activation consent is granted
Provision – Resources and configurations are created
Operate – Incremental operational consent may be requested
Revoke – Access and trust can be cleanly removed

Making this sequence explicit in your design—and in your documentation—dramatically reduces confusion for customers and reviewers alike.

How tenant linking shapes Marketplace readiness

Identity tends to leave a lasting impression as it is one of the first architectural design choices encountered by customers.

Strong tenant linking and consent design lead to:

Faster certification (applies to SaaS offer only)
Fewer conditional approvals
Lower onboarding drop‑off
Easier enterprise security reviews

These outcomes are not accidental. They reflect intentional design choices made early.

What’s next in the journey

Tenant identity sets the foundation, but it is only one part of Marketplace readiness. In upcoming guidance, we’ll connect identity decisions to commerce, SaaS Fulfillment APIs, and operational lifecycle management—so buy, activate, provision, operate, and revoke will work together as a single, coherent system.

Key Resources

See curated, step-by-step guidance to help you build, publish, or sell your app or agent (no matter where you start) in App Advisor

Quick-Start Development Toolkit can connect you with code templates for AI solution patterns

Microsoft AI Envisioning Day Events 

How to build and publish AI apps and agents for Microsoft Marketplace

Get over $126K USD in benefits and technical consultations to help you replicate and publish your app with ISV Success 

Customer review: TestMu AI HyperExecute is a game changer

Nikhil_Viswanathan — Tue, 21 Apr 2026 21:16:27 GMT

TestMu AI HyperExecute, a solution published to Microsoft Marketplace, is an end-to-end smart test orchestration tool built on Microsoft Azure Functions and Azure App Services and integrated with Microsoft Teams, Azure Pipelines, GitHub, and other Azure services. Featuring auto-grouping, auto-retry, and fail-fast mechanisms, HyperExecute provides an integrated testing environment with 360 degrees of visibility into test execution to simplify debugging.

Microsoft Marketplace interviewed Abhijeet Teware, Head of QA at Bajaj Finserv Limited in India, to learn what he had to say about HyperExecute.

What do you like best about HyperExecute?

What stands out most is the depth of real device coverage combined with the speed at which we can run tests. With 5,000+ real devices available on the platform, including devices widely used across our key regions like Mumbai, we were finally able to catch device-specific crashes and ANR issues before they reached our users. That alone was a game changer for us, given that 90 percent of our user base is on mobile. On top of that, HyperExecute cut our test execution time dramatically, and SmartUI gave us confidence that our UI wasn’t regressing visually between releases.

We also appreciated how well the platform integrates with our CI/CD pipeline on Azure, which kept our weekly release cadence running smoothly without adding overhead to the QA team. It’s not just a testing tool; it’s become a core part of how we ship reliably.

How has the product helped your organization?

TestMu AI has fundamentally changed how our QA team operates. The most immediate impact was a 5-hour reduction in test execution time per cycle, which directly translated into faster, more frequent deployments. We went from ad-hoc releases to reliable weekly code pushes. We scaled our automation coverage by 40X between 2022 and 2024, moving the bulk of our regression suite away from manual execution. That shift alone reduced human error and freed our engineers to focus on new feature quality rather than repetitive regression checks.

The Private Real Device Cloud also eliminated our dependency on maintaining physical device labs in-house, which was a significant operational cost saving. For a platform where app stability directly affects user access to critical services, the improvement in reliability has had a significant impact on our customer experience.

How are customer service and support?

The support has been one of the best parts of working with TestMu AI. Any time we hit a technical issue, whether it was an integration problem with our Azure-based pipeline or a flaky test environment, the response was fast and genuinely helpful, not just a link to documentation. What I particularly appreciated was how the team stayed engaged beyond just resolving tickets. They proactively flagged new capabilities relevant to our use case, helped us tune our HyperExecute configuration, and were available when we were scaling up our Private Real Device Cloud capacity by 5X. For a QA team running on tight release schedules, that kind of partnership matters a lot.

Any recommendations to other users considering the product?

My biggest piece of advice: don’t start small and scale later. Start at scale. We initially rolled out incrementally and had to retroactively migrate more of our suite onto TestMu AI. If you’re on Azure, take advantage of the fact that the platform integrates cleanly into your existing pipeline, setup is straightforward, and the parallel execution gains are immediate.

Enable HyperExecute early; the speed difference is significant, and it changes how your team thinks about test frequency. Use SmartUI from the start too, visual regressions are easy to miss in code review but very obvious to end users. And if you’re a mobile-first product like us, get onto the Private Real Device Cloud as quickly as possible. The breadth of device coverage is genuinely hard to replicate in-house. Finally, lean on their support team; they’re knowledgeable and proactive, not just reactive.

What is your overall rating for the product?

TestMu AI gets a 5 out of 5 from us. Across every dimension that matters to our team — device coverage, execution speed, automation scalability, CI/CD integration, and support quality — the platform has delivered. We came in with a manual-heavy QA process and a mobile app that was difficult to test consistently at scale. We now run automated regression at 40X the volume we did in 2022, ship weekly with confidence, and have meaningfully reduced the number of issues reaching production. For a mission-critical platform where quality failures have serious implications for our users, that reliability is not just a KPI — it matters. We wouldn’t hesitate to recommend it.

Cloud marketplaces are transforming the way businesses find, try, and deploy applications to help their digital transformation. Learn more about Microsoft Marketplace and find ways to discover the right application for your business needs.

Veeam and Celebal Technologies offer transactable partner solutions in Azure Marketplace

Nikhil_Viswanathan — Mon, 20 Apr 2026 13:00:00 GMT

Microsoft partners like Veeam and Celebal Technologies deliver transact-capable offers, which allow you to purchase directly from Azure Marketplace. Learn about these offers below:

Veeam Data Cloud Vault: Veeam offers a fully managed, secure backup storage on Microsoft Azure that protects your data with zero trust data resilience, including immutability, encryption, and logical air gaps. Veeam Data Cloud Vault simplifies offsite protection with on-demand access and no setup, while keeping costs predictable with straightforward pricing that includes restores and egress.

AICXM: Artificial Intelligence Customer Experience Management: Celebal Technologies’ AICXM turns enterprise contact centers into engagement centers by unifying customer conversations using generative AI virtual assistants, real-time agent assist, and post-call analytics to speed resolution, strengthen compliance, and elevate customer experience across every channel. The solution, built on Microsoft Azure and integrated with a host of Azure AI services, offers five different modules that deliver omnichannel, multilingual self-service, live guidance, QA insights, and continuous agent learning.

Agent Garage: This enterprise platform from Celebal Technologies orchestrates autonomous AI agents to optimize complex workflows. Built on agentic AI architecture, Agent Garage securely connects proprietary systems with multimodal RAG, real-time retrieval, and enterprise observability to eliminate slow handoffs, approvals, and manual rework without exposing data to public GenAI tools. The platform delivers configurable agents and prebuilt connectors across functions to accelerate decisions, reduce effort, and improve outcomes from onboarding and document processing to support and procurement.

Designing a reliable environment strategy for Microsoft Marketplace AI apps and agents

Julio_Colon — Wed, 22 Apr 2026 15:35:25 GMT

Technical guidance for software companies

Delivering an AI app or agent through Microsoft Marketplace requires more than strong model performance or a well‑designed user flow. Once your solution is published, both you and your customers must be able to update, test, validate, and promote changes without compromising production stability. A structured environment strategy—Dev, Stage, and Production—is the architectural mechanism that makes this possible.

This post provides a technical blueprint for how software companies and Microsoft Marketplace customers should design, operate, and maintain environment separation for AI apps and agents. It focuses on safe iteration, version control, quality gates, reproducible deployments, and the shared responsibility model that spans publisher and customer tenants. You can always get a curated step-by-step guidance through building, publishing and selling apps for Marketplace through App Advisor.

This post is part of a series on building and publishing well-architected AI apps and agents in Microsoft Marketplace. The series focuses on AI apps and agents that are architected, hosted, and operated on Azure, with guidance aligned to building and selling solutions through Microsoft Marketplace.

Why environment strategy is a core architectural requirement

Environment separation is not just a DevOps workflow. It is an architectural control that ensures your AI system evolves safely, predictably, and traceably across its lifecycle. This is particularly important for Marketplace solutions because your changes impact not just your own environment, but every tenant where the solution runs.

AI‑driven systems behave differently from traditional software:

Prompts evolve and drift through iterative improvements.
Model versions shift, sometimes silently, affecting output behavior.
Tools and external dependencies introduce new boundary conditions.
Retrieval sources change over time, producing different Retrieval Augmented Generation (RAG) contexts.
Agent reasoning is probabilistic and can vary across environments.

Without explicit boundaries, an update that behaves as expected in Dev may regress in Stage or introduce unpredictable behavior in Production. Marketplace elevates these risks because customers rely on your solution to operate within enterprise constraints.

A well‑designed environment strategy answers the fundamental operational question: How does this solution change safely over time?

Publisher-managed environment (tenant)

Software companies publishing to Marketplace must maintain a clear three‑tier environment strategy. Each environment serves a distinct purpose and enforces different controls.

Development environment: Iterate freely, without customer impact

In Dev, engineers modify prompts, adjust orchestration logic, integrate new tools, and test updated model versions. This environment must support:

Rapid prompt iteration with strict versioning, never editing in place.
Model pinning, ensuring inference uses a declared version.
Isolated test data, preventing contamination of production RAG contexts.
Feature‑flag‑driven experimentation, enabling controlled testing.

Staging environment: Validate behavior before promotion

Stage is where quality gates activate. All changes—including prompt updates, model upgrades, new tools, and logic changes—must pass structured validation before they can be promoted. This environment enforces:

Integration testing
Acceptance criteria
Consistency and performance baselines
Safety evaluation and limits enforcement

Production environment: Serve customers with reliability and rollback readiness

Solutions running in production environments, regardless of whether they are publisher hosted or deployed into a customer's tenant must provide:

Stable, predictable behavior
Strict separation from test data sources
Clearly defined rollback paths
Auditability for all environment‑specific configurations

This model highlights the core environments required for Marketplace readiness; in practice, publishers may introduce additional environments such as integration, testing, or preproduction depending on their delivery pipeline.

The customer tenant deployment model: Deploying safely across customer environments

Once a Marketplace customer purchases and deploys your AI app or agent, they must be able to deploy and maintain your solution across all their environments without reverse engineering your architecture. A strong offer must provide:

Repeatable deployments across all heterogeneous environments.
Predictable configuration separation, including identity, data sources, and policy boundaries.
Customer‑controlled promotion workflows—updates should never be forced.
No required re‑creation of environments for each new version.

Publishers should design deployment artifacts such that customers do not have to manually re‑establish trust boundaries, identity settings, or configuration details each time the publisher releases a solution update.

Plan for AI‑specific environment challenges

AI systems introduce behavioral variances that traditional microservices do not. Your environment strategy must explicitly account for them.

Prompt drift

Prompts that behave well in one environment may respond differently in another due to:

Different user inputs, where production prompts encounter broader and less predictable queries than test environments
Variation in RAG contexts, driven by differences in indexed content, freshness, and data access
Model behavior shifts under scale, including concurrency effects and token pressure
Tool availability differences, where agents may have access to different tools or permissions across environments

This requires explicit prompt versioning and environment-based promotion.

Model version mismatches

If one environment uses a different model version or even a different checkpoint, behavior divergence will appear immediately. Publishers should account for the following model management best practices:

Model version pinning per environment
Clear promotion paths for model updates

RAG context variation

Different environments may retrieve different documents unless seeded on purpose. Publishers should ensure their solutions avoid:

Test data appearing in production environments
Production data leaking into non-production environments
Cross contamination of customer data in multi-tenant SaaS solutions

Make sure your solution accounts for stale-data and real-time data.

Agent variability

Agents exhibit stochastic reasoning paths. Environments must enforce:

Controlled tool access
Reasoning step boundaries
Consistent evaluation against expected patterns

Publisher–customer boundary: Shared responsibilities

Marketplace AI solutions span publisher and customer tenants, which means environment strategy is jointly owned. Each side has well-defined responsibilities.

Publisher responsibilities

Publishers should:

Design an environment model that is reproducible inside customer tenants.
Provide clear documentation for environment-specific configuration.
Ensure updates are promotable, not disruptive, by default.
Capture environment‑specific logs, traces, and evaluation signals to support debugging, audits, and incident response.

Customer responsibilities

Customers should:

Maintain environment separation using their governance practices.
Validate updates in staging before deploying them in production.
Treat environment strategy as part of their operational contract with the publisher.

Environment strategies support Marketplace readiness

A well‑defined environment model is a Marketplace accelerator. It improves:

Onboarding

Customers adopt faster when:

Deployments are predictable
Configurations are well scoped
Updates have controlled impact

Long-term operations

Strong environment strategy reduces:

Regression risk
Customer support escalations
Operational instability

Solutions that support clear environment promotion paths have higher retention and fewer incidents.

What’s next in the journey

The next architectural decision after environment separation is identity flow across these environments and across tenant boundaries, especially for AI agents acting on behalf of users. The follow‑up post will explore tenant linking, OAuth consent patterns, and identity‑plane boundaries in Marketplace AI architectures. See the next post in the series: Designing Tenant Linking to Scale Microsoft Marketplace AI Apps.

Key Resources

See curated, step-by-step guidance to help you build, publish, or sell your app or agent (no matter where you start) in App Advisor

Quick-Start Development Toolkit can connect you with code templates for AI solution patterns

Microsoft AI Envisioning Day Events

How to build and publish AI apps and agents for Microsoft Marketplace

Get over $126K USD in benefits and technical consultations to help you replicate and publish your app with ISV Success

Cegeka, Infinitera, and Fortinet offer transactable partner solutions in Microsoft Marketplace

Nikhil_Viswanathan — Thu, 16 Apr 2026 13:00:00 GMT

Microsoft partners like Cegeka, Infinitera, and Fortinet deliver transact-capable offers, which allow you to purchase directly from Microsoft Marketplace. Learn about these offers below:

Cegeka Quality Impact Recall Agent: Built for regulated industries and the manufacturing sector, this agent from Cegeka analyzes complaints, supplier issues, or failed quality orders, then launches a coordinated recall workflow inside Microsoft Dynamics 365. By automating impact assessment and orchestrating key actions, the agent shortens decision time, reduces variability, and ensures all stakeholders operate from the same trusted information.

toople: Infinitera's toople lets you create and query shareable databases using AI. toople’s built-in AI query, available with premium database subscriptions, helps you quickly uncover insights using your own data. Share your database with your team, select colleagues, or even the public, all while maintaining full control over who can view, edit, or manage your data.

Fortinet FortiAppSec Cloud Web Application and API Protection: FortiAppSec Cloud provides comprehensive web application and API security with a single management interface. Defend against known and zero-day threats with machine learning-enhanced web app and API protection. Choose from three different plans (Standard, Advanced, and Enterprise) to best fit your situation.

How Microsoft Foundry and Elastic Agent Builder in Microsoft Marketplace are shaping enterprise AI

Bergie — Wed, 15 Apr 2026 13:00:00 GMT

In this guest blog post, Brian Bergholm, Principal Product Marketing Manager at Elastic, covers the evolution of agentic AI and how enterprises can leverage context engineering via Microsoft Foundry and Elastic Agent Builder in the Microsoft Marketplace to operationalize agentic systems at scale.

For the past few years, AI conversations have focused heavily on large language models (LLMs): bigger models, faster models, and multimodal models. But a quieter (and arguably more important) shift is underway. The real differentiation is moving above the model layer, toward how systems understand context and take action.

The shift is particularly evident in the evolution from simple, conversational chat interfaces to sophisticated, independent AI agents. These agents are no longer just responding to prompts; they are executing specific tasks, making decisions, and managing complex workflows with their effectiveness deeply informed by advanced context engineering.

This is where context engineering and agentic AI come together and where platforms like Elastic Agent Builder and Microsoft Foundry (formerly known as Azure AI Foundry) are shaping what enterprise AI looks like in practice.

The end of prompt-centric AI

Prompt engineering was a necessary first step. It taught us how sensitive large language models are to instructions, framing, and examples. But prompt-centric AI doesn’t naturally scale well in the enterprise. Why? Prompts lack awareness of real-time data, rely on humans to manually provide context, and don’t translate easily into repeatable systems.

Enter context engineering. Context engineering is the discipline of systematically constructing the information environment AI operates within. Instead of treating context as an afterthought, it becomes a first-class design principle by including:

Relevant data (logs, metrics, tickets, and documents)
User and system state
Goals and constraints
Policies, guardrails, and domain knowledge

The insight driving this trend is simple but powerful: AI output quality is bounded by the quality and relevance of the context it receives.

As enterprises move from demos to production, context engineering is replacing clever prompts as the primary lever for improving outcomes.

Once context is engineered effectively, the next evolution is inevitable: AI systems that can act. Agentic AI systems differ from traditional assistants in a few critical ways:

They operate toward a goal, not just a query.
They can plan multistep actions.
They use tools and data sources.
They adapt based on outcomes.

This matters because enterprise problems are rarely single-turn questions. They are workflows that can:

Investigate an incident
Diagnose a security alert
Optimize system performance
Assist a customer through resolution

Agentic AI represents a shift from AI as an answer engine to AI as an operational participant.

Why are search and observability foundational to agents?

There’s a hard truth about agentic AI: Reasoning without reliable data is just speculation. High-impact agents need fast access to relevant information, the ability to explore and refine queries, and confidence that their results reflect reality, not stale knowledge. Retrieval augmented generation (RAG) provides the right context for every AI answer, ensuring its accuracy.

This is where Elastic Agent Builder becomes strategically important. Elastic Agent Builder allows teams to create AI agents that search and retrieve context from general or proprietary data; ground reasoning in logs, metrics, traces, security events, and content; and continuously refresh context as systems change.

In practice, this means agents can investigate incidents using real-time observability data, correlate signals across systems, and explain why something is happening, not just what happened.

In an agentic world, search is not a feature; it’s a cognitive function.

Microsoft Foundry: Operationalizing agentic systems at scale

While Elastic excels at context and real-time insight, enterprises still need a way to orchestrate, govern, and scale AI systems. This is the role of Microsoft Foundry. Microsoft Foundry provides:

A platform for managing models, agents, and tools
Built-in security, compliance, and governance
Integration with enterprise workflows and applications

In other words, Foundry addresses the question “How do we safely run agentic AI in production?”

When combined with Elastic’s ability to supply high-fidelity, real-time context, Foundry orchestrates the agent lifecycle, permitting agents to reason, act, and adapt with guardrails in place.

The convergence: Systems that understand, decide, and act

What we’re seeing is not just a tooling trend; it’s an architectural shift.

Old paradigm	Emerging paradigm
Prompt-based interactions	Context-engineered systems
Stateless chatbots	Goal-driven agents
AI as a side tool	AI as an operational layer
Static knowledge	Live, searchable context

Elastic Agent Builder and Microsoft Foundry sit at complementary layers of this stack:

Elastic ensures agents stay grounded in truth
Foundry ensures agents operate responsibly and at scale

Together, they enable a new class of enterprise AI: systems that are not just intelligent, but also situationally aware and operationally useful.

Looking ahead: Context is the competitive advantage

As models continue to commoditize, the winners won’t be those with the biggest parameters. Instead, they’ll be the ones who:

Engineer context deliberately
Embed AI into real workflows
Trust agents with meaningful responsibility
Govern those agents effectively

Context engineering and agentic AI are not future concepts. They are already reshaping how work gets done. The organizations that embrace this shift — grounded in platforms like Elastic and Microsoft Foundry — will move faster, respond smarter, and operate with a level of intelligence static systems simply can’t match.

Learn more about context engineering and how autonomous systems are changing cybersecurity. Or, start a seven-day free trial of Elastic on Microsoft Marketplace.

Continuity-of-care, clinically governed AI, and building on Microsoft Azure

justinroyal — Tue, 14 Apr 2026 13:44:51 GMT

In this installment of our Partner Spotlight series, we’re highlighting a partner building secure, scalable solutions on Microsoft Azure and delivering transactable offers on Microsoft Marketplace. I connected with Darren Booker, Founder and CEO of Tali Health, to learn how their clinically governed AI platform helps extend care beyond the clinic while keeping providers in control. By building AI-powered patient engagement on the Microsoft Cloud, Tali is helping care teams deliver more consistent guidance between visits while scaling support without adding burden to clinicians.

About Darren

Darren Booker is the Founder and CEO of Tali Health, a continuity-of-care platform that uses clinically governed AI to extend clinician-guided care beyond the clinic.

_______________________________________________________________________________________________________________________________________________________________

[JR] Tell us about Tali Health—what inspired its founding, and what services do you offer?

[DB] Tali Health is a continuity-of-care platform that uses clinically governed AI to extend clinician-guided care beyond the walls of the clinic. Clinical governance is key—it helps us deliver on-demand guidance and support for patients while keeping providers firmly in control of care.

Healthcare teams invest significant time and expertise developing personalized treatment plans, yet most patient care happens after the visit—at home, between appointments. That’s where confusion, nonadherence, and avoidable complications often occur.

We built Tali to close that gap. Our platform delivers multilingual, care-plan-aligned guidance between visits—helping patients follow provider instructions, understand prescribed medications, and know when symptoms are expected versus when they require attention. Tali also analyzes patient questions and remote monitoring signals—such as blood pressure or glucose levels—and triages them based on thresholds set by the care team.

The result is improved adherence and patient confidence while reducing repetitive outreach and monitoring work for clinical staff.

[JR] Who is your solution designed for, and what problems does it help solve?

[DB] Tali is designed for healthcare organizations and chronic care management vendors that support large volumes of patients who need ongoing engagement outside the clinic—particularly teams delivering chronic care management, remote patient monitoring, and post-procedure follow-up.

The platform acts as an automated, on-demand extension of the care team. Importantly, Tali never diagnoses or makes clinical decisions; it supports clinical teams between visits. Tali is designed to fit into existing provider workflows, including Epic and other EHR-connected environments, so organizations can extend post-visit support without adding disconnected tools.

Patients receive 24/7 support through the Tali app in their preferred language. The system reinforces assigned care plans, answers common questions on demand, and surfaces clinically relevant changes in symptoms or remote monitoring data for care team review.

For example, if a patient reports worsening symptoms or submits monitoring data that crosses a clinician-defined threshold, Tali flags the signal and routes it to the appropriate care team member for follow-up. Meanwhile, routine questions—such as medication timing or post-procedure expectations—are handled automatically through AI-guided responses aligned with the patient’s care plan.

This approach improves adherence and engagement while helping care coordinators focus on meaningful clinical interventions rather than repetitive administrative outreach. By combining clinically governed AI-driven triage with clinician-configured protocols, Tali helps healthcare organizations scale high-touch care models without proportionally increasing staffing requirements.

[JR] Your solution is built on Microsoft Azure. How does Microsoft technology support what you’re building?

[DB] Healthcare data requires a high bar for security and reliability, so we built Tali natively on Microsoft Azure with a HIPAA- and GDPR-aligned architecture. Azure provides the foundation for secure data handling, scalable infrastructure, and regional data residency—capabilities needed to support healthcare providers across multiple markets, including the United States and the Gulf region.

For healthcare organizations evaluating new technologies, trust is essential. Azure helps us deliver AI-powered patient engagement while meeting the compliance, security, and reliability standards required to handle protected health information.

[JR] What motivated you to publish a transactable offer on Microsoft Marketplace?

[DB] Healthcare organizations are increasingly standardizing procurement through trusted cloud marketplaces, and we recognized early that Microsoft Marketplace would be an important channel for reaching the end users Tali was designed to support—including enterprise healthcare buyers. Publishing on Microsoft Marketplace helps healthcare organizations already operating in the Microsoft ecosystem discover, evaluate, and deploy Tali more easily while aligning purchases with their existing Azure cloud commitments. Because Tali is built on Azure, Microsoft Marketplace also creates a seamless procurement path for customers who want infrastructure, security, and compliance alignment from day one.

Another key motivation was the opportunity to participate in Microsoft’s selling-with-Microsoft motion. Healthcare providers already working with Microsoft sellers can discover Tali as a complementary solution that extends the value of Azure infrastructure into patient engagement and remote care workflows. For us, Microsoft Marketplace is not just a listing—it’s a strategic go-to-market channel that helps align technology, procurement, and sales motions to accelerate adoption while maintaining the security and compliance expectations required in healthcare.

[JR] Did Microsoft tools like App Advisor or Marketplace Rewards play a role in your journey?

[DB] Absolutely. App Advisor, Marketplace Rewards, and ISV Success all played an important role in our journey. Beyond technical guidance, these all helped us think strategically about how to scale on Microsoft Marketplace—from optimizing our offer structure to building a clean pipeline and aligning with selling with Microsoft and marketplace growth initiatives. For early-stage companies especially, these opportunities can significantly accelerate both publishing and commercialization on the platform.

[JR] AI is a major focus across Microsoft and Marketplace. How are you integrating AI into your solution?

[DB] Tali is unique in its use of clinically governed AI, meaning its response logic is tied directly to the patient’s assigned care plans and medical history. For patients, this means they receive timely, targeted, and easy-to-understand guidance after a visit—whether they’re asking about medications, recovery expectations, or changes in symptoms. Our AI delivers responses aligned with the patient’s care plan while maintaining a fifth-grade reading level designed for broad accessibility.

On the provider side, AI helps identify signals that require clinical attention. Instead of care teams manually reviewing every incoming message or data point, Tali highlights situations where clinician intervention may be needed based on thresholds defined by the practice.

Looking ahead, we see AI continuing to enhance the ability of healthcare organizations to scale high-quality patient engagement—supporting more proactive care models while allowing clinicians to focus their time on the patients who need them most.

[JR] Security and compliance are critical in healthcare. How did you approach building Tali securely?

[DB] As we expanded Tali’s AI capabilities, security and compliance stayed foundational from the earliest stages of development. Because our platform works with protected health information, we designed our architecture on Microsoft Azure to align with the stringent security expectations of healthcare organizations.

Azure provides the infrastructure capabilities needed to support encrypted data storage, secure identity management, and controlled access to sensitive information. These capabilities allow us to implement HIPAA- and GDPR-aligned safeguards while also supporting regional data residency requirements for markets such as the United States and the Gulf region.

We also follow a principle of minimizing unnecessary exposure to sensitive data wherever possible, with access-controlled systems designed to ensure that only authorized users and components interact with protected health information.

Our goal is to help organizations adopt AI-driven patient engagement while maintaining the security, reliability, and compliance standards their patients expect.

[JR] What lessons would you share with other partners considering Microsoft Marketplace?

[DB] One of the most important lessons is to view Microsoft Marketplace not simply as a listing channel, but as part of a broader go-to-market strategy. Healthcare organizations increasingly prefer solutions that integrate naturally with the technology ecosystems they already trust. By aligning with Azure and Microsoft Marketplace, partners can reduce friction for buyers who want procurement, infrastructure, and security models that work together seamlessly. Another takeaway is to design your solution architecture with enterprise requirements in mind from the beginning. In healthcare, compliance, security, and scalability can’t be afterthoughts—they’re core to adoption. Finally, Microsoft Marketplace can open doors to collaboration with Microsoft sellers and ecosystem partners who are already working closely with enterprise customers. When partners align real customer needs with the strengths of the Microsoft platform, Marketplace becomes far more than a distribution channel—it becomes a catalyst for adoption and impact.

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Closing reflection Tali Health shows how clinically governed AI, built on Microsoft Azure and delivered through Microsoft Marketplace, can help care teams extend support beyond the clinic while maintaining trust, security, and clinical control.

F5, ProsperOps, and SoftGroup deliver transactable offers in Microsoft Marketplace.

Nikhil_Viswanathan — Tue, 14 Apr 2026 13:00:00 GMT

Microsoft partners like F5, ProsperOps, and SoftGroup deliver transact-capable offers, which allow you to purchase directly from Microsoft Marketplace. Learn about these offers below:

F5 NGINXaaS for Microsoft Azure: Unlock advanced programmability, streamlined automation, and robust application delivery services (ADC) with F5 NGINXaaS for Microsoft Azure. F5 NGINXaaS enhances security, observability, and performance to bridge the gap between general-purpose and advanced ADC requirements.

ProsperOps: ProsperOps continually ingests and analyzes granular cost and usage data from Microsoft Azure and other cloud providers to identify savings opportunities and automate optimization. Financial operations (FinOps) teams can maximize their effective savings rate (ESR) without additional overhead.

SATT Platform Bahrain Hub Gateway: The SATT Platform Bahrain Hub Gateway from SoftGroup enables secure, compliant connectivity to the Bahrain National Track & Trace System (NHRA-MVC). Built on Microsoft Azure, the platform automates regulatory data exchange, reporting, and validation for pharmaceutical manufacturers.

Quality and evaluation framework for successful AI apps and agents in Microsoft Marketplace

Julio_Colon — Wed, 22 Apr 2026 15:26:30 GMT

Why quality in AI is different — and why it matters for Marketplace

Traditional software quality spans many dimensions — from performance and reliability to correctness and fault tolerance — but once those characteristics are specified and validated, system behavior is generally stable and repeatable. Quality is assessed through correctness, reliability, performance, and adherence to specifications.

AI apps and agents change this equation. Their behavior is inherently non-deterministic and context‑dependent. The same prompt can produce different responses depending on model version, retrieval context, prior interactions, or environmental conditions. For agentic systems, quality also depends on reasoning paths, tool selection, and how decisions unfold across multiple steps — not just on the final output.

This means an AI app can appear functional while still falling short on quality: producing responses that are inconsistent, misleading, misaligned with intent, or unsafe in edge cases. Without a structured evaluation framework, these gaps often surface only in production — in customer environments, after trust has already been extended.

For Microsoft Marketplace, this distinction matters. Buyers expect AI apps and agents to behave predictably, operate within clear boundaries, and remain fit for purpose as they scale. Quality measurement is what turns those expectations into something observable — and that visibility is what determines Marketplace readiness. You can always get a curated step-by-step guidance through building, publishing and selling apps for Marketplace through App Advisor.

This post is part of a series on building and publishing well-architected AI apps and agents in Microsoft Marketplace. The series focuses on AI apps and agents that are architected, hosted, and operated on Azure, with guidance aligned to building and selling solutions through Microsoft Marketplace.

How quality measurement shapes Marketplace readiness

AI apps and agents that can demonstrate quality — with documented evaluation frameworks, defined release criteria, and evidence of ongoing measurement — are easier to evaluate, trust, and adopt. Quality evidence reduces friction during Marketplace review, clarifies expectations during customer onboarding, and supports long-term confidence in production. When quality is visible and traceable, the conversation shifts from "does this work?" to "how do we scale it?" — which is exactly where publishers want to be.

Publishers who treat quality as a first-class discipline build the foundation for safe iteration, customer retention, and sustainable growth through Microsoft Marketplace. That foundation is built through the decisions, frameworks, and evaluation practices established long before a solution reaches review.

What "quality" means for AI apps and agents

Quality for AI apps and agents is not a single metric — it spans interconnected dimensions that together define whether a system is doing what it was built to do, for the people it was built to serve. The HAX Design Library — Microsoft's collection of human-AI interaction design patterns — offers practical guidance for each one. These dimensions must be defined before evaluation begins. You can only measure what you have first described.

Accuracy and relevance — does the output reflect the right answer, grounded in the right context? HAX patterns Make clear what the system can do (G1) and notify users when the AI is uncertain (G10) help publishers design systems where accuracy is visible and outputs are understood in the right context — not treated as universally authoritative.
Safety and alignment — does the output stay within intended use, without harmful, biased, or policy-violating content? HAX patterns Mitigate social biases (G6) and Support efficient correction (G9) help ensure outputs stay within acceptable boundaries — and that users can identify and address issues before they cause downstream harm.
Consistency and reliability — does the system behave predictably across users, sessions, and environments? HAX patterns Remember recent interactions (G12) and notify users about changes (G18) keep behavior coherent within sessions and ensure updates to the model or prompts are never silently introduced.
Fitness for purpose — does the system do what it was designed to do, for the people it was designed to serve, in the conditions it will actually operate in? HAX patterns make clear how well the system can do what it does (G2) and Act on the user's context and goals (G4) ensure the system responds to what users actually need — not just what they literally typed.

These dimensions work together — and gaps in any one of them will surface in production, often in ways that are difficult to trace without a deliberate evaluation framework.

Designing an evaluation framework before you ship

Evaluation frameworks should be built alongside the solution. At the end, gaps are harder and costlier to close. The discipline mirrors the design-in approach that applies to security and governance: decisions made early shape what is measurable, what is improvable, and what is ready to ship.

A well-structured evaluation framework defines five things:

What to measure — the quality dimensions that matter most for this solution and its intended use cases. For AI apps and agents, this typically includes task adherence, response coherence, groundedness, and safety — alongside the fitness-for-purpose dimensions defined in the previous section.
How to measure it — the methods, tools, and benchmarks used to assess quality consistently. Effective evaluation combines AI-assisted evaluators (which use a model as a judge to score outputs), rule-based evaluators (which apply deterministic logic), and human review for edge cases and safety-relevant responses that automated methods cannot fully capture.
Who evaluates — the right combination of automated metrics, human review, and structured customer feedback. No single method is sufficient; the framework defines how each is applied and when human judgment takes precedence.
When to evaluate — at defined milestones: during development to establish a baseline, pre-release to validate against acceptance thresholds, at rollout to catch regression, and continuously in production to detect drift as models, prompts, and data evolve.
What triggers re-evaluation — model updates, prompt changes, new data sources, tool additions, or meaningful shifts in customer usage patterns. Re-evaluation should be a scheduled and triggered discipline, not an ad hoc response to visible failures.

The framework becomes a shared artifact — used by the publisher to release safely, and by customers to understand what quality commitments they are adopting when they deploy the solution in their environment.

Evaluate your AI agents - Microsoft Foundry | Microsoft Learn

Evaluation methods for AI apps and agents

Quality must be assessed across complementary approaches — each designed to surface a different category of risk, at a different stage of the solution lifecycle.

Automated metric evaluation — evaluators assess agent responses against defined criteria at scale. Some use AI models as judges to score outputs like task adherence, coherence, and groundedness; others apply deterministic rules or text similarity algorithms. Automated evaluation is most effective when acceptance thresholds are defined upfront — for example, a minimum task adherence pass rate before a release proceeds.
Safety evaluation — a dedicated evaluation category that identifies potential content risks, policy violations, and harmful outputs in generated responses. Safety evaluators should run alongside quality evaluators, not as a separate afterthought.
Human-in-the-loop evaluation — structured expert review of edge cases, borderline outputs, and safety-relevant responses that automated metrics cannot fully capture. Human judgment remains essential for interpreting context, intent, and impact.
Red-teaming and adversarial testing — probing the system with challenging, unexpected, or intentionally misused inputs (including prompt injection attempts and tool misuse) to surface failure modes before customers encounter them. Microsoft provides dedicated AI red teaming guidance for agent-based systems.
Customer feedback loops — structured collection of real-world signals from users interacting with the system in production. Production feedback closes the gap between what was tested and what customers actually experience.

Each method has a distinct role. The evaluation framework defines when and how each is applied — and which results are required before a release proceeds, a change is accepted, or a capability is expanded.

Defining release criteria and ongoing quality gates

Quality evaluation only drives improvement when it is connected to clear release criteria. In an LLMOps model, those criteria are automated gates embedded directly into the CI/CD pipeline, applied consistently at every stage of the release cycle.

In continuous integration (CI), automated evaluations run with every change — whether that change is a prompt update, a model version, a new tool, or a data source modification. CI gates catch regressions early, before they reach customers, by validating outputs against predefined quality thresholds for task adherence, coherence, groundedness, and safety.

In continuous deployment (CD), quality gates determine whether a build is eligible to proceed. Release criteria should define:

Minimum acceptable thresholds for each quality dimension — a release does not proceed until those thresholds are met
Known failure modes that block release outright versus those that are tracked, monitored, and accepted within defined risk tolerances
Deployment constraints — conditions under which a release is paused, rolled back, or progressively expanded to a subset of users before full rollout

Ongoing evaluation must be scheduled and triggered. As models, prompts, tools, and customer usage patterns evolve, the baseline shifts. LLMOps treats re-evaluation as a continuous discipline: run evaluations, identify weak areas, adjust, and re-evaluate before changes propagate.

This connects directly to governance. Quality evidence — the record of what was measured, when, and against what criteria — is part of the audit trail that makes AI behavior accountable, explainable, and trustworthy over time. For more on the governance foundation this builds on, see Governing AI apps and agents for Marketplace readiness.

Quality across the publisher-customer boundary

Clear quality ownership reduces friction at onboarding, builds confidence during operation, and protects both parties when behavior deviates. In the Marketplace context, quality is a shared responsibility — but the boundaries are distinct.

Publishers are responsible for:

Designing and running the evaluation framework during development and release
Defining quality dimensions and thresholds that reflect the solution's intended use
Providing customers with transparency into what quality means for this solution — without exposing proprietary prompts or internal logic

Customers are responsible for:

Validating that the solution performs appropriately in their specific environment, with their data and their users
Configuring feedback and monitoring mechanisms that surface quality signals in their tenant
Treating quality evaluation as a shared ongoing responsibility, not a one-time publisher guarantee

When both sides understand their role, quality stops being a handoff and becomes a foundation — one that supports adoption, sustains trust, and enables both parties to respond confidently when behavior shifts.

What's next in the journey

A strong quality framework sets the baseline — but keeping that quality visible as solutions scale is its own discipline. The next posts in this series explore what comes after the framework is in place: API resilience, performance optimization, and operational observability for AI apps and agents running in production environments. See the next post in the series: Designing a reliable environment strategy for Microsoft Marketplace AI apps and agents | Microsoft Community Hub.

Key resources

See curated, step-by-step guidance to help you build, publish, or sell your app or agent (no matter where you start) in App Advisor

Quick-Start Development Toolkit can connect you with code templates for AI solution patterns

Microsoft AI Envisioning Day Events

How to build and publish AI apps and agents for Microsoft Marketplace

Get over $126K USD in benefits and technical consultations to help you replicate and publish your app with ISV Success

Marketplace blog articles

New in Microsoft Marketplace: April 30, 2026

Get it now in our marketplace

Easily deploy virtual machine images

Go further with workshops, proofs of concept, and implementations

Contact our partners

Design predictable AI performance to scale selling through Microsoft Marketplace

Trade-offs in AI performance: latency, quality and cost

How traditional cost model assumptions break down for AI

Keep costs bounded without sacrificing quality

Achieving predictable latency: Business best practices across each layer

Design caching into your architecture from the start

What’s next in the journey

Key Resources

New in Microsoft Marketplace: April 29, 2026

Get it now in our marketplace

Easily deploy virtual machine images

Go further with workshops, proofs of concept, and implementations

Contact our partners

Improve Marketplace outcomes with AI-powered listing recommendations in App Advisor

Why App Advisor became a core part of how we operate

What’s new: AI-powered Marketplace listing recommendations

What this means in practice

How this fits into the bigger picture

Preeminent Technologies and Codemod offer transactable partner solutions in Microsoft Marketplace

From co-sell ready to co-sell active: How to drive Microsoft seller engagement

Why reaching Azure IP co-sell eligibility unlocks enterprise opportunity

What high-performing software companies do differently

1. They build collateral for sellers, not just buyers

2. They treat their partner development manager (PDM) as a working partner

3. They respond to referrals quickly and consistently

4. They submit outbound referrals, not just receive them

5. They actively use Microsoft Azure Consumption Commitments eligibility in sales conversations

The operational side

Three things worth doing immediately

About Clazar

Design predictable usage-based billing for AI apps and agents selling in Microsoft Marketplace

Design predictable usage‑based billing for AI apps and agents selling on Microsoft Marketplace

Why billing for AI systems is different

Start with plan design before you define meters

Choose a billing model that matches how your AI behaves

Metering looks different depending on your offer type

Design metered dimensions customers can actually explain

Track and plan metrics using the Microsoft Marketplace metering service APIs

Add metering guardrails to avoid cost surprises

What’s Next in the Journey

Key Resources

Unlocking hard data estates: How Cloudera on Microsoft Marketplace brings AI to regulated industries

The regulated enterprise reality

The Cloudera + Microsoft solution

A practical path forward

Integrate Marketplace commerce signals to enforce entitlements in AI apps

How fulfillment and entitlement models differ by Microsoft Marketplace offer type

Why AI apps and agents must integrate with Marketplace commerce signals

What Marketplace provides—and what publishers must implement

How fulfillment and entitlement models differ by offer type

Marketplace integration responsibilities by offer type

Designing entitlement enforcement into AI apps and agents

Handling plan changes, upgrades, and feature tiers

Failure, retry, and reconciliation patterns

How Marketplace API integration affects readiness and review

What’s next in the journey

Key resources

Microsoft Marketplace 101: Insights from high performing software development company sellers

LEARNING 1 - Understand the customer benefits of Microsoft Marketplace and weave it into your customer proposal:

LEARNING 2 - Understand how Microsoft partners can help you scale and close customer opportunities:

LEARNING 3 - Accelerate your deal closure by including Microsoft programs and incentives into your customer proposal:

LEARNING 4 - Know your Microsoft customer

Know your customer 1 - Understand the customer's Marketplace Propensity Score to help prioritize your opportunities

Know your customer 2 - Enable more tailored offers for customers by updating your MEDDIC, MEDDPICC or BANT qualification questions with these Marketplace qualifiers:

Know Your Customer 3 - Use the Partner Center referral process to engage incentivized Microsoft sellers and gain customer intelligence:

LEARNING 5 - Celebrate and amplify Microsoft co-sell/Marketplace success

Need more help?

When cloud apps become a weak link: How FortiAppSec Cloud in Microsoft Marketplace bridges the gap

Why cloud applications are under siege

8 ways FortiAppSec Cloud stands out

Get started with a free 30-day trial

Design tenant linking to scale selling on Microsoft Marketplace

Guidance for multi‑tenant AI apps

Why identity across tenants is a first‑class design decision