Design AI guardrails to support and secure enterprise-ready apps and agents in Microsoft Marketplace
As AI-powered apps and agents become more autonomous, clearly defined guardrails are essential for helping protect sensitive data, control system behavior, and meet Marketplace certification and enterprise security expectations. This article explores how software companies can design enforceable guardrails that enable safe AI autonomy—supporting reliability, scalability, and customer trust from day one. Read the full article: Designing AI guardrails for apps and agents in Marketplace
Azure Marketplace - Could not Create marketplace item
I am trying to create a marketplace offering on Azure. After successfully going through the publishing process, when I click on my offering I just see this: The "Preview" works fine though. I uploaded an app icon which shows in the preview, so not really sure what Gallery item it's asking for.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. This post is part of a series on building and publishing well-architected AI apps and agents on 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 With container offers, the customer runs the AI workload—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. 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 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 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 decisions ripple 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. 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 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 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. This post is part of a series on building and publishing well-architected AI apps and Agents on 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 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. Decisions made early will 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.” 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. Stay tuned for the upcoming post 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 SuccessSecuring AI agents in the agentic era
As AI agents take on more autonomous roles across enterprise applications, security must evolve just as quickly. In this article, explore a practical security playbook for the agentic era—covering key risks, governance considerations, and foundational principles for deploying and scaling AI agents responsibly. You’ll also learn why these security considerations matter for solutions built, published, and transacted through Microsoft Marketplace, where trust, compliance, and enterprise readiness are essential. Read the full article to understand what secure AI agent adoption means for today’s enterprises—and Marketplace publishers. Securing AI agents: The enterprise security playbook for the agentic era | Microsoft Community HubMove from idea to a Marketplace-ready app faster with the Quick-Start Development Toolkit
Want to jump right to the Quick-Start Toolkit? Visit the Quick-Start Development Toolkit in App Advisor. Software companies building applications and agents for Microsoft often face the same early challenge: getting from idea to a working build to get to market fast. Early development slows down. Teams lose momentum. And they never reach the stage of publishing or selling their application. The Quick-Start Development Toolkit in App Advisor changes that. It provides guided, action-oriented resources that helps developers move from idea to build faster with ready-to-deploy code templates, sample reference architectures, and actionable how-to resources. Instead of searching across documentation, your team can immediately focus on the next step of building an app or agent optimized for Microsoft Marketplace. Developers know early momentum matters At this stage, the priority is simple: validate ideas quickly. The Quick-Start Development toolkit is designed to assist building early momentum in three key business scenarios and works best if you: have a clear app or AI agent concept but aren’t sure how to construct it on Microsoft, understand the Microsoft technology stack but want to build faster, or want to replicate an existing solution in the Microsoft Marketplace. The toolkit supports development across several of today’s most common application scenarios. AI and agent-based applications, AWS to Azure migration and replication patterns, Security-focused applications, agents, and integrations. Across each scenario, developers are guided with tools to: Start building without complex setup, Understand the right architecture patterns, Move quickly from concept to a working prototype. The Quick-Start Development Toolkit focuses on streamlining that initial process with deployable code templates, architecture guidance, and development workflows, so teams can move forward with confidence and clarity toward a Marketplace-ready application. A guided starting point for building on Microsoft The experience begins with a simple, interactive wizard. Developers answer a few short questions about what they are building and the scenario they are targeting. Based on responses, you’ll be routed directly to the most relevant development pattern to begin development. This includes: Deployable code templates that help teams start coding immediately, Reference solution architectures aligned with common build patterns, Targeted how-to guidance for the next development step. This approach helps you move from exploration to execution faster by eliminating the need to search across multiple resources before starting the build. Designed to reduce friction, eliminate guesswork, and accelerate progress The Quick-Start Development Toolkit is built around three core outcomes: Reduce friction Start building without lengthy environment setup, Access always-on self-serve development resources, Launch preconfigured services with click-to-deploy templates. Eliminate guesswork Follow reference architectures aligned with proven development patterns, See deployable code templates to iterate and deploy, Avoid rework caused by unclear design decisions. Accelerate progress Move quickly from concept to prototype, Maintain momentum throughout the build stage, Focus on the next most relevant development action. Together, these capabilities help your team move faster while building on patterns designed to maximize Marketplace outcomes. Start building with Marketplace in mind While the toolkit focuses on accelerating the build stage, it is designed with the full Marketplace journey in mind. By following these development patterns, you can build apps and agents that align with the requirements for publishing and selling through Microsoft Marketplace. That alignment helps reduce friction later in the process when teams move from development to publishing to maximize go-to-market opportunities. Visit https://aka.ms/QuickStartToolkit to explore the experience, answer a few questions about what you’re building, and start coding in minutes.Turning AI Insights into Marketplace-Ready Solutions
Want to accelerate your AI journey on Microsoft Marketplace? This blog distills key takeaways from recent Microsoft and partner webinars, giving you expert guidance on building production-ready AI apps and agents. Learn best practices for performance, deployment, and scaling—so your solutions reach more customers, faster. Don’t miss these insider insights—read the full article today: Building production‑ready AI apps and agents for Microsoft MarketplaceHow do you actually unlock growth from Microsoft Teams Marketplace?
Hey folks 👋 Looking for some real-world advice from people who’ve been through this. Context: We’ve been listed as a Microsoft Teams app for several years now. The app is stable, actively used, and well-maintained - but for a long time, Teams Marketplace wasn’t a meaningful acquisition channel for us. Things changed a bit last year. We started seeing organic growth without running any dedicated campaigns, plus more mid-market and enterprise teams installing the app, running trials, and even using it in production. That was encouraging - but it also raised a bigger question. How do you actually systematize this and get real, repeatable benefits from the Teams Marketplace? I know there are Microsoft Partner programs, co-sell motions, marketplace benefits, etc. - but honestly, it’s been very hard to figure out: - where exactly to start - what applies to ISVs building Teams apps - how to apply correctly - and what actually moves the needle vs. what’s just “nice to have” On top of that, it’s unclear how (or if) you can interact directly with the Teams/Marketplace team. From our perspective, this should be a win-win: we invest heavily into the platform, build for Teams users, and want to make that experience better. Questions to the community: If you’re a Teams app developer: what actually worked for you in terms of marketplace growth? Which Partner programs or motions are worth the effort, and which can be safely ignored early on? Is there a realistic way to engage with the Teams Marketplace team (feedback loops, programs, office hours, etc.)? How do you go from “organic installs happen” to a structured channel? Would really appreciate any practical advice, lessons learned, or even “what not to do” stories 🙏 Thanks in advance!Building production‑ready AI apps and agents for Microsoft Marketplace
What developers can learn from Microsoft and partner AI webinars As software companies race to build AI‑powered applications and agents, success in Microsoft Marketplace requires more than a compelling idea. Customers expect solutions that are secure, scalable, governed, and built on trusted Azure services. A recent set of Microsoft and partner webinars offer practical guidance for developers who are building AI apps or agent‑based solutions with the intent to commercialize them through Microsoft Marketplace. Together, these sessions highlight how Microsoft is evolving the AI development lifecycle—from agentic DevOps and secure agent architectures to real‑world customer examples—helping software developers move from experimentation to enterprise‑ready solutions. From prototype to product: Agentic DevOps on Azure One of the biggest challenges Marketplace publishers face is turning an AI prototype into a reliable, supportable product. The “Transform Software Development with Agentic DevOps” webinar shows how Microsoft is embedding AI agents across the entire software development lifecycle using tools like GitHub Copilot and Azure services. Rather than focusing only on code generation, agentic DevOps introduces intelligent agents that assist with planning, implementation, testing, and operational insights. For Marketplace developers, this approach directly supports: Faster iteration while maintaining quality Improved code consistency and security posture Reduced technical debt as applications evolve These practices align closely with what enterprise buyers expect when evaluating Marketplace solutions: predictable delivery, maintainability, and long‑term support readiness. Building AI apps that scale on Azure: Microsoft and NVIDIA, better together Performance and scalability are critical for AI solutions sold through Marketplace. The “NVIDIA and Generative AI: Better Together – Building Your AI Apps” webinar focuses on how developers can build and deploy generative AI applications on Azure using optimized infrastructure and models such as PHI‑3, combined with NVIDIA acceleration. This content is especially relevant for Marketplace publishers because it addresses common customer concerns: Running AI models efficiently at scale Optimizing performance without custom infrastructure Deploying AI workloads using Azure‑native services By leveraging Azure AI services and NVIDIA‑optimized components, developers can deliver solutions that meet enterprise performance expectations while remaining aligned with Azure consumption models commonly used in Marketplace offers. Real‑world agentic AI in action: Lessons from Pantone The “Color Meets Code: Pantone’s Agentic AI Journey on Azure” webinar provides a concrete example of how a software company built an agentic AI experience using Azure services such as Azure AI Search, Microsoft Foundry, and Azure Cosmos DB. Pantone’s journey illustrates several principles that translate directly to Marketplace‑ready solutions: Using agentic architecture to deliver domain‑specific expertise Grounding AI responses with enterprise data using retrieval‑augmented generation Designing AI experiences that scale globally while maintaining consistency For Marketplace developers, this case study demonstrates how agent‑based applications can deliver differentiated value when built on Azure’s AI and data platforms—an important consideration when positioning an offer to enterprise buyers. Designing secure and governed AI agents on Azure Enterprise customers evaluating Marketplace solutions expect strong security and governance. The “Powerful and Secure Agents on Azure” webinar highlights how Microsoft is approaching secure AI agent design, emphasizing identity, access control, and operational oversight. This guidance is particularly relevant for Marketplace publishers building autonomous or semi‑autonomous agents, as it reinforces the importance of: Running agents within Azure’s security and compliance frameworks Applying governance to agent behavior and access Designing AI solutions that can operate safely in enterprise environments These considerations are essential for earning customer trust and supporting broader adoption through Microsoft Marketplace. What this means for Microsoft Marketplace publishing For software companies building AI apps and agents, these sessions reinforce a clear takeaway: enterprise-ready AI starts with how you build—and succeeds with how you publish. If you plan to distribute your solution through Microsoft Marketplace, now is the time to: Design for enterprise trust from day one Build agents on Azure using secure, governed architectures that meet customer expectations for security, compliance, and operational control. Move from prototype to production readiness Apply agentic DevOps practices to improve code quality, reliability, and maintainability—critical factors for customer adoption and long-term success in Marketplace. Differentiate with real-world AI value Ground your AI experiences in domain expertise and enterprise data to deliver outcomes customers can clearly understand, evaluate, and justify purchasing. Align with Azure-native services Solutions built on Azure AI, data, and infrastructure services are easier for customers to deploy, manage, and scale—strengthening your Marketplace positioning. By applying these patterns and best practices, you’re not just building innovative AI apps—you’re creating commercially viable, enterprise-grade solutions ready to be discovered, transacted, and scaled through Microsoft Marketplace. Explore the on-demand sessions to start turning your AI innovation into a Marketplace-ready offering. Transform Software Development with Agentic DevOps NVIDIA and Generative AI: Better Together – Building Your AI Apps Color Meets Code: Pantone’s Agentic AI Journey on Azure Powerful and Secure Agents on AzureAccelerating SaaS success with reference code for Marketplace fulfillment API integration
Boost your growth and reach more customers by replicating your AWS app to Azure to sell through Microsoft Marketplace. This guide will introduce the essential building blocks required for a smooth replication experience and highlight how Marketplace Fulfillment APIs streamline and automate critical post‑purchase workflows. Future posts in this series will explore each topic in more depth to help streamline your multicloud strategy. This post is part of a series on replicating apps from AWS to Azure. View all posts in this series. As a Software Development Company, expanding your Marketplace offer’s reach by replicating your app from AWS to publish to Microsoft Marketplace opens the door to scaling your solution across a global customer base. With millions of organizations using Azure, this ecosystem provides a powerful commercialization channel that enhances discoverability, drives conversions, and delivers a unified, cloud‑native buying experience. You can also join ISV Success to get access to over $126K USD in cloud credits, AI services, developer tools, and 1:1 technical consults to help you replicate your app and publish to Azure Marketplace. To help Software Companies enter the Marketplace successfully, it’s important to understand the operational components that shape the customer experience. One of the most critical components is the Fulfillment API ecosystem, which manages everything from subscription activation to entitlement updates and provisioning workflows. This guide introduces the Fulfillment API model, explains why it is essential for Software Companies preparing or optimizing their SaaS offers for the Marketplace, and directs you to a curated set of resources that provide actionable, hands‑on guidance for implementing these capabilities. Access the resources now or continue reading to learn their importance in creating a successful transactable offer that you can sell through Marketplace. Why fulfillment APIs matter for Marketplace success To sell through the Marketplace smoothly and take advantage of its 6M monthly active shoppers across 141 geographies, you need to integrate fulfillment APIs. Publishing a SaaS offer to the Marketplace is only the first step. What happens after a customer clicks “Subscribe” determines how quickly they can begin using your product—and how seamless their experience will be throughout the lifecycle of their subscription. Fulfillment APIs act as the bridge between the Marketplace and your application. They automatically notify your system when a customer starts, updates, suspends, or ends a subscription. Instead of relying on manual steps or custom internal workflows, the Marketplace standardizes these interactions to ensure predictability and consistency. For Software Companies onboarding to the Marketplace, Fulfillment API integration delivers significant benefits: Immediate and automated customer activation When a customer completes a transaction, the Fulfillment APIs notify your application so you can begin provisioning instantly. This eliminates delays, reduces onboarding friction, and ensures a smooth first‑time user experience. Consistent entitlement management Fulfillment APIs help you maintain accurate entitlement records automatically. Plan changes, cancellations, or updates to the number of users included in a subscription are sent to your application as lifecycle events—ensuring your system always reflects the customer’s current state. Operational efficiency and reduced overhead By relying on a defined, event‑driven model, your teams avoid creating and maintaining custom logic for every lifecycle scenario. This allows you to focus engineering resources on your product instead of transaction plumbing. Scalability across regions and customer segments As your Marketplace presence grows, Fulfillment API integration ensures your operational foundation remains stable, predictable, and ready for increased transaction volume. Support for private offers and custom commercial models The same event‑driven lifecycle applies whether a customer purchases publicly or under a custom private offer—creating a unified, dependable experience. Introducing the fulfillment API resource collection To help Software Companies implement these capabilities quickly, Microsoft provides a comprehensive Fulfillment API resource collection. This curated set brings together conceptual guidance, architectural patterns, learning materials, and hands‑on resources, including the open‑source SaaS Accelerator. The collection gives you everything you need to understand, design, and implement the subscription lifecycle within your own application. Some of the topics covered include: 1. End‑to‑End subscription lifecycle overview A detailed explanation of how Marketplace transactions work, what events your application should expect, and how to handle activation, provisioning, suspension, and cancellation flows. 2. Reference code implementation The SaaS Accelerator demonstrates the full Fulfillment API workflow in a working customizable end‑to‑end solution. Software Companies can use it as a learning tool or a foundational starting point. 3. Architecture and design guidance Clear architectural diagrams and recommendations illustrate best practices for handling webhook callbacks, securing API endpoints, managing tokens, and scaling your implementation. 4. How to build your landing page Guidance on creating a transactable landing page that captures customer information, initiates provisioning, and provides a clear next step for new subscribers. 5. Webhook and callback handling Step‑by‑step patterns for receiving, validating, and processing lifecycle events sent from the Marketplace. 6. Pricing plans, seat management, and entitlements Best practices for aligning your application’s internal logic with the Marketplace’s commercial and operational model. These resources are designed for teams at any stage—whether you’re preparing your first listing or modernizing an existing offer to align with Marketplace standards. Laying a strong foundation for marketplace scale Introducing your SaaS offer to the Marketplace is a significant milestone, but long‑term success requires dependable operational flows and high‑quality customer experiences. Fulfillment API integration is the backbone of these experiences, ensuring that your application responds reliably and consistently to customer actions. By leveraging the curated Fulfillment API collection and the open‑source accelerator, Software Companies can reduce time to market, eliminate guesswork, and build a strong integration that scales as customer adoption grows. Get started with the Marketplace fulfullment API resource collection
