AI agents on Microsoft Fabric for faster retail merchandising decisions
For our latest in the Partner Spotlight series, we’re highlighting a partner building business-ready AI agents on Microsoft Fabric, so organizations can turn governed enterprise data into faster decisions and automated workflows. I connected with the team at Lucid Data Hub to learn how Lucid Agents Hub brings agentic experiences directly to customers’ data in OneLake, helping retail teams move beyond manual reporting and into repeatable, insight-driven action. About Venu Amancha, Founder & CEO, Lucid Data Hub builds business-ready AI agents that run directly on enterprise data within Microsoft Fabric. Our platform, Lucid Agents Hub, enables organizations to move beyond reporting and into automated, insight-driven workflows without moving data outside their existing security boundaries. _______________________________________________________________________________________________________________________________________________________________ [JR] Who is your solution designed for, and what does it help them do? [VA] Lucid Agents Hub is designed for teams who need to make frequent, high-impact decisions from large volumes of operational data especially merchandising teams, buyers, and store operations leaders in retail. Instead of spending hours assembling recaps and interpreting dashboards, they can receive agent-generated insights and clear, actionable recommendations on a predictable cadence. The AI agent eliminated that manual cycle entirely. It now surfaces those insights automatically, every week, in minutes. One example is our Retail Sales Performance AI Agent, which automates the weekly sales insights cycle for merchandising teams and buyers by analyzing millions of rows of weekly sales, item, and store data across banners and store clusters. [JR] Can you give an example of how the Retail Sales Performance AI Agent solved a customer’s problem? [VA] At Heritage Grocers Group, merchandising teams spent 5+ hours every week manually building sales recaps. They could see what happened—but not why. Buyers lacked a clear view of category trends, item-level performance, quantity shifts, and store-cluster patterns. The Retail Sales Performance AI Agent eliminated that manual cycle. It now surfaces those insights automatically every week in minutes detecting item-level declines, identifying fast-moving margin-positive SKUs, flagging underperforming items by store cluster, and delivering recommendations directly to buyers and store managers. [JR] Which Microsoft technologies or services are foundational to what you’re building? [VA] The solution runs natively on Microsoft Fabric, using OneLake as the unified data layer. Our agents operate directly on enterprise data and inherit existing governance and access controls without additional configuration. Outputs flow into the dashboards, collaboration platforms, and reporting workflows customers already use, so insights show up where decisions get made. Microsoft Fabric was a deliberate choice, not just a default. Our enterprise customers especially in retail already have their critical data living in the Microsoft ecosystem. OneLake means there’s a single, governed copy of that data. No duplication, no movement, no additional risk surface. Our agents read directly from that layer, which means the security and compliance boundaries that customers have already invested in carry over automatically. The value of building on Fabric goes beyond the technical architecture. It fundamentally changes how enterprise buyers evaluate and procure a solution like ours. When IT and security teams see that agents operate entirely within their existing Fabric environment with role-based access controls, workspace permissions, and audit logs they already control. It removes the largest barrier to enterprise adoption: trust. Procurement conversations that used to require months of security review cycles are now dramatically faster. What we didn’t fully anticipate was how much Fabric’s native integration capabilities would simplify end-to-end delivery. Going in, we expected to spend significant engineering time on data pipeline infrastructure. What we found instead was that Fabric’s data ingestion, lakehouse, and compute layers fit together in a way that let our team focus almost entirely on agent logic and business outcomes, not infrastructure plumbing. That shift in where we spend our effort has meaningfully accelerated how quickly we can deploy for new customers and extend to new use cases. [JR] How are you using AI today in Lucid Agents Hub, and what business outcomes have customers seen? [VA] We use Microsoft Azure AI Foundry for core AI and language model capabilities, and Microsoft Fabric Copilot (Fabric IQ) as the data and compute backbone. Together, they power agents that analyze weekly sales data across banners and store clusters, generate narrative-quality insights at the category and SKU level, and deliver clear recommendations without human intervention in the analysis cycle. 5+ hours of weekly manual effort eliminated Item-level sales declines and fast-moving margin-positive SKUs surfaced automatically Top-growth categories and underperforming items identified by store cluster Recommendations delivered directly to buyers and store managers weekly This all leads to faster decisions, stronger merchandising actions, and measurable improvements in product mix, availability, and overall sales performance. [JR] Any architectural decisions or best practices you’d recommend to other partners building agents? How did you approach building securely? [VA] We designed the solution as a coordinated set of specialized agents one for data ingestion, one for validation, and one for insight generation and delivery. Each agent owns a focused task, and together they run as a connected, end-to-end workflow. This makes the system easier to maintain, consistent in its logic, and straightforward to extend to new banners, categories, or use cases. Agents run entirely within the customer’s Microsoft Fabric environment data never leaves the customer’s security perimeter. All access controls, role-based permissions, and governance policies are inherited directly from Fabric. [JR] What motivated you to publish on Microsoft Marketplace? And did you use any Microsoft tools or benefits to support your publishing process? [VA] Publishing on Microsoft Marketplace was a straightforward decision. It gives enterprise customers immediate confidence that they’re procuring from a trusted, Microsoft-validated source instead of navigating a separate vendor relationship. It also simplifies procurement transactions run through an established Microsoft channel; so, customers can move faster than in traditional sales cycles. And it expands our reach to buyers already operating in the Microsoft ecosystem who actively look to Marketplace for solutions. We actively use Marketplace Rewards, which has been valuable for amplifying go-to-market efforts and accessing Microsoft co-marketing resources. We also leverage AI-enabled Marketplace Listing Optimization and related Marketplace content guidance provided through Marketplace Rewards. We used this support primarily to improve our marketplace messaging, positioning, and listing content so it would better resonate with enterprise buyers evaluating solutions within the Microsoft ecosystem. [JR] What key takeaways would you share with other partners building and publishing agents? Any unexpected wins or challenges along the way? [VA] Building and publishing agents can be a complicated endeavor. To other partners, we’d say, start with workflows that are repetitive and directly tied to decisions weekly merchandising recaps are a perfect example. Think end-to-end, not task by task. And build on governed enterprise data from the start, because that’s what drives trust and adoption. An unexpected win was how quickly merchandising teams adapted. Receiving plain-language summaries broken down by banner, store cluster, category, and SKU was more accessible than navigating dashboards. Teams made faster, more confident decisions without needing to interpret raw data themselves. _______________________________________________________________________________________________________________________________________________________________ Closing reflection Lucid Data Hub shows how agents built on Microsoft Fabric can turn governed enterprise data into repeatable, decision-ready insight helping teams act faster while keeping security boundaries and access controls intact.When cloud apps become a weak link: How FortiAppSec Cloud in Microsoft Marketplace bridges the gap
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.
Build observability for scalable AI apps and agents selling through Microsoft Marketplace
Discover how to design observability for AI apps and agents selling through Microsoft Marketplace. This Marketplace Community article explains why visibility into execution behavior is essential for operating AI systems confidently at scale—not just keeping them running. As AI apps and agents reason, branch, retry, and exit dynamically at runtime, traditional infrastructure metrics fall short. Behavioral signals such as execution flow, token usage, latency, and failure patterns help explain what systems are doing, why outcomes occur, and how limits and safeguards shape behavior across tenants and environments. Learn how observability turns runtime telemetry into clarity that supports customer trust, usage‑based billing, and scalable operations. Read more: Design observability for AI apps and agents selling through Microsoft MarketplaceFirm AI for professional services: governed, agentic workflows built on Microsoft Azure
In this installment of our Partner Spotlight series, we’re highlighting partners building industry-focused AI solutions and bringing them to customers through Microsoft Marketplace. I connected with Richard Baskerville from Intapp to learn how the company is delivering Firm AI—governed, agentic capabilities designed specifically for professional and financial services firms—while aligning with Microsoft Azure for security, scale, and enterprise procurement. Intapp’s approach shows what it looks like to pair deep vertical workflow expertise with a trusted cloud platform so customers can adopt AI in a way that is both practical and accountable. About Richard Baskerville Richard Baskerville is a Senior Director at Intapp, where he helps shape strategic AI alliances across the Microsoft ecosystem and beyond. _______________________________________________________________________________________________________________________________________________________________ [JR] Tell us about Intapp. What inspired the founding of the company, and what problems do your solutions help customers solve? [RB] Intapp was founded on a durable observation: professional firms—law firms, accounting practices, private capital firms, and investment banks—operate differently than general enterprises. Their workflows are shaped by client relationships, professional obligations, and regulatory requirements that generic software was never designed to handle. Our founders saw that these firms were either building bespoke systems at enormous cost or forcing themselves into enterprise tools that didn’t fit. Today, Intapp delivers Firm AI—governed AI purpose-built for professional services. Our solutions span the full firm lifecycle: business development through Intapp DealCloud, time capture and billing through Intapp Time, and risk and compliance management across Intapp Conflicts, Intake, Terms, Walls, and Employee Compliance. Underpinning it all is Intapp Celeste, our agentic AI platform, which puts AI to work on the specific workflows that drive firm performance—while keeping humans accountable and in control. For firms where every engagement carries professional liability, that governance layer isn’t a feature; it’s the foundation. [JR] What industries or types of organizations do you primarily serve today? [RB] Intapp serves professional and financial services firms exclusively. That focus is intentional—and it’s what makes us different. Our customers are law firms, accounting and consulting practices, investment banks and advisory firms, private capital managers, and real assets firms. Many are among the largest in their categories globally. These firms share characteristics that set them apart from general enterprises: partnership structures, billable-hour economics, client conflict management, regulatory oversight, and relationship networks spanning decades. Generic CRM or ERP tools aren’t built for these dynamics. Intapp is. That vertical depth—built over more than 20 years—is what Firm AI means in practice: AI that understands the context of a law firm partner’s client obligations or a dealmaker’s fund-level requirements, not just the general shape of business software. [JR] What were your initial expectations for Microsoft Marketplace when you first started your journey? [RB] Our initial expectation was straightforward: Marketplace would give us a cleaner path to transact with customers already operating inside the Microsoft ecosystem. Many of our customers—large law firms and financial services firms—had already committed significant Azure spend through enterprise agreements. Marketplace offered a way to meet them commercially where they already were. What we underestimated was how much Marketplace would shape the broader partnership. We went in expecting a distribution channel. What we found was a framework that connected co-sell, Azure consumption alignment, and joint go-to-market in ways that changed how our teams engaged. The commercial mechanics—particularly MACC drawdown eligibility—became a real conversation-opener with procurement and finance stakeholders, not just a contract path. [JR] What applications do you have available in Microsoft Marketplace, and how do they help customers? [RB] Intapp has 12 SaaS solutions available in Microsoft Marketplace today, transacted via private offers. They span the full professional services firm lifecycle—from relationship intelligence and deal management with Intapp DealCloud, to time capture with Intapp Time, to risk and compliance management across Intapp Conflicts, Intake, Terms, Walls, and Employee Compliance. Because we focus exclusively on professional and financial services, customers aren’t buying horizontal software adapted for their industry; they’re buying solutions designed for their workflows, compliance obligations, and client structures. Looking ahead, Intapp Celeste—our agentic AI platform—will be available in Marketplace via a consumption-based, metered model. That structure matches how agentic AI gets used: variably, tied to real firm activity, and governed end-to-end. [JR] What were the biggest lessons you learned early on when selling through Marketplace? [RB] Three lessons stood out early: Listing is the beginning, not the end. Initial traction required deliberate investment in co-sell enablement—ensuring Microsoft field teams could position Intapp clearly, not just point to a catalog entry. Specificity wins. Our customers are sophisticated buyers. What worked was leading with vertical relevance—speaking directly to the compliance requirements of a law firm or the relationship-data challenges of a private capital manager. Private offers require commercial fluency. Helping customers understand how Intapp maps to Azure commitments—and helping Microsoft sellers tell that story—made a material difference in deal velocity. [JR] How has your business changed with a transactable offer on the Marketplace? [RB] Transactable offers have changed how deals close. Customers with existing Azure commitments can apply Intapp spend against their MACC, removing a procurement obstacle that previously added months to cycles. Finance and procurement can work within familiar Microsoft frameworks rather than running separate vendor onboarding. Marketplace has also expanded our reach. Microsoft’s field organization has relationships we can’t replicate at scale, and co-sell has helped translate that reach into qualified pipeline—especially in segments where we previously had limited coverage. And the signal matters: being transactable in Marketplace reinforces that Intapp is an enterprise-grade partner, not a niche point solution. [JR] How has collaborating with Microsoft sellers impacted your Marketplace growth? [RB] Microsoft sellers are the activation mechanism for our Marketplace offers. Without field alignment, a listing is a catalog entry. With it, it becomes a joint pipeline motion. We’ve invested in enablement—giving sellers the vertical context to position Intapp credibly in front of legal and financial services CIOs, and making it easy to bring us into deals where Azure capabilities are already in the conversation. That alignment shows up in specific segments. In private capital and investment banking, Microsoft enterprise relationships often predate ours—co-sell provides warm introductions backed by a trusted infrastructure partner. In legal, where Microsoft 365 is near-universal, that adjacency and deep interoperability creates natural entry points. Co-sell turns those adjacencies into active pipeline rather than theoretical opportunity. [JR] What has made the co-sell relationship with Microsoft particularly valuable for Intapp? [RB] Co-sell works because it’s structurally aligned, not just commercially convenient. Microsoft’s investment in these verticals—through industry clouds, compliance frameworks, and dedicated field teams—maps directly onto Intapp’s customer base. We’re selling into the same firms, with the same platform expectations underpinning both offerings. What makes it particularly valuable is the mutual trust transfer. Firms hold Microsoft to a high standard for security, data governance, and regulatory compliance. When Microsoft sellers bring Intapp into the conversation, that credibility extends to us and compresses the trust-building phase of an enterprise cycle—especially in regulated industries. [JR] How does Microsoft Marketplace fit into Intapp’s long-term growth strategy? [RB] Marketplace is central to how we scale Firm AI globally. Our ambition is to be the governed AI platform of record for professional firms across legal, private capital, accounting, and advisory. Helping customers decrement MACC through Marketplace purchases is a clear win-win because it aligns platform investment with workflow outcomes. The upcoming Marketplace availability of Intapp Celeste which will offer different commercial models for customers (e.g. consumption based) marks the next phase. As Celeste deepens integration with Microsoft 365, Teams, and Azure AI services, the commercial and technical stories converge—customers can both buy and operate within an architecture where Firm AI and Microsoft’s platform reinforce each other. [JR] What advice would you give other SDCs who are just starting their Microsoft Marketplace journey? [RB] Three things matter most early on: Earn the co-sell relationship before you need it. Invest in enablement early so Microsoft sellers have enough vertical context to represent your value clearly. Get your commercial model right for the channel. Understand how private offers interact with Azure commitments, and plan for consumption-based pricing where it fits AI usage. Lead with a sharp point of view. The partners who gain traction fastest are the obvious choice for a specific industry workflow or problem—know what that is and communicate it consistently. _______________________________________________________________________________________________________________________________________________________________ Closing reflection Intapp’s Marketplace journey shows that industry-specific, governed AI wins when it’s paired with an enterprise platform customers already trust. By making solutions transactable—especially through private offers that align to customer’s existing Azure commitments—Intapp reduces procurement friction and accelerates adoption. And like many successful partners, their growth ultimately comes down to enablement: clear vertical messaging and tight co-sell alignment that turns Marketplace presence into a real, qualified pipeline.AI apps and agents: choosing your Marketplace offer type
Choosing your Marketplace offer type is one of the earliest—and most consequential—decisions you’ll make when preparing an AI app for Microsoft Marketplace. It’s also one of the hardest to change later. This post is the second in our Marketplace‑ready AI app series. Its goal is not to push you toward a specific option, but to help you understand how Marketplace offer types map to different AI delivery models—so you can make an informed decision before architecture, security, and publishing work begins. 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 offer type is an important Marketplace decision Offer type is more than a packaging choice. It defines the operating model of your AI app on Marketplace: How customers acquire your solution Where the AI runtime executes Determining the right security and business boundaries for the AI solution and associated contextual data Who operates and updates the system How transactions and billing are handled Once an offer type is selected, it cannot be changed without creating a new offer. Teams that choose too quickly often discover later that the decision creates friction across architecture, security boundaries, or publishing requirements. Microsoft’s Publishing guide by offer type explains the structural differences between offer types and why this decision must be made up front. How Marketplace offer types map to AI delivery models AI apps differ from traditional software in a few critical ways: Contextual data may need to remain in a specific tenant or geography Agents may operate autonomously and continuously Control over infrastructure often determines trust and compliance How the solution is charged and monetized, including whether pricing is usage‑based, metered, or subscription‑driven (for example, billing per inference, per workflow execution, or as a flat monthly fee) The buyer’s technical capability, including the level of engineering expertise required to deploy and operate the solution (for example, SaaS is generally easier to consume, while container‑based and managed application offers often require stronger cloud engineering and DevOps skills) Marketplace offer types don’t describe features. They define responsibility boundaries—who controls the AI runtime, who owns the infrastructure, and where customer data is processed. At a high level, Marketplace supports four primary delivery models for AI solutions: SaaS Azure Managed Application Azure Container Virtual Machine Each represents a different balance between publisher control and customer control. The sections below explain what each model means in practice. Check out the interactive offer selection wizard in App Advisor for decision support. Below, we unpack each of the offer types. SaaS offers for AI apps SaaS is the most common model for AI apps and agents on Marketplace—and often the default starting point. With a SaaS offer, the AI service runs in the publisher’s Azure environment and is accessed by customers through a centralized endpoint. This model works well for: Multi‑tenant AI platforms and agents Continuous model and prompt updates Rapid experimentation and iteration Usage‑based or subscription billing Because the service is centrally hosted, publishers retain full control over deployment, updates, and operational behavior. For multi-tenant AI apps, this also means making early decisions about Microsoft Entra ID configuration—such as how customers are onboarded, whether access is granted through tenant-level consent or external identities, and how user identities, roles, and data are isolated across tenants to prevent cross-tenant access or data leakage. For official guidance, see the SaaS section of the Marketplace publishing guide and the AI agent overview, which describes SaaS‑based agent deployments. Plan a SaaS offer for Microsoft Marketplace. Azure Managed Applications for AI solutions In this model, the solution is deployed into the customer’s Azure subscription, not the publisher’s. There are two variants: Managed applications, where the publisher retains permissions to operate and update the deployed resources Solution templates, where the customer fully manages the deployment after installation This model is a strong fit when AI workloads must run inside customer‑controlled environments, such as: Regulated or sensitive data scenarios Customer‑owned networking and identity boundaries Infrastructure‑heavy AI solutions that can’t be centralized Willingness or need on part of the customer or IT team to tailor the app to the needs of the end customer specific environment Managed Applications sit between SaaS and fully customer‑run deployments. They offer more customer control than SaaS, while still allowing publishers to manage lifecycle aspects when appropriate. Marketplace guidance for Azure Applications is covered in the publishing guide. For more information, see the following links: Plan an Azure managed application for an Azure application offer. Azure Container offers for AI workloads Container offer AI workloads—typically on AKS—using container images supplied by the publisher. This model is best suited for scenarios that require: Strict data residency Air‑gapped or tightly controlled environments Customer‑managed Kubernetes infrastructure The publisher delivers the container artifacts, but deployment, scaling, and runtime operations occur in the customer’s environment. This shifts operational responsibility, risk and compute costs away from the publisher and toward the customer. Container offer requirements are covered in the Marketplace publishing guide. Plan a Microsoft Marketplace Container offer. Virtual Machine offers for AI solutions Virtual Machine offers still play a role, particularly for specialized or legacy AI solutions. VM offers package a pre‑configured AI environment that customers deploy into their Azure subscription. Compared to other models, they offer: Updates and scaling are more tightly scoped Iteration cycles tend to be longer The solution is more closely aligned with specific OS or hardware requirements They are most commonly used for: Legacy AI stacks Fixed‑function AI appliances Solutions with specialized hardware or driver dependencies VM publishing requirements are also documented in the Marketplace publishing guide. Plan a virtual machine offer for Microsoft Marketplace. Comparing offer types across AI‑specific decision dimensions Rather than asking “which offer type is best,” it’s more useful to ask what trade‑offs you’re making in an AI app delivery model comparison. Key lenses to consider include: Who operates the AI runtime day‑to‑day Where customer data and AI prompts inputs and outputs are processed and stored Example: When evaluating Saas vs managed apps for AI, check industry specific compliance requirements to evaluate whether the data has to remain in the customer’s tenant or it can be sent to the publisher’s tenant. How quickly models, prompts, and logic can evolve The balance between publisher control and customer control How Marketplace transactions and billing align with runtime behavior SaaS Container (AKS / ACI) Virtual Machine (VM) Azure Managed Application What it is Fully managed, externally hosted app integrated with Marketplace for billing and entitlement Containerized app deployed into customer-managed Azure container environments VM image deployed directly into the customer’s Azure subscription Azure native solution deployed into the customer’s subscription, managed by the publisher Control plane Publisher‑owned Customer owned Customer owned Customer owned (with publisher access) Operational model Centralized operations, updates, and scaling Customer operates infra; publisher provides containers Customer operates infra; publisher provides VM image Per customer deployment and lifecycle Good fit scenarios • Multi‑tenant AI apps serving many customers • Fast onboarding and trials • Frequent model or feature updates • Publisher has full runtime control • AI apps or agents built as microservices • Legacy or lift-and-shift AI workloads • Enterprise AI solutions requiring customer owned infrastructure Avoid when • Customers require deployment into their own subscription • Strict data residency mandates customer control • Offline or air‑gapped environments are required • Customers standardize on Kubernetes • Custom OS or driver dependencies • Tight integration with customer Azure resources Typical AI usage pattern Centralized inference and orchestration across tenants • Portability across environments is important • Specialized runtime requirements • Strong compliance and governance needs Different AI solutions land in different places across these dimensions. The right choice is the one that matches your operational reality—not just your product vision. Note: If your solution primarily delivers virtual machines or containerized workloads, use a Virtual Machine or Container offer instead of an Azure Managed Application. Supported sales models and pricing options by Marketplace offer type Marketplace offer types don’t just define how an AI app and agent is deployed — they also determine how it can be sold, transacted, and expanded through Microsoft Marketplace. Understanding the supported sales models early helps avoid misalignment between architecture, pricing, and go‑to‑market strategy. Supported sales models Offer type Transactable listing Public listing Private offers Resale enabled Multiparty private offers Azure IP Co‑sell eligible SaaS Yes Yes Yes Yes Yes Yes Container Yes Yes Yes No Yes Yes Virtual Machine Yes Yes Yes Yes Yes Yes Azure Managed Application Yes Yes Yes No Yes Yes What these sales models mean Transactable listing A Marketplace listing that allows customers to purchase the solution directly through Microsoft Marketplace, with billing handled through Microsoft. Public listing A listing that is discoverable by any customer browsing Microsoft Marketplace and available for self‑service acquisition. Private offers Customer‑specific offers created by the publisher with negotiated pricing, terms, or configurations, purchased through Marketplace. Resale enabled Using resale enabled offers, software companies can authorize their channel partners to sell their existing Marketplace offers on their behalf. After authorization, channel partners can independently create and sell private offers without direct involvement from the software company. Multiparty private offers Private offers that involve one or more Microsoft partners (such as resellers or system integrators) as part of the transaction. Azure IP Co‑sell eligible When all requirements are met this allows your offers to contribute toward customers' Microsoft Azure Consumption Commitments (MACC). Pricing section Marketplace offer types determine the AI pricing models available. Make sure you build towards a marketplace offer type that aligns with how you want to deploy and price your solution. SaaS – Subscription or flat‑rate pricing, per‑user pricing, and usage‑based (metered) pricing. Container – Kubernetes‑based offers support multiple Marketplace‑transactable pricing models aligned to how the workload runs in the customer’s environment, including per core, per core in cluster, per node, per node in cluster, per pod, or per cluster pricing, all billed on a usage basis. Container offers can also support custom metered dimensions for application‑specific usage. Alternatively, publishers may offer Bring Your Own License (BYOL) plans, where customers deploy through Marketplace but bring an existing software license. Virtual Machine – Usage-based hourly pricing (flat rate, per vCPU, or per vCPU size), with optional 1-year or 3-year reservation discounts. Publishers may also offer Bring Your Own License (BYOL) plans, where customers bring an existing software license and are billed only for Azure infrastructure. Azure Managed Application – A monthly management or service fee charged by the publisher; Azure infrastructure consumption is billed separately to the customer. Note: Azure Managed Applications are designed to charge for management and operational services, not for SaaS‑style application usage or underlying infrastructure consumption. Buyer‑side assumptions to be aware of For customers to purchase AI apps and agents through these sales models: The customer must be able to purchase through Microsoft Marketplace using their existing Microsoft procurement setup Marketplace purchases align with enterprise buying and governance controls, rather than ad‑hoc vendor contracts For private and multiparty private offers, the customer must be willing to engage in a negotiated Marketplace transaction, rather than pure self‑service checkout Important clarification Supported sales models are consistent across Marketplace offer types. What varies by offer type is how the solution is provisioned, billed, operated, and updated. Sales flexibility alone should not drive offer‑type selection — it must align with the architecture and operating model of the AI app and agent. How this decision impacts everything that follows Offer type selection for AI apps and agents ripples through the rest of the Marketplace journey. They directly shape: Architecture design choices Security and compliance boundaries Fulfillment APIs and billing integration Publishing and certification requirements Cost, scalability, and operational responsibility Follow the series for updates on new posts. What’s next in the journey With the offer type decision in place, the focus shifts to turning that choice into a production‑ready solution. This includes designing an architecture that aligns with your delivery model, establishing clear security and compliance boundaries, and preparing the operational foundations required to run, update, and scale your AI app or agent confidently in customer environments. Getting these elements right early reduces rework and sets the stage for a smoother Marketplace journey. See the next post in the series: Designing Production‑Ready AI App and Agent Architectures for Microsoft Marketplace. 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 SuccessSuccess with AI apps and agents on Marketplace: the end-to-end
Preparing an AI app or agent for Microsoft Marketplace requires solving a broader set of problems—ones that extend beyond the model and into architecture, security, compliance, operations, and commerce. These requirements often surface late, when teams are already moving toward launch. Teams often reach the same milestone: the AI works, the demo is compelling, and early customers are interested. But when it’s time to publish, transact, and operate that solution through Marketplace, gaps emerge—around security, compliance, reliability, operations, or commerce integration. Whether you are demo ready or starting with a great AI idea, this series is designed to address those challenges across the AI app publishing lifecycle through a connected, end‑to‑end journey. It brings together the decisions and requirements needed to build AI apps and agents that are not only functional, but Marketplace‑ready from day one. 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 on 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 an end‑to‑end journey matters A working AI app does not automatically mean a Marketplace‑ready AI app. Marketplace readiness spans far more than model selection or prompt engineering. It requires a holistic approach across: Architecture and hosting design Security and AI guardrails Compliance and governance Operational maturity Commerce, billing, and lifecycle integration While guidance exists across each of these areas, it is often fragmented. This series connects those pieces into a single, reusable mental model that software companies can use to design, build, publish, and operate AI apps and agents—supporting end‑to‑end AI readiness with confidence. This first post frames the journey. Each subsequent post goes deep into one stage. The marketplace‑ready AI app and agent lifecycle At a high level, Marketplace‑ready AI apps and agents follow this lifecycle: Define how the AI app and agent will be delivered Identify industry compliance and regulatory requirements Design a production‑ready AI architecture Embed security and AI guardrails into the design Validate compliance and governance Build and test an MVP with potential customers Build for quality, reliability, and scale Integrate with Marketplace commerce Prepare for publishing and go‑live Operate, monitor, and evolve safely Promoting your AI app and agent to close initial sales This lifecycle is intentionally introduced once, at a high level and serves as an AI app launch checklist, where decisions made earlywill shape everything that follows. Throughout the series, this lifecycle serves as a shared reference point. Step 1: Decide how your AI app and agent will be packaged and delivered The first decision is how the AI app and agent will be delivered through Marketplace. Offer types—such as SaaS, Azure Managed Applications, Containers, and Virtual Machines—are not just listing formats. They are delivery models that directly impact: Identity and authentication Billing and metering Deployment responsibilities Operational ownership Customer onboarding experience Supported sales models Choosing an offer type early helps avoid costly redesigns later. Step 2: Design a production‑ready AI architecture Marketplace AI apps and agents are expected to meet enterprise customer expectations for performance, reliability, and security. Architecture decisions must account for: Customer business, compliance, and security needs Offer‑specific hosting best practices For example, SaaS offers typically require: Tenant isolation Environment separation Strong identity boundaries Architecture must also support both AI behavior and Marketplace lifecycle events, such as provisioning, subscription changes, and entitlement checks. Step 3: Secure the AI app and agent and define guardrails Security cannot be treated as a certification checklist at the end of the process. AI introduces new risks beyond traditional applications, including expanded attack surfaces through prompts and inputs. Frameworks such as the OWASP GenAI Top 10 provide a useful lens for identifying these risks. Guardrails must be enforced: At design time through architecture and policy decisions At runtime through monitoring, enforcement, and response AI‑specific incident response must also factor in privacy regulations and customer trust. Step 4: Treat AI agents as compliance‑governed systems AI agents and their data are first‑class compliance subjects. This includes: Prompts and responses Contextual and training data Actions taken by the agent These artifacts must be auditable and governed inline, not retroactively. At the same time, publishers must balance compliance with intellectual property protection by enabling explainability and transparency without exposing proprietary logic. Step 5: Build for quality, reliability, and scale Marketplace buyers expect predictable behavior. AI apps and agents should formalize: Quality and evaluation frameworks Reliability and performance targets Scaling and cost optimization strategies Quality, reliability, and performance directly influence customer trust and satisfaction. Step 6: Integrate with Marketplace commerce and lifecycle APIs Marketplace is not “just a listing,” but a runtime contract that depends on AI commerce integration. For transactable offers that help you sell globally direct to customers or through channel and allow customers to count sales of your app against their cloud commitments, Marketplace becomes an operational contract. Subscription state, entitlements, billing, and metering are runtime responsibilities of the application. For SaaS offers, SaaS Fulfillment APIs define the source of truth for subscription lifecycle events. Integrate Marketplace lead flows with your CRM using the Marketplace lead connector for CRM Step 7: Prepare for publishing, certification, and go‑live Publishing requires more than code completion. Marketplace certification validates: Security posture Customer experience Operational readiness Using templates, checklists, and tooling such as Quick Start Development Toolkit, Marketplace Rewards resources, and App Advisor reduces friction and rework. Step 8: Operate and evolve safely after go‑live Launch is not the end of the journey. AI apps and agents evolve continuously, making: Safe deployment strategies CI/CD discipline Rollback and monitoring practices This is essential for protecting both customers and publishers. Operational maturity also includes maintaining Marketplace offer assets (store images) as the product evolves. Use this framework to help you build a production ready AI app and agent, well architected, secured, reliable, scalable and integrated with Microsoft Marketplace global commerce engine. Step 9: Promote your AI app and agent Becoming Marketplace‑ready does not end at publication. AI app and agent success also depends on how effectively the solution is discovered, evaluated, and trusted by customers within Microsoft Marketplace and the broader Microsoft ecosystem. Promotion in Microsoft Marketplace is tightly integrated with how customers discover and purchase solutions. AI apps and agents are surfaced through Marketplace search, categories, and in‑product experiences, and once your AI app or agent becomes Azure IP co-sell eligible - the purchase of your offer can count towards your customers' Microsoft Azure Consumption Commitments (MACC) motivating customers to buy your offer. This reduces buying friction and accelerates evaluation‑to‑purchase transitions. Top activities to grow your sales: Optimize your listing once you publish your app, by getting an agentic review of your published listing in seconds, based on Marketplace listing best practices and expert Microsoft editorial guidance. Promote your Marketplace offer and track your engagement following best practices. Manage and nurture leads from trials to purchase, and from purchase to higher level SKUs. Private offers, which allow publishers to create customer-specific or negotiated offers directly through Marketplace, including multiparty private offers involving Microsoft channel partners Sell through channel, use resale enabled offers to enable resellers and channel partners to sell your app to their customers, Co-sell motions, where eligible AI apps and agents are sold jointly with Microsoft sellers and count toward customer cloud consumption commitments Effective customer engagement depends on alignment between how the AI app and agent is positioned and how it is delivered. Clear descriptions, accurate architectural boundaries, and transparent operational expectations help customers move confidently from discovery to production adoption. For publishers, programs such as ISV Success provide guidance and tooling to help align technical readiness, Marketplace requirements, and go‑to‑market execution as AI apps and agents scale through Microsoft Marketplace. Sales don't happen by accident, it's essential you engage in promoting your marketing. When promotion is treated as a first‑class step in the lifecycle, it reinforces trust, accelerates evaluation, and increases the likelihood that an AI app and agent transitions from initial interest to sustained use. How to use this series This series is designed to be used in two ways: Read sequentially to understand the full Marketplace‑ready journey Use individual posts alongside Microsoft Learn content, App Advisor, and Quick Start resources for deeper implementation guidance This series provides a structured, end‑to‑end view of what it takes to move from a working AI app and agent to a solution that customers can trust, deploy, and buy through Marketplace. It is designed to complement hands‑on implementation guidance, including Microsoft Learn resources such as Publishing AI agents to the Microsoft marketplace, and to help software companies navigate Marketplace readiness with fewer surprises and less rework. The upcoming post is about choosing your marketplace offer type which defines the operating model of your AI app or agent on Marketplace and influences key architectural decisions for your app or agent. 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 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 SuccessOptimizing Azure spend with Microsoft Marketplace
As someone deeply involved with Microsoft Marketplace product marketing team, I was excited to host our recent customer office hour session with Trunal Bhanse, CEO of Clazar. Our conversation focused on using Microsoft Marketplace to optimize Azure spend. The session explored how organizations can leverage Marketplace as a strategic procurement engine and maximize their cloud investments. Setting the stage: Marketplace as a growth engine Every organization today is striving to become a frontier firm—enriching employee experiences, reinventing customer engagement, and reshaping business processes. With AI at the center of transformation, the question often arises: should we build or buy AI solutions? If buying, how do we procure them efficiently and securely? That’s where Microsoft Marketplace comes in. It’s your trusted source for cloud solutions, AI apps, and agents, offering the largest catalog in the industry. Marketplace is fully integrated with Microsoft Cloud, providing a seamless experience from discovery to deployment. Whether you need standard contracts, private offers, or multi-year agreements, Marketplace adapts to your procurement needs and ensures your transactions are visible in the Azure cost management portal. Azure spend optimization: The power of Microsoft Azure Consumption Commitment (MACC) A major focus of our session was the Microsoft Azure Consumption Commitment (MACC). This agreement allows organizations to commit to a certain level of Azure consumption in exchange for discounted rates. The beauty of MACC is that eligible Marketplace transactions decrement your commitment dollar-for-dollar. That means when you purchase MACC-eligible solutions through Marketplace, you’re directly funding your cloud investments and maximizing your discounts. Our conversation covered how to identify MACC-eligible solutions using tools like Azure Marketplace Compass, the Azure portal, and Marketplace storefront. With over 4,000 eligible solutions available, most organizations can find the software they need and align it with their MACC commitments. This approach is especially valuable at fiscal year-end or when budgets are tight, allowing you to leverage your commitment for critical investments. Operationalizing Marketplace procurement To truly optimize spend companies should start with an inventory of all solutions currently deployed or planned for procurement across their organization. By mapping this inventory against MACC-eligible offers, they can ensure every purchase maximizes commitment and discounts. Security and governance are also paramount. Marketplace enables role-based access controls and private marketplaces, so only authorized employees can procure approved applications. This walled-garden approach gives administrators full control over what’s available for procurement. Partner solutions and automation To bring the MACC optimization process to life, Clazar provided a live demonstration of their platform which specializes in automation of this process. Their solution enables organizations to seamlessly match their software inventory against MACC-eligible offers, giving procurement and finance teams consolidated visibility into spend and streamlining the entire procurement workflow. With robust integrations for single sign-on-systems and automated dashboards, Clazar empowers customers to instantly identify eligible applications and make faster, more informed decisions about their Azure Marketplace investments Microsoft Marketplace is more than a procurement platform—it’s a strategic lever for optimizing Azure spend, accelerating innovation, and simplifying operations. By aligning purchases with MACC commitments, organizations unlock savings, streamline processes, and gain unparalleled visibility into their cloud investments. To learn more, watch the full recording of our conversation here: Using Microsoft Marketplace to optimize Azure spend - Microsoft Marketplace Community Resources Microsoft Marketplace: Microsoft Marketplace | cloud solutions, AI apps, and agents Azure Consumption Commitment (MACC) benefit: Azure Consumption Commitment Benefit - Marketplace customer documentation | Microsoft Learn Cost management for Microsoft Marketplace purchases: Cost management for Microsoft Marketplace purchases - Marketplace customer documentation | Microsoft LearnPartner perspective: How Breakthru uses App Advisor and AI-listing optimization to drive growth
Optimizing a Marketplace listing isn’t just a marketing exercise—it directly impacts discoverability, demand, and revenue. But knowing what to change (and when) can be challenging for software development companies. In this partner‑written blog post, Marketplace software development company Breakthru shares firsthand experience using AI‑powered listing recommendations in App Advisor to move from guesswork to confident, data‑driven optimization—without risking listing performance. Dan Langille also reflects on how App Advisor became a core part of their business, what’s working in practice, and how AI is changing how teams iterate on their Marketplace presence. 👉 Read the partner story here: Improve Marketplace outcomes with AI‑powered listing recommendations in App Advisor Discussion prompts for the community: Would AI‑driven recommendations change how often you iterate on your listing? Have you used App Advisor for selling and growing app and AI agent sales? Curious to hear how other Marketplace partners are approaching listing optimization today!
