Accelerate your AI or agent build to sell on Marketplace with Quick-Start Development Toolkit
Want to skip right to coding in minutes? Start with the interactive wizard in App Advisor Building AI products quickly is becoming table stakes. Building them in a way that supports scalability, repeatability, and a path to commercialization is where software companies create advantage. The challenge now is reducing the time between identifying an opportunity and getting developers working inside a proven structure that supports real deployment outcomes. That’s where the AI, agentic, and Copilot branch of the Quick-Start Development Toolkit helps. Embedded directly within App Advisor, Quick-Start Development Toolkit helps software companies move from concept to implementation faster using guided development patterns, trusted architectures, deployable reference code, and practical resources designed to reduce friction across the development process. Build AI & agentic products faster without starting from scratch Development teams often know the customer scenario they want to solve. What slows momentum is deciding where to begin, selecting architecture patterns, and aligning implementation decisions across teams. The Quick-Start Development Toolkit helps remove that uncertainty. By answering a few focused questions about what you want to build, who it serves, and the products you’re building with, you’re matched with a development pattern designed to accelerate execution. Each development pattern includes: Self-serve, click-to-deploy reference code aligned to your scenario, Sample solution architecture to help visualize products and reduce guesswork, and Practical how-to resources and implementation guidance to overcome friction points, Everything is structured to support faster decision making and help teams move confidently into development. Accelerate development with purpose-built AI accelerators The AI and agent branch of Quick-Start Development Toolkit includes development accelerators designed around high-value scenarios, so your team can spend less time assembling foundations and more time building differentiated experiences. Each of these accelerators is built and fully maintained by Microsoft experts, so you can be confident your code template isn’t stale. Our most popular accelerators include: Multi-Agent Custom Automation Engine Accelerator: Delegate complex, repetitive tasks to AI agents that act on your behalf—executing work efficiently, reducing manual effort, and ensuring results align with your organization's standards. Conversation Knowledge Mining Accelerator: Improve contact center performance with AI-powered conversation intelligence—analyzing audio and text data on a large scale to show insights, improve service, and drive smarter decisions. Accelerate agentic applications for Unified Data Foundations (with Microsoft Fabric): Accelerate decision making at scale with secure, agentic AI built on a unified data foundation with two use cases for sales performance and customer insights. Each pattern includes common use cases, related resources, and pathways to adjacent scenarios so teams can continue progressing without losing momentum. The goal is to help your team move from experimentation to a product that can be packaged, deployed, and prepared for customers. You can see more of our accelerators here Coming this week: The Microsoft IQ solution accelerator leverages a shared intelligence layer to unify data, knowledge, and workflows, enabling AI-powered insights and coordinated actions for measurable business outcomes. Build with Microsoft Marketplace outcomes in mind Development choices shape commercial outcomes. Starting with trusted architecture and structured implementation guidance can help reduce redesign cycles later when preparing to package, publish, and scale. Quick-Start Development Toolkit helps software companies: Shorten time from idea to deployable AI product, Improve alignment across implementation decisions, Reduce development overhead through reusable foundations, and Create repeatable pathways toward publishing and selling. When development starts with clarity, commercialization becomes easier. Keep moving forward with App Advisor Quick-Start Development Toolkit is embedded within App Advisor because building is only one stage of the journey. App Advisor helps connect decisions across design, development, publishing, and growth so teams can continue moving forward with less context switching and more confidence. As your solution evolves, App Advisor provides curated, step-by-step guidance to help you prepare for Marketplace readiness and make the next decision faster. Ready to start? Explore Quick-Start Development Toolkit Start where you need help with App AdvisorAI 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 SuccessMove 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.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 Azure
Accelerating 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 collectionDiscover why to build AI apps and agents with Microsoft and sell through Marketplace
Customer demand for AI is accelerating fast. And your company's AI app or agent should be there to meet it. In this fiscal year, Microsoft has already seen 2x growth in customers purchasing AI products through Microsoft Marketplace, while also being the largest catalog of AI apps and agents in the industry. Building AI apps and agents isn’t just about model performance or speed to market. It’s about meeting your customers’ needs when they have them. For software companies, success depends on whether your AI solution is secure, compliant, responsibly designed, and ready to scale in real-world work environments. That’s why App Advisor starts by showing the many reasons why building with Microsoft is the right foundation for AI apps and agents. Why building AI apps and agents with Microsoft is different Microsoft, named an AI Leader by Gartner, brings together AI innovation, Responsible AI, and enterprise-grade security into a single, integrated platform. This matters when you’re quickly building AI-powered experiences and agents that your customers can trust. When you build with Microsoft, you’re building on an AI-native platform designed for production use: Industry-leading AI and agentic capabilities supporting Gen AI, RAG, ML, predictive analytics, and multi-modal agent workflows, Integrated developer tools to help teams ship faster that you already use and trust (like GitHub Copilot, Visual Studio, and Microsoft Foundry), Seamless integration across the Microsoft stack to make it easier to connect data, services, and user experiences without stitching different systems together. This foundation helps you focus on what you’re building. Microsoft handles the complexity behind the scenes. Build confidently from day one, stay up to date with AI best practices Building with AI doesn't have to be risky. Data access, model behavior, governance, and compliance all matter more when AI and agents are embedded directly into customer workflows. Microsoft approaches this with end-to-end security and Responsible AI practices that are integrated throughout the development lifecycle. That's why App Advisor and Microsoft keep you up with the speed of designing with AI: Principles to design your own AI Center of Excellence, Sessions focused on the future of AI and agents in the AI Tour, Resources and webinars, like the AI envisioning sessions, to keep you current. This is especially critical for software companies selling into regulated or security-conscious industries. Security isn't an afterthought. You’re building on a platform where they’re already part of the system. How App Advisor can help answer questions about building AI apps and agents The first step in App Advisor is intentionally focused on clarity. Instead of jumping straight into tooling or publishing requirements, it helps you evaluate: Why Microsoft is the right platform for AI apps and agents, How building with Microsoft assists in development, scaling, and customer trust, What kinds of opportunities exist in the Microsoft Marketplace and how to maximize on them. However, App Advisor doesn’t stop at discovery or development. The same experience that helps you build AI apps and agents also supports growth through the Microsoft Marketplace—giving you access to global customers, streamlined procurement, and enterprise-ready distribution. From first line of code to go-to-market readiness, the platform is designed to support sustainable, scalable growth with confidence. Ready to build your AI app or agent? When you start with the right foundation, everything that follows moves faster—and with less risk. Start with the fundamentals: realize the potential of building with Microsoft with curated guidance in App Advisor We look forward to seeing your AI app or agent on Microsoft Marketplace!Migrating your AWS offer to Microsoft Marketplace - AWS to Azure service comparisons
As an Independent Software Vendor (ISV), expanding your Marketplace offer's reach beyond AWS Marketplace by replicating to Microsoft Marketplace offers exciting opportunities to grow your customer base. With millions of customers across a global network of businesses and industries, Azure presents a thriving platform to enhance your app’s visibility and functionality. This post is part of a series on replicating apps from AWS to Azure. View all posts in this series. Boost your growth and access more customers by replicating your AWS app to Azure and selling through Microsoft Marketplace. This guide will compare commonly used AWS and Azure components, highlighting differences, to help you replicate your app quickly and easily to prepare it for publishing on Microsoft Marketplace. Future posts will dive deeper into each component area. To ensure a seamless app replication, start by reviewing the marketplace listing requirements. Understanding the key differences between AWS and Azure will help you transition and optimize performance on Azure while benefiting from its unique advantages. This guide will outline these differences, highlight similar services, and offer steps for a seamless replication or migration. 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 Marketplace. The benefits of replicating or migrating to Microsoft Marketplace Migrating to Marketplace unlocks a wealth of opportunities for ISVs. The Azure ecosystem offers several advantages, including: Global reach: Azure’s vast global network of data centers ensures high availability and low-latency access to your application for customers worldwide. Cost efficiency: Azure’s flexible pricing models and cost management tools allow ISVs to optimize their cloud spending. Scalability: With Azure’s powerful compute and storage options, you can scale your application effortlessly to accommodate growing demand. Security and compliance: Azure’s comprehensive security tools and certifications help you meet industry-specific compliance standards, ensuring that your application is secure and trusted. Meet where your customers are: Deploy into customer subscriptions, making your solution more integrated to customer workload. AWS vs. Azure AWS and Azure are the top cloud platforms with diverse services for developers and businesses. Below, we will highlight key areas where AWS and Azure differ—and how to leverage Azure services—when moving your Marketplace offer from AWS to Microsoft Marketplace. Microsoft Marketplace capabilities In Azure, ISVs can leverage metered billing to charge customers based on actual usage, similar to AWS's pay-as-you-go model. This flexible pricing model is ideal for SaaS solutions. Partner Center offers tools for setting pricing models, tracking usage, and adjusting billing. It also provides anomaly detection to help partners identify unexpected usage and ensure transparent billing. When creating SaaS offers in Marketplace, ISVs can define plans with various pricing strategies, such as usage-based or flat-rate billing. These plans, or SKUs, can be customized through free trials, BYOL (Bring Your Own License), or vCPU-based pricing for virtual machines. Both Azure and AWS allow flexible, metered billing based on usage. Azure also provides the ability to set customer discounts or negotiated pricing. Using Partner Center, you can configure and manage these offerings, providing flexibility for customers and partners to scale as needed. Like AWS Control Tower, Azure Lighthouse enables service providers to manage multiple customer Azure environments securely and at scale, offering enhanced visibility, control, and automation. For usage-based monthly billing, you can choose from predefined or custom pricing options (using metered billing APIs). Predefined options like per core, per node, or per pod let Microsoft bill customers based on hourly usage, billing them monthly. Learn more about usage-based pricing here: Setting Plan Pricing. Mapping AWS services to Azure services Your Marketplace offer may use multiple AWS services, and you can build the same offer using Azure services. However, this requires careful mapping to ensure your application functions seamlessly in the Azure environment. Here’s a quick overview of how popular AWS services map to Azure:: Networking: AWS VPC → Azure Virtual Networks (VNets) Compute Services: AWS EC2 → Azure Virtual Machines (VMs), Azure App Services (for web apps) Storage: Amazon S3 → Azure Blob Storage, Azure Data Lake Storage (for big data) Identity Management: AWS IAM → Entra ID Containers: EKS and Elastic Beanstalk → AKS and Azure App Services Serverless: AWS Lambda → Azure Functions Databases: Amazon RDS → Azure SQL Database, Azure Cosmos DB (for NoSQL) Azure for AWS professionals provides you with a more comprehensive mapping of different services. Let's take a deeper look into each of these areas. Cloud architecture and networking One of the primary differences between AWS and Azure lies in their cloud architecture and networking models. AWS uses Virtual Private Clouds (VPCs) to create isolated networks, while Azure employs Virtual Networks (VNets). Both services perform similar functions, but they have different terminologies and setups. For instance, in Azure, you'll be working with VNet Peering, Network Security Groups (NSGs), and Azure VPNs for secure networking. The goal is to map your AWS VPC setup to Azure VNets with ease. AWS needs a Nat Gateway for egress access whereas Azure does not need a Nat Gateway for default egress. AWS Subnets are pinned to Availability Zones (AZs) whereas Azure Subnets span across the AZs. Compute services: EC2 vs. Virtual Machines (VMs) AWS EC2 instances are one of the most widely used compute services, allowing you to run applications on virtual servers. In Azure, the equivalent service is Azure Virtual Machines (VMs). While both offer scalable compute resources, the key differences are in the range of VM sizes, configurations, and the management interface. When migrating from AWS EC2 to Azure VMs, it's important to assess the appropriate Azure VM sizes and configurations that match the performance of your EC2 instances. Additionally, Azure VMs support Azure Resource Manager (ARM) templates, which provide more automation for resource management. For those who have utilized EC2's Auto Scaling feature, Azure provides similar functionality through Azure Scale Sets. Storage: S3 vs. Blob Storage For object storage, AWS uses Amazon S3, while Azure uses Azure Blob Storage. Both services serve the same purpose — storing large amounts of unstructured data — but the underlying configurations, security features, and cost structures differ. While migrating from S3 to Blob Storage, it’s important to review your storage needs and adjust your application accordingly. Azure Blob Storage offers Cool and Archive tiers, which can be a great way to optimize storage costs for infrequently accessed data, and Azure's data redundancy options ensure high availability and durability. The Azure Storage Explorer tool also makes it easier for ISVs to manage their data after migration. Identity and Access Management (IAM) & billing: IAM vs. Entra ID IAM services on AWS and Azure differ in how they manage roles and permissions. AWS uses IAM for users, roles, and policies, while Azure uses Entra ID for IAM across cloud services. AWS organizes accounts through AWS Organizations, with IAM used for role-based access control (RBAC) and policies for service access. Azure’s structure involves Subscriptions and Management Groups, with Entra ID managing identity and access. Azure uses RBAC to assign roles at various levels (Subscription, Resource Group, Resource) and Azure Policies for governance and compliance. Azure Entra ID integrates with Microsoft services, like Office 365, SharePoint, and Teams, supporting identity federation, multi-factor authentication, and RBAC for granular permissions. It enhances governance and security across platforms. Azure handles billing management via subscriptions providing access to resources and can be reassigned to new owners. It offers three classic subscription administrator roles for resource access and management for billing and resource access. Container management: Elastic Beanstalk vs. Azure App Services and EKS vs. AKS For containerized applications, AWS offers Elastic Beanstalk for easy application deployment and management. Azure’s equivalent services include Azure App Services for simple web application hosting and Azure Kubernetes Service (AKS) for container orchestration. While Azure App Services is more suitable for traditional web applications, AKS provides a robust and scalable solution for microservices and containerized applications, similar to AWS’s Elastic Kubernetes Service (EKS). ISVs who are accustomed to Elastic Beanstalk for deploying containerized applications will find Azure App Services or AKS a seamless alternative, with Azure offering rich integrations with DevOps pipelines, CI/CD workflows, and container registries. Serverless: AWS Lambda vs. Azure Functions Both AWS and Azure support serverless computing, which allows developers to run code without managing servers. AWS offers Lambda, while Azure offers Azure Functions. Both services allow you to trigger code in response to events, such as file uploads or API calls. The key difference is that Azure Functions integrates deeply with other Azure services, such as Azure Logic Apps and Azure Event Grid. If your application leverages AWS Lambda, you will find that Azure Functions can serve as an excellent equivalent. Azure also provides Durable Functions, which extend Azure Functions for stateful workflows. Migrating from AWS Lambda to Azure Functions typically requires mapping your event-driven functions and configuring their triggers in the Azure ecosystem. Databases: RDS vs. Azure SQL and Cosmos DB When it comes to databases, AWS offers Amazon RDS for relational databases, and Amazon DynamoDB for NoSQL. Azure provides several alternatives, including Azure SQL Database for relational storage and Azure Cosmos DB for NoSQL storage. Both platforms support database scalability, automated backups, and high availability. If you are using Amazon RDS with services like MySQL or PostgreSQL, you can migrate to Azure Database for MySQL or Azure Database for PostgreSQL. Similarly, if you are using AWS DynamoDB, Azure’s Cosmos DB offers a global, scalable NoSQL database with low-latency access. Messaging: AWS SQS vs. Azure Service Bus Messaging services are crucial when your application handles high-throughput, asynchronous communication between different components. AWS offers Simple Queue Service (SQS) for messaging and SNS for pub/sub notifications while Azure offers Azure Service Bus and Azure Event Grid. Azure Service Bus provides similar functionality to SQS but offers additional capabilities like advanced message routing, dead-lettering, and sessions for handling ordered messages. If your application relies on a queuing mechanism for inter-service communication, you’ll want to map AWS SQS to Azure Service Bus. For event-driven architectures, Azure Event Grid can connect different services and trigger actions across Azure services. Security: Protecting your application on Azure When migrating from AWS to Azure, security is paramount. Both platforms offer strong frameworks to protect data, apps, and infrastructure. Azure provides a suite of integrated security services to maintain high security while enabling cloud scalability. AWS offers AWS Shield and WAF for DDoS and web application firewalls, while Azure offers Azure DDoS Protection and Azure Firewall for similar threat prevention. Azure Security Center monitors your security posture, and Azure Sentinel provides cloud-native SIEM (Security Information and Event Management) for threat detection and response. Microsoft Defender for Identity and Azure Entra ID Identity Protection integrate with Entra ID, ensuring your app security is tightly linked to user identity and governance. Compliance: Meeting regulatory standards on Azure Ensuring compliance with industry standards and regulations is crucial for many ISVs. Azure provides a robust compliance framework that aligns with global standards to meet the most stringent requirements. Whether your application deals with sensitive data or operates in highly regulated industries, Azure’s comprehensive compliance offerings can help you achieve the necessary certifications. Azure complies with key standards such as: GDPR HIPAA SOC 1, 2, and 3 ISO 27001 and other ISO standards FedRAMP Azure provides tools like Azure Policy for governance and Azure Blueprints for complex regulatory requirements. It offers a similar set of compliance certifications to AWS, with a stronger integration into Microsoft enterprise tools, easing compliance for businesses in regulated sectors. For apps handling sensitive data, use Azure Security and Compliance Blueprint to ensure regulatory adherence. Azure’s Compliance Manager helps track and manage compliance, simplifying the process of meeting industry standards. Key resources SaaS Workloads - Microsoft Azure Well-Architected Framework | Microsoft Learn Metered billing for SaaS offers in Partner Center Create plans for a SaaS offer in Azure Marketplace Metered billing with Azure Managed Applications Set plan pricing and availability for an Azure Container offer in Microsoft commercial marketplace - Marketplace publisher Configure pricing and availability for a virtual machine offer in Partner Center - Marketplace publisher Overview - CSP marketplace - Partner Center Azure for AWS professionals - Azure Architecture Center Azure networking documentation Microsoft Entra ID documentation - Microsoft Entra ID Azure security documentation Azure compliance documentation Azure Storage Documentation Hub Microsoft Azure container services documentation Azure serverless - Azure Logic Apps Migration examples Get over $126K USD in benefits and technical consultations to help you replicate and publish your app with ISV Success Maximize your momentum with step-by-step guidance to publish and grow your app with App AdvisorReplicating your AWS application to Azure: key resources for software development companies
Azure offers a broad global footprint, strong security and compliance foundations, flexible cost options, and the ability to deploy your solution directly into a customer’s subscription for tighter integration with their environment. While Microsoft Marketplace expands your reach instantly by connecting your solution to millions of customers across Microsoft’s global ecosystem. It also provides deeper integration with Azure services and a unified experience that makes it easier for organizations to discover, purchase, and deploy your app. You can scale with channel-led sales by extending your reach through an ecosystem of 500K+ partners through a variety of sales models. With ISV Success, you can also accelerate replication with cloud credits, AI services, and hands on technical guidance. Understanding how AWS and Azure services align — across networking, storage, identity, regions, and marketplace requirements — helps ensure a smooth replication process. This post highlights key resources that compare AWS and Azure components, outline migration considerations, and guide you through preparing an Azure‑ready version of your application. Essential guides for AWS‑to‑Azure replication To get started, here is a curated set of resources that cover architecture differences, identity, security, networking, regions, and marketplace publishing — all designed to help you build an Azure‑ready version of your existing AWS application. App replication foundations Advantages of replicating your app from AWS to Azure Guide to replicating your app from AWS to Azure Quick‑start toolkit for AWS‑to‑Azure replication Architecture & service mapping AWS to Azure service comparisons Storage migration paths AWS‑to‑Azure network design Region selection for AWS developers Identity & Security Identity and Access Management AWS‑to‑Azure security model comparison Marketplace Enablement Publishing and selling through Marketplace Step-by-step curated guidance through App Advisor These resources provide a complete starting point for understanding how to replicate your AWS‑based application to Azure, from comparing services and configuring infrastructure to preparing your Marketplace listing and extending your multi-cloud reach. Want more? Start coding in minutes with code templates, solution architecture, and how-to articles to start coding in minutes? Visit the AWS to Azure replication code library in the Quick-Start Development Toolkit.
