Why to include Azure in your multi-cloud strategy
For software companies building on AWS, adding Azure isn’t just a technical decision—it’s a growth strategy. In this Marketplace blog, learn how replicating your solution to Azure can unlock access to Microsoft’s global seller network, enterprise customers, and commercial marketplace incentives that can reduce procurement friction and accelerate deal velocity. The article breaks down the business case, the role of Marketplace and co-sell, and the financial incentives available to partners—plus practical guidance on when and how to get started. Read the full article to understand why expanding to Azure is increasingly a go-to-market decision, not just an infrastructure one. Replicating solutions to Azure: The business case, the incentives, and how to get there fast | Microsoft Community Hub Learn more and join the April 2nd webinar for live Q&A Why Azure belongs in your multicloud strategy - Microsoft Marketplace CommunityReplicating solutions to Azure: The business case, the incentives, and how to get there fast
Johan Aussenac is CEO at WeTransact a Microsoft Certified Software company specializing in Microsoft Marketplace listing, co-sell activation, and cloud GTM strategy for software companies. ________________________________________________________________________________________________________________________________________________________________ When should Azure be part of your cloud strategy and what does Microsoft offer to help you get there? Most software development companies building on AWS did so for the right reasons. AWS is mature, well-documented, and has been the default for cloud-native companies for over a decade. This article is not an argument for abandoning that. It is an argument for asking a more strategic question: at what point does adding Azure to your infrastructure also open a fundamentally different commercial channel and what does it cost to get there? For a growing number of software companies the answer is clear. Azure is not just an alternative cloud. It is an entry point into Microsoft's commercial ecosystem: its seller network, its enterprise relationships, and a marketplace transacting billions of dollars of software annually. Understanding when and how to replicate your solution to Azure and add it to your strategy is increasingly a GTM decision, not just an infrastructure one. The business case: Why Azure belongs in a multi-cloud strategy Microsoft as a distribution channel When a software company lists in Microsoft Marketplace and enrolls in co-sell, Microsoft's own field sellers more than 25,000 globally, are incentivized to include that software company’s product in their customer conversations. This is not passive discoverability. It is an active sales motion, driven by a specific commercial mechanism. The mechanism is the Microsoft Azure Consumption Commitment (MACC). Large enterprises increasingly sign pre-committed cloud spend agreements with Microsoft. Software transacted through Microsoft Marketplace counts toward these commitments, which means enterprise procurement teams actively prefer Marketplace-listed solutions they help burn down an existing budget obligation. For software companies, this translates to reduced procurement friction and shorter sales cycles inside accounts that already have a Microsoft relationship. By comparison, neither AWS nor Google Cloud offers this at an equivalent scale. Microsoft's footprint spanning Office 365, Teams, Dynamics 365, Azure, and now Copilot touches more business units and more decision-makers across an enterprise than any other vendor. Adding Azure to your stack means plugging into that network. Technical differentiators worth knowing Beyond the commercial case, Azure offers capabilities that are genuinely differentiated for certain software company profiles: Azure OpenAI Service. Microsoft holds an exclusive enterprise partnership with OpenAI. For software companies that need GPT-4o or o1 with private endpoints, data residency, and enterprise compliance certifications, this is only available on Azure. Microsoft 365 and Copilot extensibility. Software companies can embed products directly into Teams, Outlook, and Word via Copilot plugins and agents, which is a distribution surface with no direct equivalent on other clouds. Microsoft Entra ID. Most enterprise identity infrastructure runs on Entra ID (formerly Active Directory). Native SSO and RBAC integration is cleaner when you build on Azure. The .NET and Windows ecosystem. For teams and customers already in the Microsoft developer stack, Azure is simply where the tooling is best optimized. Microsoft's funding and incentives for software companies One of the most underutilized advantages of moving to Azure is the range of Microsoft programs designed to offset the cost and complexity of doing so. Both of the following are free to join and available to most software companies. Microsoft for Startups (Founders Hub). Provides up to $150,000 in Azure credits in the first year, plus access to technical advisory, developer tooling, and go-to-market support. Enroll before you begin any replication. These credits cover compute and storage costs during your build and test phases. ISV Success. Microsoft's program for software companies building on Azure. It includes technical architecture guidance, co-sell readiness support, and dedicated Microsoft contacts. ISV Success enrolment is also the prerequisite for co-sell eligibility, the commercial mechanism that unlocks Microsoft's seller network on your behalf. Both programs include access to Microsoft technical advisors at no charge. This is worth emphasizing: before spending on a replication partner or committing engineering time, software companies can get a scoped assessment of their replication from Microsoft itself, tailored to their specific stack and target architecture. How to get there: Partner-led or self-service There are two realistic paths to adding Azure. The right one depends on your engineering bandwidth, your timeline, and how much of the replication you want to own internally. The partner-led path (recommended for most software companies) For founders and CTOs, the real cost of replication is not tooling, it is engineering time diverted from product. Every sprint spent on infrastructure is a sprint not spent on customer value. A partner-led approach solves this directly. Enroll in a Microsoft program. Start with Microsoft for Startups or ISV Success (or both). This secures your credits, establishes your Microsoft relationship, and is the prerequisite for co-sell access. It is free and should be done before anything else. Book a free technical consultation. Use the technical advisory included in your program to scope your replication with Microsoft. Explain your stack, your target architecture, and your timeline. This session produces a documented brief which becomes your handoff document for step three. Engage a specialist partner. Take that brief to an Azure Expert MSP, Microsoft's highest-tier replication partners, certified for complex replications and incentivized by Microsoft to keep costs manageable, often including access to replication credits that offset engagement fees. Alternatively, Microsoft Certified Software companies (such as WeTransact) can handle both the replication and the parallel Marketplace listing and co-sell activation, so you arrive on Azure already set up to sell, not just to run. The self-service path For software companies with available engineering capacity and simpler workloads, a self-directed replication is viable. The tooling has improved significantly. The key tools are: Azure Migrate Microsoft's free hub for discovery, assessment, and replication. Maps AWS services to Azure equivalents, flags compatibility issues, and estimates costs. Start here for any self-service replication. Azure Storage Mover Built specifically for moving data from AWS S3 to Azure Blob Storage. Supports parallel transfers, preserves file metadata, and integrates with Azure Monitor for progress tracking. Azure Database Migration Service (DMS) Migrates SQL Server, MySQL, PostgreSQL, MongoDB and others from AWS RDS or on-premises to Azure managed database services. AWS DataSync Useful for transferring large datasets between AWS and Azure storage during a phased replication. Azure Data Factory For complex ETL workloads: extracting, transforming, and loading data across clouds with scheduling and conflict resolution. Azure Well-Architected Framework Run this assessment against your current architecture before replicating it. It evaluates reliability, security, cost, performance, and operations. The goal is to land in a better architectural state, not simply replicate what existed on AWS. Whichever path you take, one step is non-negotiable: establishing a proper Azure landing zone before any workload moves. This means setting up your subscription structure, networking, identity, governance, and monitoring upfront. Microsoft publishes a software company-specific landing zone guide and a portal-based accelerator (no infrastructure-as-code expertise required) to make this straightforward. Skipping creates security and compliance debt that is significantly harder to fix retroactively. The bottom line Adding Azure to your cloud strategy is not primarily an infrastructure decision. It is a go-to-market decision. The question is whether your company benefits from access to Microsoft's seller network, its enterprise customer base, and Marketplace mechanics that reduce procurement friction for your buyers. For many software companies, the answer is yes and Microsoft's programs make the cost of getting there lower than most assume. The partner ecosystem exists to take the technical burden off your engineering team. The self-service tools are capable enough for simpler replications. The commercial opportunity on the other side, co-sell, MACC alignment, Marketplace distribution is real and growing. To learn more join us on April 2, 2026, at 8:30 AM PDT for Why Azure belongs in your multicloud strategy - Microsoft Marketplace Community and live Q&A. If you miss the session, you will be able to watch it on demand through the same link. Where to start → Microsoft for Startups → ISV Success → Azure Expert MSP directory: partner.microsoft.com → Software company-specific Azure landing zone guide: Independent software vendor (ISV) considerations for Azure landing zones - Cloud Adoption Framework | Microsoft Learn This article was produced in partnership with WeTransact, a Microsoft Certified Software company specializing in Microsoft Marketplace listing, co-sell activation, and cloud GTM strategy for software companies.
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.
Moving from Private Plans to Private Offers — Should We Make the Switch?
Hi Azure Marketplace community, We, at https://marketplace.microsoft.com/en-us/product/saturaminc.qualdo_drx are currently using private plans to handle custom pricing for specific customers, and we're evaluating whether it makes sense to transition to private offers. Would love to hear from others who've made this move — or who've deliberately stayed on private plans. Here's where we're at: private plans have served us well for restricting visibility and offering tiered pricing to select tenants, but as our deal complexity has grown (more enterprise customers, negotiated terms, channel partners), we're starting to feel some of the limitations. A few things pushing us toward private offers: Custom pricing flexibility — Private offers let us set percentage discounts or absolute prices per customer without creating a new plan for every deal. As our customer base grows, managing individual plans is getting unwieldy. Multi-party / channel support — We work with some resellers and CSPs. Private offers seem to support that flow much better with multi-party private offers (MPPO). Are there scenarios where private plans are still the better choice over private offers? How are you handling the coexistence of both during a transition period? Any impact on reporting, billing, or reconciliation we should be aware of? We want to make sure we're not solving one problem and creating another. Appreciate any real-world experiences!. Thanks in Advance, Kavitha SrinivasanAI 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 SuccessAdding Azure into your multicloud strategy
Building a multicloud strategy? Learn why Azure plays a critical role in accelerating Marketplace success. Join WeTransact at the April 2nd Microsoft Marketplace webinar to explore how aligning Azure with your multicloud approach can help you increase deal size, unlock co-sell opportunities, and move faster using Microsoft tools, funding, and incentives. You’ll gain practical guidance on replication, modernization, and next steps to drive long-term Marketplace momentum. Learn more and attend: Why Azure belongs in your multicloud strategy - Microsoft Marketplace CommunityPartner Case Study | DeepJudge
Legal work depends on precision, precedent, and the ability to apply institutional knowledge across diverse matters. For many firms, that knowledge is documented but not always easy to access or act on. DeepJudge, a Microsoft partner, is helping legal teams bridge that gap with AI-powered search and workflow tools built on Microsoft Azure. DeepJudge specializes in enterprise search and agentic AI workflows tailored for legal professionals. DeepJudge participated in Microsoft for Startups and the Pegasus Program, a selective initiative that helps high-potential partners scale through technical guidance, go-to-market support, and early access to Microsoft innovations. Today, the company’s platform is built on Microsoft Azure—including Azure OpenAI Services, Azure Kubernetes Service, and Microsoft Defender for Cloud—to promote high performance and robust data protection. Making internal knowledge accessible—and secure CMS Switzerland, a full-service law firm with more than 160 employees and a legacy spanning over 80 years, offers tailored legal solutions for businesses, investors, and private individuals. Known for its deep legal expertise and cross-border capabilities, CMS Switzerland built a strong foundation of internal knowledge—contracts, case files, templates, presentations, and precedent documents—stored across various systems and folders. While this information was historically well maintained, it wasn’t always easy for lawyers to locate and apply it consistently across cases. “Clients hire law firms for their expertise—but law firms often underestimate their breadth and depth of existing knowledge and experience,” said Stefan Brunnschweiler, Managing Partner at CMS Switzerland. The firm wanted to better surface and apply its internal expertise across teams—without disrupting existing workflows or compromising on data protection. The goal was to institutionalize internal know-how so that all employees could access and apply it confidently in their daily work. Security was a critical consideration. As part of CMS, one of the largest international law firms with over 7,200 lawyers in 92 offices across 50 countries, CMS Switzerland needed a solution that could meet strict data protection requirements while offering the flexibility and performance of modern AI tools. “Data security is our top priority,” Brunnschweiler emphasized. “The fact that Microsoft hosts our data in Switzerland and that DeepJudge, through Microsoft, also ensures high data protection convinced us.” CMS Switzerland began exploring options that could help surface internal knowledge more efficiently, reduce time spent on manual research, and support faster onboarding of new employees. The firm was looking for a solution that could meet the highest standards for security, reliability, and usability—while also aligning with the operational realities of legal work. Continue reading here Explore all case studies or submit your own Subscribe to case studies tag to follow all new case study posts. Don't forget to follow this blog to receive email notifications of new stories!
