Getting first customers after publishing what actually worked for you?
I published two transactable SaaS offers earlier this month (Microsoft 365 security and compliance assessment). The technical side went fine, with good support from the ISV team. Getting the offers in front of customers is where I'm stuck. So far I've reached Rewards Tier 3 and activated the listing optimisation benefit. I understand customer references are the gate to co-sell readiness, which feels like a chicken-and-egg problem when you have none yet. For publishers further along than me: what actually produced your first few customers? Was it private offers to targeted tenants, co-sell once you had references, the Marketplace Rewards activities, or something outside the marketplace entirely? I'd rather spend my limited time on what works than keep guessing.Work IQ Cost and Pricing Model for Agents published in Marketplace
I would very much like to ask for assistance and input in regards of "Consumption, cost forecasting, break-even analysis and recommended customer pricing. " I believe a bit of context is required. We are about to publish two Work IQ MCP Agent in Marketplace. Both agents will be integrated to our existing Apps that are published in Marketplace. 1. iGlobe CRM for Teams and 2. iPlanner Pro for Outlook and Teams. Both Apps are multitenant Apps running on customers own tenant. The Apps can only be deployed from Marketplace. Lets use iPlanner Pro Agent as an example. In short, iPlanner Pro Agent uses Work IQ and MCP tools to understand emails, meetings and Microsoft 365 context, while iPlanner Pro turns that intelligence into structured Planner execution, including the appropriate plan, bucket, assignment, priority and supporting email context. Including saving the email or attachments on the task. A prompt could look like this "Find the latest email from email address removed for privacy reasons. Create a task for in Planner, put it in the correct Plan and bucket, assign it to the appropriate owner, set a due date for next week and save the email on the Planner Task" Work IQ and MCP provide context and intelligence, iPlanner Pro executes the structured actions, and Microsoft Planner remains the source of truth. To my understanding Work IQ API usage is billed through Copilot Credits rather than a separate per-user Work IQ license. Microsoft 365 Copilot includes Work IQ functionality within native Microsoft Copilot experiences, but third-party solutions such as iPlanner Pro consume Copilot Credits when they call Work IQ APIs. Work IQ does not require a separate API subscription, SKU or per-user licence. API usage is consumption-based and is measured in Copilot Credits. Work IQ operation Credit consumption Planning note Chat API Variable Depends on grounding, retrieval and reasoning complexity. Context API Variable Depends on the scenario and the sources used. Tool API 0.1 credit per API call Static component: $0.001 per call at $0.01 per credit. Example: 1,000 Tool API calls consume 100 credits, equivalent to $1.00 at the planning rate. Now here comes my confusion. 1. How do we know how many credits we are using with a prompt like the example I showed? the consumption is 0.1 credit per API call. 2. If I understand the above correct, the total consumption is calculated on the customer tenant. That consumption includes much more than just iPlanner Pro. Is that correct? 3. OR, will the sum of all customer using iPlanner Pro Agent be direct to us and we will be billed based on the total consumption of all our customers? If that is the case ( I don't think it is) then how do we know how much each customer consumed? 4. If consumption and billing is on the customer, how can the customer see what iPlanner Pro cost in consumed credits? This is important in regards of giving a cost forecast for customers. I am sure they would want that before start using the Agent. It is also important in regards of the question of if we should charge or offer it as a free agent? If we are billed then we will need to change for it. If the customer is billed then we probably will not be able to charge for it. I hope I have been able to explain my confusion. I tried asking contacts I have at Microsoft, but unfortunately haven't heard anything. That is why I am asking in the community. Have any one else the same challenge? what have you done? Can Microsoft assist here? I will be happy to take a Teams call and others are welcome to join. Thank you
Publishing readiness for AI apps and agents in Microsoft Marketplace
Discover how to prepare AI apps and agents for publishing in Microsoft Marketplace. This Marketplace Community article explains why readiness starts before Partner Center, focusing on the operational, technical, and organizational foundations required to ensure solutions can be evaluated, purchased, and operated reliably. As AI systems manage identity, data, runtime behavior, and subscription lifecycles, gaps in readiness can create friction during certification and customer adoption. Clearly defined identity boundaries, consistent data handling practices, and predictable responses to subscription events help ensure solutions behave as expected across environments and tenants. Learn how to establish publishing readiness that supports smooth certification, reliable operations, and confident customer adoption at Marketplace scale. Read more: Publishing readiness for AI apps and agents on Microsoft MarketplaceDesign CI/CD pipelines for AI apps and agents in Microsoft Marketplace
Discover how to design CI/CD pipelines for AI apps and agents selling through Microsoft Marketplace. This Marketplace Community article explains why controlling how changes reach production is essential for maintaining predictable behavior, reliability, and customer trust. As AI systems evolve through updates to code, models, prompts, and agent logic, behavior can change in ways that impact cost, performance, and outcomes. Structured pipelines that isolate change, validate behavior, and enable safe promotion and rollback help ensure updates are introduced deliberately—without unexpected impact across environments or tenants. Learn how to design CI/CD strategies that support safe iteration, controlled releases, and consistent behavior as AI solutions scale in Marketplace environments. Read more: Design CI/CD for AI apps and agents selling through Microsoft MarketplaceDesign reliable environment strategies for AI apps and agents in Microsoft Marketplace
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Discover how to enforce entitlements in AI apps and agents using Microsoft Marketplace commerce signals. This Marketplace Community article explains why purchase and subscription data must be integrated at runtime to ensure customers only access what they’ve paid for. As AI apps and agents dynamically invoke tools, expose capabilities, and operate without constant user input, static enforcement approaches fall short. Translating Marketplace signals into deterministic runtime behavior—across SaaS, containers, virtual machines, and managed applications—ensures access is controlled, auditable, and aligned with subscription state. Learn how to design entitlement enforcement that remains consistent through plan changes, scaling workloads, and real‑time agent decisions. Read more: Integrate Marketplace commerce signals to enforce entitlements in AI appsBuild observability for scalable AI apps and agents selling through Microsoft Marketplace
Discover how to design observability for AI apps and agents selling through Microsoft Marketplace. This Marketplace Community article explains why visibility into execution behavior is essential for operating AI systems confidently at scale—not just keeping them running. As AI apps and agents reason, branch, retry, and exit dynamically at runtime, traditional infrastructure metrics fall short. Behavioral signals such as execution flow, token usage, latency, and failure patterns help explain what systems are doing, why outcomes occur, and how limits and safeguards shape behavior across tenants and environments. Learn how observability turns runtime telemetry into clarity that supports customer trust, usage‑based billing, and scalable operations. Read more: Design observability for AI apps and agents selling through Microsoft MarketplaceDesign resilient API execution for AI apps and agents in Microsoft Marketplace
Discover how to design resilient and reliable APIs for AI apps and agents selling through Microsoft Marketplace. This Marketplace Community article explains why reliability is a critical readiness requirement and how predictable API behavior protects customer trust when systems encounter failure. AI apps and agents depend on APIs for every stage of execution—from model inference to tool calls and orchestration. Designing clear API boundaries, enforcing timeouts and retries, and applying resilience patterns such as circuit breakers and bulkheads helps contain failures, prevent cascading issues, and keep agent behavior bounded as solutions scale. Learn how to design AI systems that fail safely, operate predictably, and remain stable under real customer load across Marketplace environments. Read more: API resilience and reliability patterns for AI apps and agents selling through Microsoft MarketplaceBuild and scale AI apps and agents for Microsoft Marketplace success
Ready to move from AI concept to Marketplace success? This article walks through the end-to-end journey of building, publishing, and commercializing AI apps and agents on Microsoft Marketplace—helping software companies navigate the full lifecycle with confidence. From architecture and security to compliance, operations, and commerce integration, Marketplace readiness requires more than a working model—it demands a holistic, production-ready approach. This post introduces a connected framework to guide every stage of development, ensuring your solution is designed for real-world deployment, scalability, and customer trust. Whether you’re refining a prototype or preparing for launch, discover how to align your AI solution with Marketplace requirements from day one and accelerate your path to revenue. Read the full article: Success with AI apps and agents in Marketplace: the end-to-endDesign predictable AI performance to scale selling through Microsoft Marketplace
Discover how to design predictable AI performance that scales when selling apps and agents through Microsoft Marketplace. This Marketplace Community article explains why performance variability can undermine customer trust and how intentional design boundaries help customers evaluate value with confidence. As AI systems reason, retrieve data, and orchestrate tools at runtime, latency, quality, and cost become tightly linked. Bounded execution paths, cost‑aware orchestration, and layered performance controls help ensure responses remain fast, reliable, and predictable—without sacrificing output quality as usage grows. Learn how to design AI architectures that customers can trial confidently, scale sustainably, and operate without surprises across Marketplace environments. Read more: Design Predictable AI Performance for Apps Selling Through Microsoft Marketplace
