Securing AI apps and agents on Microsoft Marketplace
Why security must be designed in—not validated later AI apps and agents expand the security surface beyond that of traditional applications. Prompt inputs, agent reasoning, tool execution, and downstream integrations introduce opportunities for misuse or unintended behavior when security assumptions are implicit. These risks surface quickly in production environments where AI systems interact with real users and data. Deferring security decisions until late in the lifecycle often exposes architectural limitations that restrict where controls can be enforced. Retrofitting security after deployment is costly and can force tradeoffs that affect reliability, performance, or customer trust. Designing security early establishes clear boundaries, enables consistent enforcement, and reduces friction during Marketplace review, onboarding, and long‑term operation. In the Marketplace context, security is a foundational requirement for trust and scale. This post is part of a series on building and publishing well-architected AI apps and Agents on Microsoft Marketplace. How AI apps and agents expand the attack surface Without a clear view of where trust boundaries exist and how behavior propagates across systems, security controls risk being applied too narrowly or too late. AI apps and agents introduce security risks that extend beyond those of traditional applications. AI systems accept open‑ended prompts, reason dynamically, and often act autonomously across systems and data sources. These interaction patterns expand the attack surface in several important ways: New trust boundaries introduced by prompts and inputs, where unstructured user input can influence reasoning and downstream actions Autonomous behavior, which increases the blast radius when authentication or authorization gaps exist Tool and integration execution, where agents interact with external APIs, plugins, and services across security domains Dynamic model responses, which can unintentionally expose sensitive data or amplify errors if guardrails are incomplete Each API, plugin, or external dependency becomes a security choke point where identity validation, audit logging, and data handling must be enforced consistently—especially when AI systems span tenants, subscriptions, or ownership boundaries. Using OWASP GenAI Top 10 as a threat lens The OWASP GenAI Top 10 provides a practical, industry‑recognized lens for identifying and categorizing AI‑specific security threats that extend beyond traditional application risks. Rather than serving as a checklist, the OWASP GenAI Top 10 helps teams ask the right questions early in the design process. It highlights where assumptions about trust, input handling, autonomy, and data access can break down in AI‑driven systems—often in ways that are difficult to detect after deployment. Common risk categories highlighted by OWASP include: Prompt injection and manipulation, where malicious input influences agent behavior or downstream actions Sensitive data exposure, including leakage through prompts, responses, logs, or tool outputs Excessive agency, where agents are granted broader permissions or action scope than intended Insecure integrations, where tools, plugins, or external systems become unintended attack paths Highly regulated industries, sensitive data domains, or mission‑critical workloads may require additional risk assessment and security considerations that extend beyond the OWASP categories. The OWASP GenAI Top 10 allows teams to connect high‑level risks to architectural decisions by creating a shared vocabulary that sets the foundation for designing guardrails that are enforceable both at design time and at runtime. Designing security guardrails into the architecture Security guardrails must be designed into the architecture, shaping where and how policies are enforced, evaluated, and monitored throughout the solution lifecycle. Guardrails operate at two complementary layers: Design time, where architectural decisions determine what is possible, permitted, or blocked by default Runtime, where controls actively govern behavior as the AI app or agent interacts with users, data, and systems When architectural boundaries are not defined early, teams often discover that critical controls—such as input validation, authorization checks, or action constraints—cannot be applied consistently without redesign: Tenancy boundaries, defining how isolation is enforced between customers, environments, or subscriptions Identity boundaries, governing how users, agents, and services authenticate and what actions they can perform Environment separation, limiting the blast radius of experimentation, updates, or failures Control planes, where configuration, policy, and behavior can be adjusted without redeploying core logic Data planes, controlling how data is accessed, processed, and moved across trust boundaries Designing security guardrails into the architecture transforms security from reactive to preventative, while also reducing friction later in the Marketplace journey. Clear enforcement boundaries simplify review, clarify risk ownership, and enable AI apps and agents to evolve safely as capabilities and integrations expand. Identity as a security boundary for AI apps and agents Identity defines who can access the system, what actions can be taken, and which resources an AI app or agent is permitted to interact with across tenants, subscriptions, and environments. Agents often act on behalf of users, invoke tools, and access downstream systems autonomously. Without clear identity boundaries, these actions can unintentionally bypass least‑privilege controls or expand access beyond what users or customers expect. Strong identity design shapes security in several key ways: Authentication and authorization, determines how users, agents, and services establish trust and what operations they are allowed to perform Delegated access, constraints agents to act with permissions tied to user intent and context Service‑to‑service trust, ensures that all interactions between components are explicitly authenticated and authorized Auditability, traces actions taken by agents back to identities, roles, and decisions A zero-trust approach is essential in this context. Every request—whether initiated by a user, an agent, or a backend service—should be treated as untrusted until proven otherwise. Identity becomes the primary control plane for enforcing least privilege, limiting blast radius, and reducing downstream integration risk. This foundation not only improves security posture, but also supports compliance, simplifies Marketplace review, and enables AI apps and agents to scale safely as integrations and capabilities evolve. Protecting data across boundaries Data may reside in customer‑owned tenants, subscriptions, or external systems, while the AI app or agent runs in a publisher‑managed environment or a separate customer environment. Protecting data across boundaries requires teams to reason about more than storage location. Several factors shape the security posture: Data ownership, including whether data is owned and controlled by the customer, the publisher, or a third party Boundary crossings, such as cross‑tenant, cross‑subscription, or cross‑environment access patterns Data sensitivity, particularly for regulated, proprietary, or personally identifiable information Access duration and scope, ensuring data access is limited to the minimum required context and time When these factors are implicit, AI systems can unintentionally broaden access through prompts, retrieval‑augmented generation, or agent‑initiated actions. This risk increases when agents autonomously select data sources or chain actions across multiple systems. To mitigate these risks, access patterns must be explicit, auditable, and revocable. Data access should be treated as a continuous security decision, evaluated on every interaction rather than trusted by default once a connection exists. This approach aligns with zero-trust principles, where no data access is implicitly trusted and every request is validated based on identity, context, and intent. Runtime protections and monitoring For AI apps and agents, security does not end at deployment. In customer environments, these systems interact continuously with users, data, and external services, making runtime visibility and control essential to a strong security posture. AI behavior is also dynamic: the same prompt, context, or integration can produce different outcomes over time as models, data sources, and agent logic evolve, so monitoring must extend beyond infrastructure health to include behavioral signals that indicate misuse, drift, or unintended actions. Effective runtime protections focus on five core capabilities: Vulnerability management, including regular scanning of the full solution to identify missing patches, insecure interfaces, and exposure points Observability, so agent decisions, actions, and outcomes can be traced and understood in production Behavioral monitoring, to detect abnormal patterns such as unexpected tool usage, unusual access paths, or excessive action frequency Containment and response, enabling rapid intervention when risky or unauthorized behavior is detected Forensics readiness, ensuring system-state replicability and chain-of-custody are retained to investigate what happened, why it happened, and what was impacted Monitoring that only tracks availability or performance is insufficient. Runtime signals must provide enough context to explain not just what happened, but why an AI app or agent behaved the way it did, and which identities, data sources, or integrations were involved. Equally important is integration with broader security event and incident management workflows. Runtime insights should flow into existing security operations so AI-related incidents can be triaged, investigated, and resolved alongside other enterprise security events—otherwise AI solutions risk becoming blind spots in a customer’s operating environment. Preparing for incidents and abuse scenarios No AI app or agent operates in a perfectly controlled environment. Once deployed, these systems are exposed to real users, unpredictable inputs, evolving data, and changing integrations. Preparing for incidents and abuse scenarios is therefore a core security requirement, not a contingency plan. AI apps and agents introduce unique incident patterns compared to traditional software. In addition to infrastructure failures, teams must be prepared for prompt abuse, unintended agent actions, data exposure, and misuse of delegated access. Because agents may act autonomously or continuously, incidents can propagate quickly if safeguards and response paths are unclear. Effective incident readiness starts with acknowledging that: Abuse is not always malicious, misuse can stem from ambiguous prompts, unexpected context, or misunderstood capabilities Agent autonomy may increase impact, especially when actions span multiple systems or data sources Security incidents may be behavioral, not just technical, requiring interpretation of intent and outcomes Preparing for these scenarios requires clearly defined response strategies that account for how AI systems behave in production. AI solutions should be designed to support pause, constrain, or revoke agent capabilities when risk is detected, and to do so without destabilizing the broader system or customer environment. Incident response must also align with customer expectations and regulatory obligations. Customers need confidence that AI‑related issues will be handled transparently, proportionately, and in accordance with applicable security and privacy standards. Clear boundaries around responsibility, communication, and remediation help preserve trust when issues arise. How security decisions shape Marketplace readiness From initial review to customer adoption and long‑term operation, security posture is a visible and consequential signal of readiness. AI apps and agents with clear boundaries—around identity, data access, autonomy, and runtime behavior—are easier to evaluate, onboard, and trust. When security assumptions are explicit, Marketplace review becomes more predictable, customer expectations are clearer, and operational risk is reduced. Ambiguous trust boundaries, implicit data access, or uncontrolled agent actions can introduce friction during review, delay onboarding, or undermine customer confidence after deployment. Marketplace‑ready security is therefore not about meeting a minimum bar. It is about enabling scale. Well-designed security allows AI apps and agents to integrate into enterprise environments, align with customer governance models, and evolve safely as capabilities expand. When security is treated as a first‑class architectural concern, it becomes an enabler rather than a blocker—supporting faster time to market, stronger customer trust, and sustainable growth through Microsoft Marketplace. What’s next in the journey Security for AI apps and agents is not a one‑time decision, but an ongoing design discipline that evolves as systems, data, and customer expectations change. By establishing clear boundaries, embedding guardrails into the architecture, and preparing for real‑world operation, publishers create a foundation that supports safe iteration, predictable behavior, and long‑term trust. This mindset enables AI apps and agents to scale confidently within enterprise environments while meeting the expectations of customers adopting solutions through 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 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 SuccessReshaping enterprise go-to-market with Microsoft Marketplace and ecosystem partnerships
As the pace of enterprise transformation accelerates, we’re seeing a fundamental shift in how organizations go to market—and it’s being powered by ecosystems, not silos. Partner1 recently hosted two industry events where we explored how Microsoft Marketplace is becoming a central engine for this change, helping partners unlock new routes to growth while making it easier for customers to discover, buy, and deploy innovative solutions. From AI-driven offerings to multiparty private offers and deeper channel integrations, Marketplace is redefining how partnerships come together to deliver end-to-end value. It’s not just about listing solutions—it’s about creating scalable, repeatable growth through a connected ecosystem that meets customers where and how they want to buy. If you’re thinking about how to evolve your go-to-market strategy, scale with partners, or tap into new revenue opportunities, this is a conversation you won’t want to miss. Read the full article to see how Marketplace and ecosystem partnerships are reshaping enterprise go-to-market—and what it means for your business. How Microsoft Marketplace and ecosystem partnerships are reshaping enterprise go-to-market | Microsoft Community HubAI 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 Success
No plans are available for the selected subscription
I've set up a SaaS offer in the Azure Marketplace and created a free plan for testing purposes. However, when attempting to subscribe to the offer, I encounter an issue: No plans are available for the selected subscription, despite configuring both yearly and monthly plans. Additionally, a paid plan has been configured. Please see the screenshot below for reference.Do you want to publish a transactable offer but are finding it difficult to do?
Many SaaS companies want to sell through Microsoft Marketplace. But surprisingly few actually launch transactable offers. Why? Over the last few years, Microsoft has heavily invested in its commercial marketplace. For ISVs and SaaS companies, the opportunity is clear: Access Microsoft's enterprise customers Co-sell with Microsoft sellers Shorten procurement cycles Unlock Azure consumption commitments But despite the upside, many companies still struggle to publish transactable offers. Not because they lack great products. Because marketplace readiness requires new operational muscle. From working with companies exploring the marketplace path, three challenges show up repeatedly. 1. Offer Architecture & Packaging Most companies start with a product but the marketplace requires a sellable offer structure. That means translating your product into: SaaS offers Managed apps Private plans Metered billing models Azure-backed services Questions teams often wrestle with: Should this be SaaS, VM, or a managed app? What pricing model works in marketplace billing? How should enterprise customers purchase it? Without clear packaging, the publishing process stalls quickly. 2. Technical & Operational Readiness Publishing an offer is not just a marketing step. It touches multiple teams: Engineering Product Finance Legal Marketplace operations Some of the most common blockers include: Marketplace APIs and SaaS fulfillment integration Metering implementation Identity and tenant provisioning Azure resource deployment templates Testing and certification For companies new to marketplace infrastructure, the learning curve can be steep. 3. Internal Alignment & Ownership One of the biggest challenges isn’t technical. It’s organizational. Marketplace initiatives often sit between multiple teams: Partnerships Product Revenue operations Cloud alliances Sales leadership Without a clear owner, progress slows. Successful marketplace companies usually have a dedicated marketplace strategy owner or partner GTM lead driving execution. Why This Matters Now Enterprise buyers increasingly prefer purchasing through marketplaces. Reasons include: Faster procurement Existing vendor relationships Budget alignment with cloud commitments Simpler contract management Which means companies that enable marketplace transactions often see: Faster deal cycles Larger enterprise deals More co-sell opportunities with Microsoft But getting there requires navigating the early friction. The Question for the Ecosystem If your company is exploring Microsoft Marketplace — or already trying to publish an offer: What has been your biggest challenge? 1️⃣ Offer packaging 2️⃣ Technical integration 3️⃣ Internal ownership / alignment 4️⃣ Something else? Drop your experience in the comments. The more companies share what’s blocking progress, the easier it becomes for the ecosystem to improve the process. Comment with your biggest blocker or lesson learned from publishing a marketplace offer.How do you actually unlock growth from Microsoft Teams Marketplace?
Hey folks 👋 Looking for some real-world advice from people who’ve been through this. Context: We’ve been listed as a Microsoft Teams app for several years now. The app is stable, actively used, and well-maintained - but for a long time, Teams Marketplace wasn’t a meaningful acquisition channel for us. Things changed a bit last year. We started seeing organic growth without running any dedicated campaigns, plus more mid-market and enterprise teams installing the app, running trials, and even using it in production. That was encouraging - but it also raised a bigger question. How do you actually systematize this and get real, repeatable benefits from the Teams Marketplace? I know there are Microsoft Partner programs, co-sell motions, marketplace benefits, etc. - but honestly, it’s been very hard to figure out: - where exactly to start - what applies to ISVs building Teams apps - how to apply correctly - and what actually moves the needle vs. what’s just “nice to have” On top of that, it’s unclear how (or if) you can interact directly with the Teams/Marketplace team. From our perspective, this should be a win-win: we invest heavily into the platform, build for Teams users, and want to make that experience better. Questions to the community: If you’re a Teams app developer: what actually worked for you in terms of marketplace growth? Which Partner programs or motions are worth the effort, and which can be safely ignored early on? Is there a realistic way to engage with the Teams Marketplace team (feedback loops, programs, office hours, etc.)? How do you go from “organic installs happen” to a structured channel? Would really appreciate any practical advice, lessons learned, or even “what not to do” stories 🙏 Thanks in advance!5 App Advisor capabilities that help sell faster on Microsoft Marketplace
Signing in to App Advisor unlocks features that help you manage projects, personalize your development guidance, and move from build to Marketplace sales faster. App Advisor helps streamline the process for anyone, with no barriers to start. However, when you sign in and authenticate, the experience becomes even more powerful. Read the 5 reasons to authenticate in App Advisor here.
SaaS Transactable Listing on Microsoft Marketplace — Questions & Best Practices
Hi Microsoft Marketplace Community, We are Saturam Inc., the team behind Qualdo-DRX — a Data Observability, Reliability & Quality SaaS solution now listed on the Microsoft Azure Marketplace. As we continue to build and optimize our Marketplace presence, we have a few questions and would love to hear from experienced partners and the Microsoft team. Qualdo-DRX is a Managed Application offer on Azure Marketplace. We help enterprises monitor data quality, reliability, and observability across Azure-native data services including Microsoft Fabric, Azure Synapse, ADLS, and more. A free trial plan is also available. We are interested in connecting with other ISVs who have successfully published transactable SaaS offers and navigated the Microsoft Marketplace journey. If you have lessons learned, tips, or resources to share, we welcome the conversation! Feel free to reply below or reach out directly. We are happy to share our own experience listing Qualdo-DRX and support others on their Marketplace journey. #AzureMarketplace #SaaS #TransactableOffer #ISV #MicrosoftMarketplace #CoSell #MarketplaceRewardsMicrosoft Marketplace partner office hours: How to build a Microsoft Marketplace channel practice
Building a strong Marketplace practice requires aligned teams, clear ownership, and a solid strategy—not just great offers. Join us February 18th 8:30 AM PT for the February Partner office hour online session with live Q&A, to learn how top partners structure their organizations and use a proven framework to drive Marketplace growth. You’ll walk away with practical tools and templates to accelerate onboarding, publishing, co‑sell, and sales execution. Registration is not required. Learn how you can attend and receive reminders for the session here: Marketplace office hours for partners: February 2026 - Microsoft Marketplace Community All sessions are recorded, and the link above can be used to watch the recording once the session date has passed. Partner office hour sessions are held monthly, typically on the third Wednesday of each month. Check the Marketplace Community Events Calendar for upcoming dates and times. Session topics will be added as the event approaches.
