Design tenant linking to scale selling on Microsoft Marketplace
Designing tenant linking and Open Authorization (OAuth) directly shapes how customers onboard, grant trust, and operate your AI app or agent through Microsoft Marketplace. In this context, consent refers to the explicit authorization a customer tenant grants—via OAuth—for a publisher’s application or agent to access specific resources and perform defined actions within that tenant. This post explains how to design scalable, review‑ready identity patterns that support secure activation, clear authorization boundaries, and enterprise trust from day one. Guidance for multi‑tenant AI apps Identity decisions are rarely visible in architecture diagrams, but they are immediately visible to customers. In Microsoft Marketplace, tenant linking and OAuth consent are not background implementation details. They shape activation, onboarding, certification, and long‑term trust with enterprise buyers. When identity decisions are made late, the impact is predictable. Onboarding breaks. Permissions feel misaligned. Reviews stall. Customers hesitate. When identity is designed intentionally from the start, Marketplace experiences feel coherent, secure, and enterprise‑ready. This article focuses on how to design tenant linking and consent patterns that scale across customers, offer types, and Marketplace review—without rework later. 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 identity across tenants is a first‑class design decision Designing identity is not just about authentication. It is about how trust is established between your solution and a customer tenant, and how that trust evolves over time. When identity decisions are deferred, failure modes surface quickly: Activation flows that cannot complete cleanly Consent requests that do not match declared functionality Over‑privileged apps that fail security review Customers who cannot confidently revoke access These are not edge cases. They are some of the most common reasons Marketplace onboarding slows or certifications are delayed. A good identity and access management design ensures that trust, consent, provisioning, and operation follow a predictable and reviewable path—one that customers understand and administrators can approve. Marketplace tenant linking requirements A key mental model simplifies everything that follows, separate trust establishment from authorization. Tenant linking and OAuth consent solve different problems. Tenant linking establishes trust between tenants OAuth consent grants permission within that trust Tenant linking answers: Which customer tenant does this solution trust? OAuth consent answers: What is this solution allowed to do once trusted? AI solutions published in Microsoft Marketplace should enforce this separation intentionally. Trust must be established before meaningful permissions are granted, and permission scope must align to declared functionality. Making this explicit distinction early prevents architectural shortcuts that later block certification. Throughout the rest of this post, tenant linking refers to trust establishment, not permission scope. Microsoft Entra ID as the identity foundation Microsoft Entra ID provides the primitives for identity-based access control, but the concepts only become useful when translated into publisher decisions. Each core concept maps to a choice you make early: Home tenant vs resource tenant Determines where operational control lives and how cross‑tenant trust is anchored. App registrations Define the maximum permission boundary your solution can ever request. Service principals Determine how your app appears, is governed, and is managed inside customer tenants. Managed identities Reduce long‑term credential risk and operational overhead. Understanding these decisions early prevents redesigning consent flows, re‑certifying offers, or re‑provisioning customers later. Marketplace policies reinforce this by allowing only limited consent during activation, with broader permissions granted incrementally after onboarding. Importantly, activation consent is not operational consent. Activation establishes the commercial and identity relationship. Operational permissions come later, when customers understand what your solution will actually do. OAuth consent patterns for multi‑tenant AI apps OAuth consent is not an implementation detail in Marketplace. It directly determines whether your AI app can be certified, deployed smoothly, and governed by enterprise customers. Common consent patterns map closely to AI behavior: User consent Supports read‑only or user‑initiated interactions with no autonomous actions. Admin consent Enables agents, background jobs, cross‑user access, and cross‑resource operations. Pre‑authorized consent Enables predictable, enterprise‑grade onboarding with known and approved scopes. While some AI experiences begin with user‑driven interactions, most AI solutions in Marketplace ultimately require admin consent. They operate asynchronously, act across resources, or persist beyond a single user session. Aligning expectations early avoids friction during review and deployment. Designing consent flows customers can trust Consent dialogs are part of your product experience. They are not just Microsoft‑provided UI. Marketplace reviewers evaluate whether requested permissions are proportional to declared functionality. Over‑scoped consent remains one of the most common causes of delayed or failed certification. Strong consent design: Requests only what is necessary for declared behavior Explains why permissions are needed in plain language Aligns timing with customer understanding Poor explanations increase admin rejection rates, even when permissions are technically valid. Clear consent copy builds trust and accelerates approvals. Tenant linking across offer types Identity design must align with offer type; a helpful framing is ownership: SaaS offers The publisher owns identity orchestration and tenant linking. Microsoft Marketplace reviewers expect this alignment, and mismatches surface quickly during certification. Containers and virtual machines The customer owns runtime identity; the publisher integrates with it. Managed applications Responsibility is shared, but the publisher defines the trust boundary. Each model carries different expectations for control, consent, and revocation. Designing tenant linking that matches the offer type reduces customer confusion. When consent happens in Marketplace lifecycle Many identity issues stem from unclear timing. A simple lifecycle helps anchor expectations: Buy – The customer purchases the offer Activate – Tenant trust is established Consent – Limited activation consent is granted Provision – Resources and configurations are created Operate – Incremental operational consent may be requested Revoke – Access and trust can be cleanly removed Making this sequence explicit in your design—and in your documentation—dramatically reduces confusion for customers and reviewers alike. How tenant linking shapes Marketplace readiness Identity tends to leave a lasting impression as it is one of the first architectural design choices encountered by customers. Strong tenant linking and consent design lead to: Faster certification (applies to SaaS offer only) Fewer conditional approvals Lower onboarding drop‑off Easier enterprise security reviews These outcomes are not accidental. They reflect intentional design choices made early. What’s next in the journey Tenant identity sets the foundation, but it is only one part of Marketplace readiness. In upcoming guidance, we’ll connect identity decisions to commerce, SaaS Fulfillment APIs, and operational lifecycle management—so buy, activate, provision, operate, and revoke will work together as a single, coherent system. 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 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 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. 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 SuccessContinuity-of-care, clinically governed AI, and building on Microsoft Azure
In this installment of our Partner Spotlight series, we’re highlighting a partner building secure, scalable solutions on Microsoft Azure and delivering transactable offers on Microsoft Marketplace. I connected with Darren Booker, Founder and CEO of Tali Health, to learn how their clinically governed AI platform helps extend care beyond the clinic while keeping providers in control. By building AI-powered patient engagement on the Microsoft Cloud, Tali is helping care teams deliver more consistent guidance between visits while scaling support without adding burden to clinicians. About Darren Darren Booker is the Founder and CEO of Tali Health, a continuity-of-care platform that uses clinically governed AI to extend clinician-guided care beyond the clinic. _______________________________________________________________________________________________________________________________________________________________ [JR] Tell us about Tali Health—what inspired its founding, and what services do you offer? [DB] Tali Health is a continuity-of-care platform that uses clinically governed AI to extend clinician-guided care beyond the walls of the clinic. Clinical governance is key—it helps us deliver on-demand guidance and support for patients while keeping providers firmly in control of care. Healthcare teams invest significant time and expertise developing personalized treatment plans, yet most patient care happens after the visit—at home, between appointments. That’s where confusion, nonadherence, and avoidable complications often occur. We built Tali to close that gap. Our platform delivers multilingual, care-plan-aligned guidance between visits—helping patients follow provider instructions, understand prescribed medications, and know when symptoms are expected versus when they require attention. Tali also analyzes patient questions and remote monitoring signals—such as blood pressure or glucose levels—and triages them based on thresholds set by the care team. The result is improved adherence and patient confidence while reducing repetitive outreach and monitoring work for clinical staff. [JR] Who is your solution designed for, and what problems does it help solve? [DB] Tali is designed for healthcare organizations and chronic care management vendors that support large volumes of patients who need ongoing engagement outside the clinic—particularly teams delivering chronic care management, remote patient monitoring, and post-procedure follow-up. The platform acts as an automated, on-demand extension of the care team. Importantly, Tali never diagnoses or makes clinical decisions; it supports clinical teams between visits. Tali is designed to fit into existing provider workflows, including Epic and other EHR-connected environments, so organizations can extend post-visit support without adding disconnected tools. Patients receive 24/7 support through the Tali app in their preferred language. The system reinforces assigned care plans, answers common questions on demand, and surfaces clinically relevant changes in symptoms or remote monitoring data for care team review. For example, if a patient reports worsening symptoms or submits monitoring data that crosses a clinician-defined threshold, Tali flags the signal and routes it to the appropriate care team member for follow-up. Meanwhile, routine questions—such as medication timing or post-procedure expectations—are handled automatically through AI-guided responses aligned with the patient’s care plan. This approach improves adherence and engagement while helping care coordinators focus on meaningful clinical interventions rather than repetitive administrative outreach. By combining clinically governed AI-driven triage with clinician-configured protocols, Tali helps healthcare organizations scale high-touch care models without proportionally increasing staffing requirements. [JR] Your solution is built on Microsoft Azure. How does Microsoft technology support what you’re building? [DB] Healthcare data requires a high bar for security and reliability, so we built Tali natively on Microsoft Azure with a HIPAA- and GDPR-aligned architecture. Azure provides the foundation for secure data handling, scalable infrastructure, and regional data residency—capabilities needed to support healthcare providers across multiple markets, including the United States and the Gulf region. For healthcare organizations evaluating new technologies, trust is essential. Azure helps us deliver AI-powered patient engagement while meeting the compliance, security, and reliability standards required to handle protected health information. [JR] What motivated you to publish a transactable offer on Microsoft Marketplace? [DB] Healthcare organizations are increasingly standardizing procurement through trusted cloud marketplaces, and we recognized early that Microsoft Marketplace would be an important channel for reaching the end users Tali was designed to support—including enterprise healthcare buyers. Publishing on Microsoft Marketplace helps healthcare organizations already operating in the Microsoft ecosystem discover, evaluate, and deploy Tali more easily while aligning purchases with their existing Azure cloud commitments. Because Tali is built on Azure, Microsoft Marketplace also creates a seamless procurement path for customers who want infrastructure, security, and compliance alignment from day one. Another key motivation was the opportunity to participate in Microsoft’s selling-with-Microsoft motion. Healthcare providers already working with Microsoft sellers can discover Tali as a complementary solution that extends the value of Azure infrastructure into patient engagement and remote care workflows. For us, Microsoft Marketplace is not just a listing—it’s a strategic go-to-market channel that helps align technology, procurement, and sales motions to accelerate adoption while maintaining the security and compliance expectations required in healthcare. [JR] Did Microsoft tools like App Advisor or Marketplace Rewards play a role in your journey? [DB] Absolutely. App Advisor, Marketplace Rewards, and ISV Success all played an important role in our journey. Beyond technical guidance, these all helped us think strategically about how to scale on Microsoft Marketplace—from optimizing our offer structure to building a clean pipeline and aligning with selling with Microsoft and marketplace growth initiatives. For early-stage companies especially, these opportunities can significantly accelerate both publishing and commercialization on the platform. [JR] AI is a major focus across Microsoft and Marketplace. How are you integrating AI into your solution? [DB] Tali is unique in its use of clinically governed AI, meaning its response logic is tied directly to the patient’s assigned care plans and medical history. For patients, this means they receive timely, targeted, and easy-to-understand guidance after a visit—whether they’re asking about medications, recovery expectations, or changes in symptoms. Our AI delivers responses aligned with the patient’s care plan while maintaining a fifth-grade reading level designed for broad accessibility. On the provider side, AI helps identify signals that require clinical attention. Instead of care teams manually reviewing every incoming message or data point, Tali highlights situations where clinician intervention may be needed based on thresholds defined by the practice. Looking ahead, we see AI continuing to enhance the ability of healthcare organizations to scale high-quality patient engagement—supporting more proactive care models while allowing clinicians to focus their time on the patients who need them most. [JR] Security and compliance are critical in healthcare. How did you approach building Tali securely? [DB] As we expanded Tali’s AI capabilities, security and compliance stayed foundational from the earliest stages of development. Because our platform works with protected health information, we designed our architecture on Microsoft Azure to align with the stringent security expectations of healthcare organizations. Azure provides the infrastructure capabilities needed to support encrypted data storage, secure identity management, and controlled access to sensitive information. These capabilities allow us to implement HIPAA- and GDPR-aligned safeguards while also supporting regional data residency requirements for markets such as the United States and the Gulf region. We also follow a principle of minimizing unnecessary exposure to sensitive data wherever possible, with access-controlled systems designed to ensure that only authorized users and components interact with protected health information. Our goal is to help organizations adopt AI-driven patient engagement while maintaining the security, reliability, and compliance standards their patients expect. [JR] What lessons would you share with other partners considering Microsoft Marketplace? [DB] One of the most important lessons is to view Microsoft Marketplace not simply as a listing channel, but as part of a broader go-to-market strategy. Healthcare organizations increasingly prefer solutions that integrate naturally with the technology ecosystems they already trust. By aligning with Azure and Microsoft Marketplace, partners can reduce friction for buyers who want procurement, infrastructure, and security models that work together seamlessly. Another takeaway is to design your solution architecture with enterprise requirements in mind from the beginning. In healthcare, compliance, security, and scalability can’t be afterthoughts—they’re core to adoption. Finally, Microsoft Marketplace can open doors to collaboration with Microsoft sellers and ecosystem partners who are already working closely with enterprise customers. When partners align real customer needs with the strengths of the Microsoft platform, Marketplace becomes far more than a distribution channel—it becomes a catalyst for adoption and impact. _______________________________________________________________________________________________________________________________________________________________ Closing reflection Tali Health shows how clinically governed AI, built on Microsoft Azure and delivered through Microsoft Marketplace, can help care teams extend support beyond the clinic while maintaining trust, security, and clinical control.How manufacturers can scale AI from pilot to production with Microsoft Marketplace
Manufacturers are under unprecedented pressure. Labor constraints, rising costs, shifting supply chains, and growing demand are all colliding at once—and expectations for AI to help address these challenges have never been higher. But as many organizations are discovering, interest in AI does not automatically translate into operational impact. In a recent Microsoft Marketplace customer office hour, Microsoft explored what it takes to move AI initiatives in manufacturing beyond pilots—and how Microsoft Marketplace can help organizations scale AI in a governed, practical way. Below are the key takeaways manufacturing leaders should consider as they chart their AI strategy. Why AI scaling is the biggest challenge in manufacturing Across conversations with manufacturing customers, several challenges consistently emerge: Factory and operational data remain highly fragmented Integrating edge and cloud systems introduces significant complexity AI ambition often outpaces integration readiness, cost controls, and operational maturity The result is a familiar pattern: AI pilots that demonstrate promise but never reach production. In fact, more than half of manufacturers are still operating AI initiatives in pilot mode. The problem is not whether AI works. The real challenge is how to scale AI in a way that is sustainable, secure, and governed. In many cases, the issue is not model performance, but the architectural and operational complexity required to deploy AI across multiple plants and production environments. Without the right data foundation and deployment model, even successful pilots remain isolated experiments rather than scalable operational capabilities. The unified data foundation: The backbone of industrial AI One of the most important lessons from the session is simple: AI models alone do not create value. AI delivers real impact when it is grounded in connected, high‑quality data across manufacturing systems, including: ERP systems Manufacturing execution systems (MES) Maintenance and asset management platforms IoT sensors and historians Documents, logs, and frontline operator knowledge This unified data foundation breaks down operational silos and enables analytics, digital twins, and AI agents to operate at scale. Without it, AI initiatives struggle to move beyond isolated use cases and proof‑of‑concept projects. This foundation enables manufacturers to transition from insight‑generation to decision‑making—unlocking the ability for AI agents to act on operational context in real time rather than simply producing analytical outputs. How edge and cloud AI work together in manufacturing A successful industrial AI strategy does not force a choice between edge or cloud—they are complementary. Edge intelligence enables real-time inference and low‑latency decisions close to machines Cloud intelligence supports advanced analytics, cross plant insights, digital twins, and large‑scale reasoning When connected through a governed data foundation, edge and cloud work together to drive continuous improvement across manufacturing operations. This architectural approach enables manufacturers to run latency‑sensitive inference close to equipment at the edge, while scaling analytics, simulation, and optimization in the cloud across plants. Designing AI solutions that leverage both environments is often critical to achieving operational impact without disrupting production workflows. Microsoft Marketplace spans these layers, offering access to vetted AI models, agents, applications, and connectors that deploy directly into Azure environments with built-in governance and cost control. Real world manufacturing AI use cases driving measurable impact Several high impact scenarios illustrate where AI is already delivering value in manufacturing today. Predictive maintenance: Reducing downtime with AI Predictive maintenance sits at the intersection of machine telemetry, historical failure data, maintenance logs, inspection notes, and operator expertise. By blending internal telemetry with proven industry models—and deploying analytics at the edge when required—manufacturers can reduce unplanned downtime without rebuilding maintenance systems from scratch. By combining unstructured plant knowledge with real‑time telemetry, predictive maintenance becomes one of the fastest paths for manufacturers to realize measurable ROI from AI investments. Production optimization: Improving yield and throughput Production optimization focuses on identifying bottlenecks, reducing yield loss, and improving throughput. This requires combining process data with AI reasoning to understand where inefficiencies exist and what corrective actions to take. Once value is proven on a single line or plant, solutions can be scaled using reusable components across the organization. When deployed successfully, these solutions can be extended across lines and facilities using reusable components, enabling continuous improvement at scale. Frontline enablement: Scaling knowledge with AI agents Manufacturing organizations often struggle with training, onboarding, and knowledge retention. AI agents can deliver task specific guidance directly to frontline workers, reducing reliance on tribal knowledge while improving safety, productivity, and training outcomes. This approach helps close the gap between experienced and newer workers by making operational expertise accessible at the point of need. Build, buy, or blend: Choosing the right AI adoption path There is no one size fits all approach to AI adoption. Manufacturing leaders must balance control, speed, and cost when deciding how to move forward. Build offers maximum customization and differentiation, but requires time, specialized skills, and higher upfront investment Buy enables faster deployment with proven, pre‑vetted solutions and predictable costs Blend combines internal IP with partner solutions, offering flexibility and faster time to value Microsoft Marketplace supports all three paths, helping organizations discover solutions, simplify procurement, and maintain governance—whether deploying directly, working with partners, or leveraging private offers and private marketplaces. For many manufacturing scenarios, a blended approach is often the most practical, allowing organizations to retain differentiation in proprietary processes while accelerating time‑to‑value through proven partner solutions available in Marketplace. How Microsoft Marketplace helps manufacturers scale AI securely For manufacturing customers, Microsoft Marketplace serves as a trusted source of cloud and AI solutions across industries and use cases. Marketplace enables organizations to: Discover vetted AI applications, agents, and models Deploy solutions directly into Azure environments Maintain governance, security, and cost control Accelerate procurement and reduce vendor onboarding friction Support build, buy, and blend strategies through a single platform This approach helps manufacturers move faster while retaining operational and financial control. By deploying solutions within existing Azure environments, Marketplace helps ensure that identity, access controls, and cost governance remain aligned with enterprise policies as AI initiatives scale. How to avoid getting stuck in AI pilot mode To move AI from experimentation to production, manufacturers should start with: One high impact operational scenario Data that is accessible, governed, and connected across IT and OT systems A clear decision on what to build, buy, or blend Using proven components available through Microsoft Marketplace can reduce integration complexity, procurement delays, and governance friction, —allowing manufacturers to focus engineering effort on differentiated capabilities rather than rebuilding common AI functionality. Operationalizing Industrial AI at Scale As manufacturers move from experimentation to execution, the ability to scale AI responsibly across operations will become a defining competitive advantage. Achieving that scale requires more than deploying models—it depends on connecting data across systems, aligning AI to real operational scenarios, and making deliberate decisions about what to build, buy, or blend. Microsoft Marketplace helps accelerate this journey by reducing integration complexity, procurement delays, and governance friction—allowing organizations to move from pilot to production while focusing engineering effort on the capabilities that truly differentiate their business. Watch the on‑demand session: Charting your AI strategy for manufacturing with Marketplace to learn more about how to scale AI securely and strategically across your manufacturing operations and move AI initiatives from pilots to real operational impact diving deeper into architectures, decision frameworks, and real-world scenarios.
The AI-powered partner: Winning in the Microsoft ecosystem
About the author: Richard Jean-Felix, Cloud Marketplace Architect, WorkSpan has spent his career at the intersection of cloud marketplace strategy and partner operations, with hands-on experience helping organizations scale their presence on both AWS and Microsoft Marketplace. At WorkSpan, he works directly with partners navigating the operational complexity of Microsoft co-sell, from integrating leads in Partner Central to building the processes that turn marketplace listings into repeatable revenue. He's the person in the room who's actually done the work. About WorkSpan: WorkSpan provides AI agents for sellers and partner managers through the WorkSpan.AI Marketplace and Co-sell Platform For Sellers: WorkSpan's in‑CRM AI drives earlier, smarter co‑sell actions. For Partner Managers: WorkSpan's AI‑powered insights help launch and scale Microsoft partnerships. _________________________________________________________________________________________________________________________________________________________________ How AI is rewriting the rules of co-sell, Microsoft Marketplace success, and enterprise procurement — and why the window to act is now. For years, success in Microsoft's partner ecosystem came down to relationships, hustle, and knowing the right people. Those things still matter. But the gap between partner organizations that are winning and those that are stalling has opened — and increasingly, the difference isn't effort, it's intelligence. The volume of signals a modern partner leader is expected to act on — pipeline health, seller engagement, marketplace activity, incentive windows, account targeting, co-sell alignment — has grown faster than any team can process manually. At the same time, a seismic shift in how enterprise software is bought and sold is reshaping every go-to-market strategy. Cloud marketplaces are becoming the default procurement channel. AI is becoming the operating system of high-performing partnerships and selling with Microsoft's ecosystem is rewarding the prepared, the consistent, and the fast. This guide brings together the most critical insights from across the Microsoft partner landscape — the marketplace mistakes that cost software companies millions, the signals Microsoft sellers act on, the future of enterprise procurement, and the AI capabilities becoming table stakes for partner leaders who intend to win. Part 01 / The new procurement reality Cloud marketplaces are becoming the default buying channel For decades, enterprise software procurement followed a familiar path: a vendor sold directly to the customer, procurement teams negotiated contracts, finance approved the purchase, and software was deployed within the customer's infrastructure. That model is rapidly replaced. Cloud marketplaces like Microsoft Marketplace are no longer simply listing directories. They have evolved into strategic procurement channels that align the interests of customers, cloud providers, and software vendors simultaneously. The committed spend driver Large enterprises frequently commit hundreds of millions of dollars to cloud providers through multi-year agreements. When customers purchase software through a cloud marketplace, that purchase often counts toward their cloud spend commitments, uses existing cloud budgets, and avoids adding new vendor contracts to finance's plate. Procurement simplicity changes everything Traditional enterprise procurement can take months: vendor onboarding, contract negotiation, security reviews, procurement approvals, payment processing. Cloud marketplaces streamline this entire process. Because the software vendor is already integrated with the cloud provider's billing infrastructure, procurement teams can often purchase using the same contract they already have with the cloud provider. For software vendors, this means faster sales cycles and dramatically reduced time-to-revenue. For customers, it means deploying solutions in days, not months. The ecosystem reshaping partner relationships Marketplaces introduce new collaboration opportunities: partners can bundle solutions together, participate in multiparty private offers, transact jointly, and align services with software purchases. AI automation opportunity: AI can automate the monitoring of Marketplace performance metrics — pipeline health, private offer status, renewal windows, and Azure consumption contribution — surfacing next-best actions before opportunities slip. What once required manual reporting across multiple systems becomes a continuous, intelligent feed of actionable insight. Part 02 / Common pitfalls The 7 biggest mistakes software companies make with Microsoft Marketplace Microsoft Marketplace has become one of the fastest-growing enterprise software procurement channels. Many companies struggle to gain traction — and in most cases, the problem isn't Marketplace itself. It's the strategy behind how it's used. Mistake 01 — Treating Marketplace as a "listing requirement" Publishing a listing to satisfy a partner requirement — but never actively using it. Little internal awareness, minimal seller adoption, no real revenue impact. ✓ Fix: Treat your listing as a core sales asset integrated into every deal. Mistake 02 — Waiting until the end of the deal to introduce Marketplace Introducing Marketplace only at procurement stage creates friction and stalled deals. The deal structure is already set, and changing it feels disruptive. ✓ Fix: Position Marketplace as a buying advantage from first contact. Mistake 03 — Not enabling Microsoft sellers Without enablement, Microsoft sellers don't know what your solution does, which customers it fits, or how it drives Azure consumption — so they won't bring it to deals. ✓ Fix: Provide a one-page seller pitch, target profiles, and Azure architecture alignment. Mistake 04 — Making private offers operationally difficult When creating a private offer requires multiple approval steps, manual calculations, and cross-team coordination, deals stall and sales teams avoid it. ✓ Fix: Automate private offer creation — it should be as easy as generating a quote. Mistake 05 — Ignoring internal sales enablement If your own sellers don't understand compensation for Marketplace deals or see it as friction, they'll actively avoid it regardless of customer readiness. ✓ Fix: Ensure sales compensation neutrality and train teams on marketplace value. Mistake 06 — Not tracking Marketplace performance Without visibility into pipeline influence, co-sell rates, and revenue flow, leadership can't justify investment and the program stalls from neglect. ✓ Fix: Track Marketplace pipeline, win rates, and Azure consumption as core revenue metrics. Mistake 07 — Failing to build a repeatable Marketplace motion Celebrating a first Marketplace deal but never scaling beyond it. The real value comes from repeatability — automated offer creation, seller training, co-sell alignment, renewals, and upsell offers built into a consistent operating model. ✓ Fix: Think of Marketplace as an operational capability, not a transactional tool. AI automation opportunity: AI can directly address the most painful of these mistakes. Automated private offer generation reduces Mistake 04 from a multi-day process to minutes. Intelligent seller enablement surfaces the right pitch and customer profile in context — eliminating Mistakes 03 and 05 without manual coordination. Performance dashboards powered by AI turn Mistake 06 into a continuous leadership advantage rather than a quarterly scramble. Part 03 / The co-sell engine How Microsoft sellers decide which partners to co-sell with Microsoft's field organization includes thousands of account executives and cloud sellers working with enterprise customers across the globe. In theory, this presents a massive opportunity. Microsoft sellers prioritize only a small number of partners in their daily sales motions. Does the solution drive Azure consumption? The single most important factor. Every Microsoft cloud seller is measured on Azure revenue growth. Solutions driving AI/ML workloads, data and analytics, security, or industry-specific Azure infrastructure receive the most attention. Is the solution easy to sell? Microsoft sellers operate with aggressive targets and little time. Top partners provide a simple one-page field brief: value proposition, ideal customer scenario, Azure architecture, and Marketplace purchasing instructions. Is the solution available through Microsoft Marketplace? Microsoft sellers strongly prefer solutions purchasable through Marketplace. Transactions simplify procurement and help customers apply purchases toward Azure consumption commitments — a win for the customer, Microsoft, and the partner. Is the partner actively engaged in selling with Microsoft? Publishing a listing is not enough. Effective partners register opportunities in Partner Center, share deal updates with Microsoft account teams, and participate in joint customer meetings. Does the partner have strong customer proof? Solutions with strong customer validation — case studies, referenceable deployments, measurable business outcomes — are much easier for sellers to recommend with confidence. Is the partner easy to work with? Partners who respond quickly, provide clear pricing, simplify contracting through Marketplace, and support joint selling activities build trust over time. Do Microsoft sellers know you exist? Even the best solution struggles if sellers aren't aware of it. Successful partners invest in seller briefings, joint webinars, and account planning sessions to actively build visibility. AI automation opportunity: The most transformative AI application in co-sell is moving from "here's our content, go find it" to one where intelligence is delivered to the seller proactively: the right account, the right partner fit, the right "better together" message — all surfaced automatically in the flow of how sellers actually work. Partners that crack seller activation at scale build a structural advantage that's very hard for competitors to close. Part 04 / The intelligence layer The partner leader's attention problem is real and structural. A typical partner organization is simultaneously managing dozens to hundreds of active co-sell opportunities, seller relationships across Microsoft field teams, Marketplace offers with expiration dates, incentive programs with changing eligibility, and account targeting decisions that should be data-driven but rarely are. Most partner teams are operating on instinct and heroics. Things fall through the cracks not because people aren't working hard — but because the volume of decisions requiring good information exceeds what's humanly possible to track. AI addresses this at the root: not by replacing the partner leader's judgment, but by ensuring that judgment is applied to the right things, at the right time, with the right context. 🔍 Pipeline intelligence — AI continuously monitors pipeline health, flags deals going cold before it's too late, scores accounts by fit based on historical win patterns, and surfaces at-risk opportunities automatically. ✍️ Content generation — AI generates targeted value propositions for specific verticals, drafts seller-ready one-pagers, creates account-specific "better together" narratives, and produces ROI examples grounded in real customer data. ⚡ Offer automation — AI automates private offer creation workflows, proactively surfaces renewal windows, and reduces time-to-offer from days to minutes. 🎯 Account targeting — AI identifies accounts that look like past wins, generates lookalike account lists, surfaces applicable incentives in context, and ensures the right partner-to-account fit reaches the right seller at the right moment. 📊 Unified reporting — AI connects co-sell data, Marketplace data, seller activation data, and partner relationship data into a single view of partnership health — enabling leaders to see around corners and catch at-risk relationships before QBRs. 🔄 Repeatability — AI transforms one-time Marketplace transactions into repeatable operational playbooks, automating renewals, surfacing upsell signals, and systematizing institutional knowledge. Part 05 / The compounding return The ecosystem intelligence layer: Where it all connects Individual AI capabilities are valuable. But the real step-change comes when those capabilities are connected — when co-sell data, Marketplace data, seller activation data, and partner relationship data inform a unified view of partnership health. Most partner organizations today operate with fragmented data: CRM in one place, partner portal data in another, Marketplace reporting somewhere else, and seller feedback captured nowhere. Every leadership meeting requires someone to manually pull everything together — and even then, the picture is incomplete. Partner leaders who build a connected ecosystem intelligence layer gain the ability to see around corners — to know before a QBR that a key co-sell relationship is at risk, to catch a Marketplace renewal window before the customer's procurement cycle closes, to see that a particular vertical is outperforming and double down before the opportunity peaks. This is the compounding return on AI investment in partnerships. The longer you run on connected data, the smarter your decisions get — and the harder it becomes for competitors operating on intuition to catch up. AI turns co-sell from a relationship management problem into a performance management discipline. Partner leaders who make this shift stop asking "are we aligned with Microsoft?" and start asking, "what does our data say about where we win, where we stall, and what the next best action is?" Conclusion The window to build this advantage is now The enterprise software buying process is evolving faster than most organizations are moving. Cloud marketplaces are rapidly becoming one of the most important channels for software procurement. Microsoft's selling programs reward partners who are prepared, consistent and responsive - and AI enables partners to meet those expectations at scale. The partners building this capability today are already seeing the effects — in seller engagement, pipeline conversion, Marketplace performance, and the quality of their strategic conversations with Microsoft. The infrastructure to do this exists, and it doesn't require a multi-year transformation. Success in the Microsoft ecosystem doesn't come from simply publishing a Marketplace listing or registering as a co-sell partner. It comes from building a structured, AI-powered go-to-market operation that turns every signal — every deal, every seller interaction, every Marketplace transaction — into compounding intelligence. Those who adapt early and build strong Marketplace strategies, powered by AI, will be well positioned to capture the growing opportunity of this new procurement model. Join us on April 28th for a live webinar to learn more and ask questions. Maximize selling with Microsoft and Marketplace ROI - Microsoft Marketplace Community. If you are unable to attend, the session will be recorded and available on demand via the same link.How Microsoft Marketplace and ecosystem partnerships are reshaping enterprise go-to-market
Author Juhi Saha is CEO at Partner1, a two-time Inc. Power Partner Award winner and an official Microsoft Partner Led Network. Partner1 helps B2B software and services companies maximize the value of their partner ecosystems and transform partnerships into scalable profit engines. Specializing in channel development and strategic alliances, Partner1 empowers organizations to unlock their partnership potential through expert guidance, partnership program design, and actionable growth strategies. By focusing on partner-driven growth, Partner1 helps businesses, from startups to scale-ups, maximize revenue, accelerate market expansion, and build a lasting competitive advantage. ________________________________________________________________________________________________________________________________________________________________ Key takeaways from recent NYC founder and investor events “It’s no longer the era of go fast. It’s the era of go faster.” That sentiment, shared by an investor during one of our recent New York City gatherings, captures a broader shift underway in how startups are expected to scale. Speed is no longer just a function of product development or hiring. It is increasingly a function of how effectively companies leverage platforms, ecosystems, and commercial infrastructure that already exist. Over the past several weeks, Partner1 hosted two curated events bringing together founders, investors, and ecosystem leaders to explore how startups are accessing enterprise customers and accelerating growth through partnerships. The conversations centered on a practical question that continues to surface across early-stage and growth-stage companies: how do startups break into enterprise and scale in a market defined by AI, platforms, and increasingly complex buying environments? What emerged from these discussions is a clear pattern: the traditional model of building a product, hiring a sales team, and scaling through direct enterprise relationships is being supplemented, and in many cases replaced, by ecosystem-led growth. Partnerships are no longer a downstream channel decision. They are becoming a primary system through which companies access customers, accelerate revenue, and compete. Across both sessions, with perspectives from leaders at Microsoft, NVIDIA, Plug and Play Tech Center, and investors including Trajectory Ventures, several consistent themes emerged around how this shift is playing out in practice. Marketplace is becoming the default commercial infrastructure Evaluate your Marketplace readiness- understand how Microsoft Marketplace supports discovery, procurement, and scalable growth, and were your solution fits today. One of the most concrete shifts discussed was the role of Marketplace as the commercial backbone for modern software transactions. Marketplace is no longer positioned as an optional distribution channel. It is increasingly how Microsoft goes to market with software companies of all sizes, and how customers expect to discover, evaluate, and procure solutions. This shift is being driven by practical realities. Enterprise procurement has historically been one of the most significant sources of friction in software sales. Vendor onboarding, legal negotiations, billing complexity, and fragmented purchasing processes extend deal cycles and introduce risk. Marketplace addresses these issues directly by standardizing terms, consolidating billing, and pre-vetting vendors through the publisher agreement. These are not cosmetic improvements. They materially change how quickly transactions can occur. During the discussions, the Marketplace opportunity was reinforced with both data and real examples. Marketplace is enabling larger deals, faster sales cycles, and measurable revenue growth for companies that treat it as a core go-to-market motion and speakers shared examples from companies like Neo4j, Pangaea Data and ShookIoT. The examples shared ranged from small, niche startups closing their largest deals through Marketplace to companies significantly expanding their customer base by leveraging Microsoft’s commercial infrastructure. What stands out is that these outcomes are not isolated. They are becoming repeatable. As customer awareness of Marketplace increases, it is increasingly seen as the fastest path to the right solution, regardless of who built it. Several startups shared how their deals languished in procurement and were excited to hear from other companies in attendance around how they successfully used Marketplace to speed up procurement. Rethinking scale: why “Microsoft is too big” is the wrong assumption A recurring concern from founders was whether they are too early or too small to meaningfully engage with Microsoft. This perception is common, but it does not reflect how the ecosystem is evolving. The perspective shared by Microsoft leaders was clear. AI-native startups are not peripheral to the ecosystem. They are central to it. Supporting startups is not about proximity to large partners. It is about helping early-stage companies build faster, reduce risk, and reach enterprise customers sooner. This dynamic was described as a balance. Startups bring speed, specialization, and differentiated AI use cases. Microsoft brings global reach, enterprise relationships, and a mature commercial engine. When aligned, that combination becomes a multiplier. Multiple conversations touched on how Marketplace is where this alignment materializes. It serves as the convergence point between innovation and demand. Whether a company is early-stage or scaling, it provides a consistent path to reach customers and transact at enterprise scale. The implication is direct. Companies should not wait to be “big enough.” They should start early with Microsoft Marketplace and design for this motion from the beginning. The results will be reduced friction and enable them to reach enterprise customers faster. Co-sell is evolving from access to alignment Many founders approach partnerships with a familiar question: how do we get Microsoft sellers to pay attention to us? That framing is increasingly misaligned with how the system actually works. The more scalable model described in the sessions is based on alignment rather than attention. Becoming co-sell eligible is important, particularly as solutions begin to align with Azure consumption and commercial priorities. However, co-sell eligibility is a starting point. It allows a solution to be recognized within Microsoft’s system and to count toward seller objectives. The more important shift is where growth actually comes from. The fastest growing motion is not seller-led. It is partner-to-partner. System integrators and channel partners already have established customer relationships. They are the ones driving adoption at scale. Microsoft’s investment in channel-led growth reflects this, with partner-led motions representing one of the highest growth vectors. The takeaway for founders is practical: instead of asking how to get seller attention, the better question is how to become easy for partners to sell. Alignment to platform, customer need, and partner incentives drives outcomes more reliably than individual relationships. Partnerships are not a channel. They are a go-to-market system One of the most consistent misconceptions observed across attendees was treating partnerships as a secondary channel, but insights from the panelists as well as conversations during networking sessions highlighted how partnerships function as an integrated system that shapes how companies build, sell, and scale. Marketplace, co-sell eligibility, and partner-to-partner relationships are interconnected. Product decisions influence how easily a solution can be transacted. Marketplace presence influences discovery and procurement. Partner relationships determine how widely a solution can be distributed. This system view is especially important in AI. As solutions become more complex, both buyers and sellers are optimizing for simplicity and speed. Centralized platforms and ecosystems provide a way to meet those requirements. Companies that treat partnerships as a system create compounding advantages. Those that treat them as an add-on often struggle to gain traction, even with strong products. Expanding beyond enterprise: a multi-segment opportunity While many startups initially focus on landing large enterprise customers, the opportunity within the Microsoft ecosystem is broader. Microsoft’s reach extends across enterprise, mid-market, and SMB segments. With the rise of AI and agent-based solutions, there is increasing focus on embedding applications into environments where customers already operate, such as Microsoft 365, and leveraging channel partners to scale distribution. This creates a unified go-to-market path that spans multiple segments. Startups can reach enterprise customers while also expanding into mid-market and SMB through the same ecosystem infrastructure. Channel partners play a critical role in this expansion. They provide access, distribution, and scale that would be difficult to replicate through direct sales alone. For startups, this represents a meaningful opportunity to grow faster and more efficiently across segments. Investor perspective: partnerships as a signal of maturity From an investor standpoint, partnerships are increasingly a signal of go-to-market maturity. The ability to leverage platforms, align with ecosystem dynamics, and accelerate revenue through structured partnerships is becoming a differentiator. Going back to the investor’s comment that “It’s no longer the era of go fast. It’s the era of go faster. I am going to ask all my portfolio companies about their marketplace strategy.” - this reflects a broader shift in evaluation criteria. Marketplace and ecosystem alignment are not viewed as optional enhancements. They are becoming central to how companies compress time to revenue and scale efficiently. When evaluating companies with similar technical capabilities, investors are looking closely at how founders approach distribution. Companies with a clear strategy for leveraging ecosystems and Marketplace are often better positioned to scale with less friction and more capital efficiency. A practical starting point The guidance shared across both events was consistent and actionable. Start early. Do not wait for a specific stage to engage with the ecosystem. Build on the platform with clear, differentiated use cases that solve real customer problems. Treat Marketplace as a core go-to-market motion. This includes investing in strong listings, clear pricing, and a working knowledge of Marketplace capabilities such as private offers and partner-led transactions. Design for partner-to-partner distribution. Ensure that your solution is easy for others to position, sell, and deploy within existing customer environments. At a fundamental level, the objective is to reduce friction. Companies that are easy to buy, easy to deploy, and easy for partners to sell are the ones that scale most effectively. Enterprise growth is no longer driven solely by direct sales execution. It is increasingly shaped by how well a company integrates into an ecosystem that already has distribution, demand, and commercial infrastructure. For startups building in AI and enterprise software, the question is no longer whether to engage with platforms like Microsoft. It is how early and how intentionally they design for it. The companies that do this well are not simply participating in the ecosystem. They are using it to accelerate outcomes that would be difficult to achieve on their own. Live in NYC on April 21st: Hear from Redis, Datadog, Eden and Microsoft on how strategic Marketplace partnerships are built and scaled in practice Strategic partnerships across hyperscalers, database providers, observability platforms, and application ecosystems are no longer abstract concepts, but important GTM relationships. As customers' infrastructure becomes more complex, they require solutions that are interoperable, scalable, and easy to implement. With the rise of AI, marketplaces have become critical enablers of technology adoption. With each product offering a wide range of integrations, it's the first-party relationships between providers that set these solutions apart, delivering best-in-class support for customers' infrastructure. Partnerships, like those between Microsoft, Datadog, Eden, and Redis, accelerate and derisk enterprise cloud transformations, with the Microsoft Marketplace playing a central role in how services are delivered and scaled. Eden's migration platform, Exodus, enables zero-downtime database migrations, while Datadog is deeply integrated to ensure that these autonomous migrations are fully observed. Azure Managed Redis is a first-party Azure service that is becoming foundational for customers optimizing their data infrastructure for modern and agentic AI workloads. Eden and Datadog's autonomous migration service for Azure Managed Redis is now available on Microsoft Marketplace, making it easy for enterprises to get the most out of new Redis products. As enterprises make this shift, a broader pattern is emerging in which marketplaces are not just procurement vehicles but also enablers of ecosystem execution, particularly in the context of AI. Many AI initiatives fall short not because of model capability, but because underlying infrastructure and data environments are not properly optimized. Migrations, when executed well, become an opportunity to modernize architecture, improve performance, and prepare for scalable AI and agent deployments. Through coordinated partnerships across Microsoft, Eden, Datadog, and Redis, companies are aligning product, sales, and delivery into a unified operating model that accelerates time to value and reduces risk for enterprise customers. This is all before discussing AI as an autonomous agent for deploying new infrastructure via marketplaces. If you want to understand how these partnership models are being built in practice, and how to use marketplaces and ecosystem alignment to unlock growth and AI readiness in your own organization, this event will provide a direct view into how leading companies are executing today. Sign up here and follow for more events with partners for partners by Partner1 and Microsoft. Resources Marketplace readiness assessment Learn more about Microsoft Marketplace: Microsoft Marketplace overview - Marketplace customer documentation | Microsoft Learn Explore Microsoft Marketplace Microsoft Marketplace | cloud solutions, AI apps, and agents Join Microsoft Marketplace community: Microsoft Marketplace community | Microsoft Community HubReplicating 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.AI apps and agents: choosing your Marketplace offer type
Choosing your Marketplace offer type is one of the earliest—and most consequential—decisions you’ll make when preparing an AI app for Microsoft Marketplace. It’s also one of the hardest to change later. This post is the second in our Marketplace‑ready AI app series. Its goal is not to push you toward a specific option, but to help you understand how Marketplace offer types map to different AI delivery models—so you can make an informed decision before architecture, security, and publishing work begins. This post is part of a series on building and publishing well-architected AI apps and agents on Microsoft Marketplace. Why offer type is an important Marketplace decision Offer type is more than a packaging choice. It defines the operating model of your AI app on Marketplace: How customers acquire your solution Where the AI runtime executes Determining the right security and business boundaries for the AI solution and associated contextual data Who operates and updates the system How transactions and billing are handled Once an offer type is selected, it cannot be changed without creating a new offer. Teams that choose too quickly often discover later that the decision creates friction across architecture, security boundaries, or publishing requirements. Microsoft’s Publishing guide by offer type explains the structural differences between offer types and why this decision must be made up front. How Marketplace offer types map to AI delivery models AI apps differ from traditional software in a few critical ways: Contextual data may need to remain in a specific tenant or geography Agents may operate autonomously and continuously Control over infrastructure often determines trust and compliance How the solution is charged and monetized, including whether pricing is usage‑based, metered, or subscription‑driven (for example, billing per inference, per workflow execution, or as a flat monthly fee) The buyer’s technical capability, including the level of engineering expertise required to deploy and operate the solution (for example, SaaS is generally easier to consume, while container‑based and managed application offers often require stronger cloud engineering and DevOps skills) Marketplace offer types don’t describe features. They define responsibility boundaries—who controls the AI runtime, who owns the infrastructure, and where customer data is processed. At a high level, Marketplace supports four primary delivery models for AI solutions: SaaS Azure Managed Application Azure Container Virtual Machine Each represents a different balance between publisher control and customer control. The sections below explain what each model means in practice. Check out the interactive offer selection wizard in App Advisor for decision support. Below, we unpack each of the offer types. SaaS offers for AI apps SaaS is the most common model for AI apps and agents on Marketplace—and often the default starting point. With a SaaS offer, the AI service runs in the publisher’s Azure environment and is accessed by customers through a centralized endpoint. This model works well for: Multi‑tenant AI platforms and agents Continuous model and prompt updates Rapid experimentation and iteration Usage‑based or subscription billing Because the service is centrally hosted, publishers retain full control over deployment, updates, and operational behavior. For multi-tenant AI apps, this also means making early decisions about Microsoft Entra ID configuration—such as how customers are onboarded, whether access is granted through tenant-level consent or external identities, and how user identities, roles, and data are isolated across tenants to prevent cross-tenant access or data leakage. For official guidance, see the SaaS section of the Marketplace publishing guide and the AI agent overview, which describes SaaS‑based agent deployments. Plan a SaaS offer for Microsoft Marketplace. Azure Managed Applications for AI solutions In this model, the solution is deployed into the customer’s Azure subscription, not the publisher’s. There are two variants: Managed applications, where the publisher retains permissions to operate and update the deployed resources Solution templates, where the customer fully manages the deployment after installation This model is a strong fit when AI workloads must run inside customer‑controlled environments, such as: Regulated or sensitive data scenarios Customer‑owned networking and identity boundaries Infrastructure‑heavy AI solutions that can’t be centralized Willingness or need on part of the customer or IT team to tailor the app to the needs of the end customer specific environment Managed Applications sit between SaaS and fully customer‑run deployments. They offer more customer control than SaaS, while still allowing publishers to manage lifecycle aspects when appropriate. Marketplace guidance for Azure Applications is covered in the publishing guide. For more information, see the following links: Plan an Azure managed application for an Azure application offer. Azure Container offers for AI workloads With container offers, the customer runs the AI workload—typically on AKS—using container images supplied by the publisher. This model is best suited for scenarios that require: Strict data residency Air‑gapped or tightly controlled environments Customer‑managed Kubernetes infrastructure The publisher delivers the container artifacts, but deployment, scaling, and runtime operations occur in the customer’s environment. This shifts operational responsibility, risk and compute costs away from the publisher and toward the customer. Container offer requirements are covered in the Marketplace publishing guide. Plan a Microsoft Marketplace Container offer. Virtual Machine offers for AI solutions Virtual Machine offers still play a role, particularly for specialized or legacy AI solutions. VM offers package a pre‑configured AI environment that customers deploy into their Azure subscription. Compared to other models, they offer: Updates and scaling are more tightly scoped Iteration cycles tend to be longer The solution is more closely aligned with specific OS or hardware requirements They are most commonly used for: Legacy AI stacks Fixed‑function AI appliances Solutions with specialized hardware or driver dependencies VM publishing requirements are also documented in the Marketplace publishing guide. Plan a virtual machine offer for Microsoft Marketplace. Comparing offer types across AI‑specific decision dimensions Rather than asking “which offer type is best,” it’s more useful to ask what trade‑offs you’re making. Key lenses to consider include: Who operates the AI runtime day‑to‑day Where customer data and AI prompts inputs and outputs are processed and stored How quickly models, prompts, and logic can evolve The balance between publisher control and customer control How Marketplace transactions and billing align with runtime behavior SaaS Container (AKS / ACI) Virtual Machine (VM) Azure Managed Application What it is Fully managed, externally hosted app integrated with Marketplace for billing and entitlement Containerized app deployed into customer-managed Azure container environments VM image deployed directly into the customer’s Azure subscription Azure native solution deployed into the customer’s subscription, managed by the publisher Control plane Publisher‑owned Customer owned Customer owned Customer owned (with publisher access) Operational model Centralized operations, updates, and scaling Customer operates infra; publisher provides containers Customer operates infra; publisher provides VM image Per customer deployment and lifecycle Good fit scenarios • Multi‑tenant AI apps serving many customers • Fast onboarding and trials • Frequent model or feature updates • Publisher has full runtime control • AI apps or agents built as microservices • Legacy or lift-and-shift AI workloads • Enterprise AI solutions requiring customer owned infrastructure Avoid when • Customers require deployment into their own subscription • Strict data residency mandates customer control • Offline or air‑gapped environments are required • Customers standardize on Kubernetes • Custom OS or driver dependencies • Tight integration with customer Azure resources Typical AI usage pattern Centralized inference and orchestration across tenants • Portability across environments is important • Specialized runtime requirements • Strong compliance and governance needs Different AI solutions land in different places across these dimensions. The right choice is the one that matches your operational reality—not just your product vision. Note: If your solution primarily delivers virtual machines or containerized workloads, use a Virtual Machine or Container offer instead of an Azure Managed Application. Supported sales models and pricing options by Marketplace offer type Marketplace offer types don’t just define how an AI app and agent is deployed — they also determine how it can be sold, transacted, and expanded through Microsoft Marketplace. Understanding the supported sales models early helps avoid misalignment between architecture, pricing, and go‑to‑market strategy. Supported sales models Offer type Transactable listing Public listing Private offers Resale enabled Multiparty private offers Azure IP Co‑sell eligible SaaS Yes Yes Yes Yes Yes Yes Container Yes Yes Yes No Yes Yes Virtual Machine Yes Yes Yes Yes Yes Yes Azure Managed Application Yes Yes Yes No Yes Yes What these sales models mean Transactable listing A Marketplace listing that allows customers to purchase the solution directly through Microsoft Marketplace, with billing handled through Microsoft. Public listing A listing that is discoverable by any customer browsing Microsoft Marketplace and available for self‑service acquisition. Private offers Customer‑specific offers created by the publisher with negotiated pricing, terms, or configurations, purchased through Marketplace. Resale enabled Using resale enabled offers, software companies can authorize their channel partners to sell their existing Marketplace offers on their behalf. After authorization, channel partners can independently create and sell private offers without direct involvement from the software company. Multiparty private offers Private offers that involve one or more Microsoft partners (such as resellers or system integrators) as part of the transaction. Azure IP Co‑sell eligible When all requirements are met this allows your offers to contribute toward customers' Microsoft Azure Consumption Commitments (MACC). Pricing section Marketplace offer types determine the pricing models available. Make sure you build towards a marketplace offer type that aligns with how you want to deploy and price your solution. SaaS – Subscription or flat‑rate pricing, per‑user pricing, and usage‑based (metered) pricing. Container – Kubernetes‑based offers support multiple Marketplace‑transactable pricing models aligned to how the workload runs in the customer’s environment, including per core, per core in cluster, per node, per node in cluster, per pod, or per cluster pricing, all billed on a usage basis. Container offers can also support custom metered dimensions for application‑specific usage. Alternatively, publishers may offer Bring Your Own License (BYOL) plans, where customers deploy through Marketplace but bring an existing software license. Virtual Machine – Usage-based hourly pricing (flat rate, per vCPU, or per vCPU size), with optional 1-year or 3-year reservation discounts. Publishers may also offer Bring Your Own License (BYOL) plans, where customers bring an existing software license and are billed only for Azure infrastructure. Azure Managed Application – A monthly management or service fee charged by the publisher; Azure infrastructure consumption is billed separately to the customer. Note: Azure Managed Applications are designed to charge for management and operational services, not for SaaS‑style application usage or underlying infrastructure consumption. Buyer‑side assumptions to be aware of For customers to purchase AI apps and agents through these sales models: The customer must be able to purchase through Microsoft Marketplace using their existing Microsoft procurement setup Marketplace purchases align with enterprise buying and governance controls, rather than ad‑hoc vendor contracts For private and multiparty private offers, the customer must be willing to engage in a negotiated Marketplace transaction, rather than pure self‑service checkout Important clarification Supported sales models are consistent across Marketplace offer types. What varies by offer type is how the solution is provisioned, billed, operated, and updated. Sales flexibility alone should not drive offer‑type selection — it must align with the architecture and operating model of the AI app and agent. How this decision impacts everything that follows Offer type decisions ripple through the rest of the Marketplace journey. They directly shape: Architecture design choices Security and compliance boundaries Fulfillment APIs and billing integration Publishing and certification requirements Cost, scalability, and operational responsibility Follow the series for updates on new posts. What’s next in the journey With the offer type decision in place, the focus shifts to turning that choice into a production‑ready solution. This includes designing an architecture that aligns with your delivery model, establishing clear security and compliance boundaries, and preparing the operational foundations required to run, update, and scale your AI app or agent confidently in customer environments. Getting these elements right early reduces rework and sets the stage for a smoother Marketplace journey. Key resources See curated, step-by-step guidance to help you build, publish, or sell your app or agent (no matter where you start) in App Advisor Quick-Start Development Toolkit Microsoft AI Envisioning Day Events How to build and publish AI apps and agents for Microsoft Marketplace Get over $126K USD in benefits and technical consultations with ISV SuccessGet personalized, fast recommendations for your Marketplace listing to boost your discoverability
First impressions matter on Microsoft Marketplace. In a growing and increasingly competitive catalog of apps and agents, the quality of your listing often determines whether a customer discovers, clicks, tries, or moves on. But most software companies are left guessing. Is your value proposition clear enough? Are your benefits compelling? Are you aligned with Marketplace best practices? Now, you don’t have to guess. Try it now: https://aka.ms/ImproveMyListing Why speed matters in a crowded Marketplace Marketplace visibility is competitive. Customers are comparing multiple solutions quickly. If your listing is unclear, vague, or missing key details, you may not get a second chance. Traditional listing optimization required: Trial-and-error updates, Waiting for manual review cycles, Interpreting best-practice documentation, Delayed feedback after publishing or guessing about feedback. Now, you can move at the speed of AI. This agentic capability, trained on the best practices of listings and the extensive experience of Marketplace Reward expert editors, scans your public Marketplace listing and delivers: Targeted, focused recommendations, A clear rating across six critical categories customers care about, Immediate guidance aligned to Microsoft Marketplace best practices. Anyone can get objective feedback in seconds — not weeks. "To make sure that the quality of our listings stays at a consistently high score is going to be really valuable for us and we can make changes to our listings more often and with more confidence." -Dan Langille, VP Mover Engagement and Customer Experience, Breakthru How the AI-powered listing optimization works The experience is intentionally simple. Sign in to App Advisor using your Partner Center account, Select your published publicly available Marketplace offer, Receive personalized recommendations in seconds. Anyone at your organization can use this feature, no special permissions required. Your listing is evaluated across six categories that directly impact discoverability and engagement: Value proposition Solution description Information quality Presentation Clarity and coherence Grammar and style Each category receives a 1–5 score. You see exactly where to improve and why. After reviewing your recommendations, you can edit your listing in Partner Center, republish, and rescan after 24 hours to measure progress. No waiting. No guesswork. No cost. Built on Microsoft expertise This AI capability is trained on Marketplace best practices and the editorial methodology used by Microsoft experts reviewing thousands of listings. You’re not receiving generic AI suggestions. You’re getting structured, Marketplace-aligned guidance designed to help your listing: Improve clarity and positioning Increase search discoverability Strengthen engagement signals Better communicate value to buyers Because insights are AI-generated, you should review and validate each recommendation before publishing changes. But the speed and structure dramatically reduce friction. Designed for software companies who want to stand out Marketplace is not just a publishing channel. It is a competitive sales surface. High-quality listings: Convert more trials, Communicate value faster, Generate stronger engagement, Differentiate you from similar offers. This capability democratizes listing optimization. Previously constrained by availability, it is now available on demand to any organization with a public Marketplace listing in the US. Have multiple listings? Scan them all. Iterate as often as you need. Stop waiting. Start optimizing. What this means for your Marketplace growth Optimization is no longer a one-time activity. It becomes an iterative advantage. Scan. Improve. Republish. Rescan. Over time, this creates compounding improvements in discoverability and engagement — helping your app or agent rise above the noise. Ready to improve your listing today? Visit https://aka.ms/ImproveMyListing Sign in, select your offer, and get recommendations in seconds.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
