Build. Secure. Launch Your Private MCP Registry with Azure API Center.
We are thrilled to embrace a new era in the world of MCP registries. As organizations increasingly build and consume MCP servers, the need for a secure, governed, robust and easily discoverable tools catalog has become critical. Today, we are excited to show you how to do just that with MCP Center, a live example demonstrating how Azure API Center (APIC) can serve as a private and enterprise-ready MCP registry. The registry puts your MCPs just one click away for developers, ensuring no setup fuss and a direct path to coding brilliance. Why a private registry? 🤔 Public OSS registries have been instrumental in driving growth and innovation across the MCP ecosystem. But as adoption scales, so does the need for tighter security, governance, and control, this is where private MCP registries step in. This is where Azure API Center steps in. Azure API Center offers a powerful and centralized approach to MCP discovery and governance across diverse teams and services within an organization. Let's delve into the key benefits of leveraging a private MCP registry with Azure API Center. Security and Trust: The Foundation of AI Adoption Review and Verification: Public registries, by their open nature, accept submissions from a wide range of developers. This can introduce risks from tools with limited security practices or even malicious intent. A private registry empowers your organization to thoroughly review and verify every MCP server before it becomes accessible to internal developers or AI agents (like Copilot Studio and AI Foundry). This eliminates the risk of introducing random, potentially vulnerable first or third-party tools into your ecosystem. Reduced Attack Surface: By controlling which MCP servers are accessible, organizations significantly shrink their potential attack surface. When your AI agents interact solely with known and secure internal tools, the likelihood of external attackers exploiting vulnerabilities in unvetted solutions is drastically reduced. Enterprise-Grade Authentication and Authorization: Private registries enable the enforcement of your existing robust enterprise authentication and authorization mechanisms (e.g., OAuth 2) across all MCP servers. Public registries, in contrast, may have varying or less stringent authentication requirements. Enforced AI Gateway Control (Azure API Management): Beyond vetting, a private registry enables organizations to route all MCP server traffic through an AI gateway such as Azure API Management. This ensures that every interaction, whether internal or external, adheres to strict security policies, including centralized authentication, authorization, rate limiting, and threat protection, creating a secure front for your AI services. Governance and Control: Navigating the AI Landscape with Confidence Centralized Oversight and "Single Source of Truth": A private registry provides a centralized "single source of truth" for all AI-related tools and data connections within your organization. This empowers comprehensive oversight of AI initiatives, clearly identifying ownership and accountability for each MCP server. Preventing "Shadow AI": Without a formal registry, individual teams might independently develop or integrate AI tools, leading to "shadow AI" – unmanaged and unmonitored AI deployments that can pose significant risks. A private registry encourages a standardized approach, bringing all AI tools under central governance and visibility. Tailored Tool Development: Organizations can develop and host MCP servers specifically tailored to their unique needs and requirements. This means optimized efficiency and utility, providing specialized tools you won't typically find in broader public registries. Simplified Integration and Accelerated Development: A well-managed private registry simplifies the discovery and integration of internal tools for your AI developers. This significantly accelerates the development and deployment of AI-powered applications, fostering innovation. Good news! Azure API Center can be created for free in any Azure subscription. You can find a detailed guide to help you get started: Inventory and Discover MCP Servers in Your API Center - Azure API Center Get involved 💡 Your remote MCP server can be discoverable on API Center’s MCP Discovery page today! Bring your MCP server and reach Azure customers! These Microsoft partners are shaping the future of the MCP ecosystem by making their remote MCP Servers discoverable via API Center’s MCP Discovery page. Early Partners: Atlassian – Connect to Jira and Confluence for issue tracking and documentation Box – Use Box to securely store, manage and share your photos, videos, and documents in the cloud Neon – Manage and query Neon Postgres databases with natural language Pipedream – Add 1000s of APIs with built-in authentication and 10,000+ tools to your AI assistant or agent - coming soon - Stripe – Payment processing and financial infrastructure tools If partners would like their remote MCP servers to be featured in our Discover Panel, reach out to us here: GitHub/mcp-center and comment under the following GitHub issue: MCP Server Onboarding Request Ready to Get Started? 🚀 Modernize your AI strategy and empower your teams with enhanced discovery, security, and governance of agentic tools. Now's the time to explore creating your own private enterprise MCP registry. Check out MCP Center, a public showcase demonstrating how you can build your own enterprise MCP registry - MCP Center - Build Your Own Enterprise MCP Registry - or go ahead and create your Azure API Center today!Introducing API Management Support in the Azure SRE Agent
In May, the Azure SRE Agent was introduced - an AI-powered Site Reliability Engineering (SRE) assistant built to help customers identify, diagnose, and resolve issues across their Azure environments faster and with less manual effort. Today, we’re excited to highlight how the SRE Agent now extends these capabilities to Azure API Management (APIM) , delivering deep operational visibility, guided troubleshooting, and intelligent remediation for customers running critical APIs at scale. API Management sits at the center of API application architectures, acting as a unified entry point for services, enforcing security, transforming requests, and routing traffic to backends. Ensuring the reliability of this layer is crucial - but as systems grow more distributed, it becomes harder to isolate failures, detect misconfigurations, or trace degraded performance to its root cause. The SRE Agent helps APIM users stay ahead of these challenges by providing both diagnostics and remediation tailored for API Management environments. You can ask the SRE agent direct API Management questions or concerns such as: “My API Management is giving me 503 errors” “We updated our policies yesterday, and now the backend is timing out.” “Can you help me figure out why requests to our billing API are failing?” “Show me recent changes to our APIM instance.” “What’s the failure rate on our orders operation this week?” Proactively Monitor API Management App Health The SRE Agent continuously monitors the overall health of your API Management service. It tracks key metrics such as CPU utilization, latency, error rates, and availability over time, surfacing any abnormal patterns and offering insight into capacity. This helps teams anticipate issues before they impact users and plan for scaling with confidence. Visualize Backend Connections and Health One of the most valuable APIM capabilities introduced with the agent is backend mapping. The agent can identify which backend services each API operation routes to, and visualize the health of those backends. This makes it much easier to answer operational questions like: “Which backend is responsible for the spike in errors on my /checkout API?” “Are there any timeouts happening from APIM to service X?” Drill into Backend App Issues If the root cause lies in a backend application - whether it's a service hosted in Azure Container Apps, Azure Functions Apps App Service, or another compute platform - the SRE Agent can go further. It analyzes backend-specific metrics such as memory and CPU usage, response time distribution, recent deployments, and any logged exceptions. The agent correlates this backend behavior with the observed degradation at the API Management layer to provide a full stack view of what’s happening. For example: “Your backend container app failed 37% of requests in the last hour due to out-of-memory errors. This correlated with a 5xx spike at the /stock/check API operation.” Detect and Fix Configuration Issues The SRE Agent also helps uncover common configuration issues that lead to downtime or silent failures, including: Malformed API policies Missing or misapplied network rules (NSGs, VNet) Incorrect scaling configuration or quota enforcement But it doesn’t stop at diagnostics. Where safe and possible, the agent can also perform remediation with your approval - for example, by adjusting NSG rules, scaling your API Management, etc. Built for Teams that Depend on APIM If API Management is critical to your infrastructure, the SRE Agent gives you an extra layer of confidence - offering the clarity and tooling needed to maintain uptime, reduce operational overhead, and catch issues before they escalate. The APIM-specific capabilities of SRE Agent are now available, and can be used in any SRE Agent resource (currently in preview). Signup for preview access We’re excited to bring this level of intelligence and automation to APIM, and we’re looking forward to your feedback as we continue to evolve the experience. Additional resources Azure SRE Agent overview (preview) | Microsoft Learn Introducing Azure SRE Agent | Microsoft Community HubAI Gateway tier of API Management now in public preview
Today, we are introducing the AI Gateway tier of Azure API Management, now in public preview. It gives platform teams a purpose-built experience built specifically for AI workloads - publishing and governing models and MCP servers. Controls are configured through policy cards rather than XML and expressions, and the portal experience and control plane are structured around models, MCP servers, and tools rather than APIs. (For brevity, we refer to the AI Gateway tier as AI Gateway throughout the rest of this article.) AI Gateway is built on Azure API Management, bringing proven operational capabilities to AI workloads. The resource runs in your subscription, uses your Entra tenant, and sends telemetry to destinations you control. The operating model will be familiar to existing API Management customers, but the interface is built around AI workloads. The AI Gateway tier is intended for teams that want this focused experience; other API Management tiers remain the right choice when organizations also need general-purpose API management or capabilities not included in the AI Gateway experience. A practical model for platform teams The AI Gateway gives platform teams a shared place to manage models, MCP servers, policies, and observability destinations, with access controlled through Azure RBAC. For example, a central platform group can connect a set of approved models and tools and publish them for application teams. The application teams can test those assets in the test console and build against them without routing every change through the central group. The platform group still owns the shared guardrails and can see how the assets are being used. After an asset is published, developers can create a named runtime key and begin calling the gateway immediately. Bring the models and tools you already use Most organizations don't standardize on a single model provider. Different models are selected based on quality, latency, cost, geography, or specialized capabilities. The preview supports models from Microsoft Foundry including OpenAI, Anthropic, Mistral, and other Foundry hosted models, as well as models hosted in AWS Bedrock, Google Vertex AI, OpenAI, and Anthropic. A guided wizard simplifies importing models from Microsoft Foundry. Other providers can be added by configuring a connection, with backend authentication configured as part of that connection. All published models are available under the same stable endpoint. Applications continue to use supported API formats such as OpenAI Chat Completions and Responses or Anthropic Messages directly or via SDKs. The AI Gateway extends governance beyond models to the MCP servers and tools agents use to interact with enterprise systems. You can expose an existing MCP server over SSE or Streamable HTTP, turn all or selected operations from a REST API into an MCP server by uploading its OpenAPI specification, or use more than 1,400 connector-backed tools from the Power Platform and Logic Apps library. You can also federate multiple MCP servers behind a single server, so an agent connects once and sees the tools across those servers. Backend authentication supports an API key, OAuth client credentials, managed identity, or mTLS. Governance that's built in Organizations need consistent governance across models and MCP servers without requiring every application team to implement those capabilities independently. The AI Gateway portal presents governance policies through an intuitive card-based experience rather than requiring policy XML. The same policies are expressed as JSON properties, making them easy to manage as infrastructure as code and to audit and enforce across a fleet with Azure Policy. In the public preview, those cards cover request and token rate limits, token quotas, Azure AI Content Safety, and fallback to a secondary model. Policies are applied per asset, making it clear which controls protect each model or MCP server. OpenTelemetry-based token metrics The AI Gateway emits token-usage metrics through OpenTelemetry, with attributes following GenAI and cloud semantic conventions. Metrics can be sent to Application Insights, Datadog, Splunk, Grafana Cloud, or another OTLP endpoint. The portal provides a monitoring view over Application Insights data. Better together: Microsoft Foundry and AI Gateway With AI Gateway, teams can extend the same governance controls, for example token rate limits and quotas, across models hosted in Microsoft Foundry and models hosted elsewhere. Foundry and non-Foundry models are published through gateway-managed endpoints, giving applications and agents a consistent way to access governed models regardless of where they are hosted. Foundry-hosted agents can consume curated sets of tools from Foundry toolboxes, with access to the underlying MCP servers and APIs governed through AI Gateway. Together, Microsoft Foundry and AI Gateway cover the enterprise application lifecycle: Foundry for building and running AI applications, and AI Gateway for publishing, governing, and observing models, tools, and MCP servers across your AI estate. The new AI Gateway tier will soon be available through the gateway experience in Microsoft Foundry portal. We are working toward a seamless, integrated AI Gateway experience within Foundry portal and will share more about that work separately. Available today in public preview The AI Gateway tier is available today at no cost in public preview in East US 2 and Sweden Central. Pricing will be shared separately. To provision a resource, add a model or MCP server, and make a first call click this to go to the AI Gateway tier portal and try it. If you prefer to start from code, use a sample to deploy all the required resources for a Foundry-hosted agent configured to access its model and tools through AI Gateway. We look forward to your feedback as we continue to rapidly evolve AI Gateway.
Expose REST APIs as MCP servers with Azure API Management and API Center (now in preview)
As AI-powered agents and large language models (LLMs) become central to modern application experiences, developers and enterprises need seamless, secure ways to connect these models to real-world data and capabilities. Today, we’re excited to introduce two powerful preview capabilities in the Azure API Management Platform: Expose REST APIs in Azure API Management as remote Model Context Protocol (MCP) servers Discover and manage MCP servers using API Center as a centralized enterprise registry Together, these updates help customers securely operationalize APIs for AI workloads and improve how APIs are managed and shared across organizations. Unlocking the value of AI through secure API integration While LLMs are incredibly capable, they are stateless and isolated unless connected to external tools and systems. Model Context Protocol (MCP) is an open standard designed to bridge this gap by allowing agents to invoke tools—such as APIs—via a standardized, JSON-RPC-based interface. With this release, Azure empowers you to operationalize your APIs for AI integration—securely, observably, and at scale. 1. Expose REST APIs as MCP servers with Azure API Management An MCP server exposes selected API operations to AI clients over JSON-RPC via HTTP or Server-Sent Events (SSE). These operations, referred to as “tools,” can be invoked by AI agents through natural language prompts. With this new capability, you can expose your existing REST APIs in Azure API Management as MCP servers—without rebuilding or rehosting them. Addressing common challenges Before this capability, customers faced several challenges when implementing MCP support: Duplicating development efforts: Building MCP servers from scratch often led to unnecessary work when existing REST APIs already provided much of the needed functionality. Security concerns: Server trust: Malicious servers could impersonate trusted ones. Credential management: Self-hosted MCP implementations often had to manage sensitive credentials like OAuth tokens. Registry and discovery: Without a centralized registry, discovering and managing MCP tools was manual and fragmented, making it hard to scale securely across teams. API Management now addresses these concerns by serving as a managed, policy-enforced hosting surface for MCP tools—offering centralized control, observability, and security. Benefits of using Azure API Management with MCP By exposing MCP servers through Azure API Management, customers gain: Centralized governance for API access, authentication, and usage policies Secure connectivity using OAuth 2.0 and subscription keys Granular control over which API operations are exposed to AI agents as tools Built-in observability through APIM’s monitoring and diagnostics features How it works MCP servers: In your API Management instance navigate to MCP servers Choose an API: + Create a new MCP Server and select the REST API you wish to expose. Configure the MCP Server: Select the API operations you want to expose as tools. These can be all or a subset of your API’s methods. Test and Integrate: Use tools like MCP Inspector or Visual Studio Code (in agent mode) to connect, test, and invoke the tools from your AI host. Getting started and availability This feature is now in public preview and being gradually rolled out to early access customers. To use the MCP server capability in Azure API Management: Prerequisites Your APIM instance must be on a SKUv1 tier: Premium, Standard, or Basic Your service must be enrolled in the AI Gateway early update group (activation may take up to 2 hours) Use the Azure Portal with feature flag: ➤ Append ?Microsoft_Azure_ApiManagement=mcp to your portal URL to access the MCP server configuration experience Note: Support for SKUv2 and broader availability will follow in upcoming updates. Full setup instructions and test guidance can be found via aka.ms/apimdocs/exportmcp. 2. Centralized MCP registry and discovery with Azure API Center As enterprises adopt MCP servers at scale, the need for a centralized, governed registry becomes critical. Azure API Center now provides this capability—serving as a single, enterprise-grade system of record for managing MCP endpoints. With API Center, teams can: Maintain a comprehensive inventory of MCP servers. Track version history, ownership, and metadata. Enforce governance policies across environments. Simplify compliance and reduce operational overhead. API Center also addresses enterprise-grade security by allowing administrators to define who can discover, access, and consume specific MCP servers—ensuring only authorized users can interact with sensitive tools. To support developer adoption, API Center includes: Semantic search and a modern discovery UI. Easy filtering based on capabilities, metadata, and usage context. Tight integration with Copilot Studio and GitHub Copilot, enabling developers to use MCP tools directly within their coding workflows. These capabilities reduce duplication, streamline workflows, and help teams securely scale MCP usage across the organization. Getting started This feature is now in preview and accessible to customers: https://aka.ms/apicenter/docs/mcp AI Gateway Lab | MCP Registry 3. What’s next These new previews are just the beginning. We're already working on: Azure API Management (APIM) Passthrough MCP server support We’re enabling APIM to act as a transparent proxy between your APIs and AI agents—no custom server logic needed. This will simplify onboarding and reduce operational overhead. Azure API Center (APIC) Deeper integration with Copilot Studio and VS Code Today, developers must perform manual steps to surface API Center data in Copilot workflows. We’re working to make this experience more visual and seamless, allowing developers to discover and consume MCP servers directly from familiar tools like VS Code and Copilot Studio. For questions or feedback, reach out to your Microsoft account team or visit: Azure API Management documentation Azure API Center documentation — The Azure API Management & API Center TeamsIntroducing native Service Bus message publishing from Azure API Management (Preview)
We’re excited to announce a preview capability in Azure API Management (APIM) — you can now send messages directly to Azure Service Bus from your APIs using a built-in policy. This enhancement, currently in public preview, simplifies how you connect your API layer with event-driven and asynchronous systems, helping you build more scalable, resilient, and loosely coupled architectures across your enterprise. Why this matters? Modern applications increasingly rely on asynchronous communication and event-driven designs. With this new integration: Any API hosted in API Management can publish to Service Bus — no SDKs, custom code, or middleware required. Partners, clients, and IoT devices can send data through standard HTTP calls, even if they don’t support AMQP natively. You stay in full control with authentication, throttling, and logging managed centrally in API Management. Your systems scale more smoothly by decoupling front-end requests from backend processing. How it works The new send-service-bus-message policy allows API Management to forward payloads from API calls directly into Service Bus queues or topics. High-level flow A client sends a standard HTTP request to your API endpoint in API Management. The policy executes and sends the payload as a message to Service Bus. Downstream consumers such as Logic Apps, Azure Functions, or microservices process those messages asynchronously. All configurations happen in API Management — no code changes or new infrastructure are required. Getting started You can try it out in minutes: Set up a Service Bus namespace and create a queue or topic. Enable a managed identity (system-assigned or user-assigned) on your API Management instance. Grant the identity the “Service Bus data sender” role in Azure RBAC, scoped to your queue/ topic. Add the policy to your API operation: <send-service-bus-message queue-name="orders"> <payload>@(context.Request.Body.As<string>())</payload> </send-service-bus-message> Once saved, each API call publishes its payload to the Service Bus queue or topic. 📖 Learn more. Common use cases This capability makes it easy to integrate your APIs into event-driven workflows: Order processing – Queue incoming orders for fulfillment or billing. Event notifications – Trigger internal workflows across multiple applications. Telemetry ingestion – Forward IoT or mobile app data to Service Bus for analytics. Partner integrations – Offer REST-based endpoints for external systems while maintaining policy-based control. Each of these scenarios benefits from simplified integration, centralized governance, and improved reliability. Secure and governed by design The integration uses managed identities for secure communication between API Management and Service Bus — no secrets required. You can further apply enterprise-grade controls: Enforce rate limits, quotas, and authorization through APIM policies. Gain API-level logging and tracing for each message sent. Use Service Bus metrics to monitor downstream processing. Together, these tools help you maintain a consistent security posture across your APIs and messaging layer. Build modern, event-driven architectures With this feature, API Management can serve as a bridge to your event-driven backbone. Start small by queuing a single API’s workload, or extend to enterprise-wide event distribution using topics and subscriptions. You’ll reduce architectural complexity while enabling more flexible, scalable, and decoupled application patterns. Learn more: Get the full walkthrough and examples in the documentation 👉 hereAnnouncing the Public Preview of the Applications feature in Azure API management
API Management now supports built-in OAuth 2.0 application-based access to product APIs using the client credentials flow. This feature allows API managers to register Microsoft Entra ID applications, streamlining secure API access for developers through OAuth 2.0 authorization. API publishers and developers can now more effectively manage client identity, access, and authorization flows. With this feature: API managers can identify which products require OAuth authorization by setting a product property to enable application-based access API managers can create and manage client applications and assign them access to specific products. Developers can see their registered applications in API management developer portal and use OAuth tokens to securely call APIs and products OAuth tokens presented in API requests are validated by the API Management gateway to authorize access to the product's APIs. This feature simplifies identity and access management in API programs, enabling a more secure and scalable approach to API consumption. Enable OAuth authorization API managers can now identify specific products which are protected by Microsoft Entra identity by enabling "Application based access". This ensures that only valid client applications which have a secure OAuth token from Microsoft Entra identity can access the APIs associated with this product. An application is created in Microsoft Entra corresponding to the product, with appropriate app role. Register client applications and assign products API managers can register client applications, identify specific developers as owners of these applications and assign products to these applications. This creates a new application in Microsoft Entra and assigns API permissions to access the product. Securely access the API using client applications Developers can login into API management developer portal and see the appropriate applications assigned to them. They can retrieve the application credentials and call Microsoft Entra to get an OAuth token, use this token to call APIM gateway and securely access the product/API. Preview limitations The public preview of the Applications is a limited-access feature. To participate in the preview and enable Applications in your APIM service instance, you must complete a request form. The Azure API Management team will review your request and respond via email within five business days. Learn more Securely access product APIs with Microsoft Entra applicationsAzure API Center Plugin for GitHub Copilot for Azure
GitHub Copilot has quickly become a developer’s best friend with its intuitive chat interface and seamless IDE integration. Now, we’re taking it a step further with GitHub Copilot for Azure, a GitHub Copilot extension designed to supercharge your Azure development tasks. 🎉 Introducing the Public Preview of the Azure API Center Plugin for GitHub Copilot for Azure! 🎉 What is a GitHub Copilot for Azure plugin? A plugin extends the capabilities of GitHub Copilot for Azure, allowing for modular customization without altering its core functionality. The API Center plugin for GitHub Copilot enables developers to incorporate Azure API Center context into their workflows. This integration helps tailor the outcomes to better meet specific needs, enhancing the overall development experience by making API creation and management more efficient and aligned with best practices. Key Features of the Azure API Center Plugin With this new plugin, you can effortlessly handle a variety of API-related tasks, making your development process smoother and more efficient: Generating API Specifications: Simply describe your requirements in natural language, and GitHub Copilot for Azure will create new API specifications tailored to your needs. It can also help you register these APIs into API Center swiftly. Designing Compliant APIs: Use GitHub Copilot for Azure to design API specifications that comply with API Center governance. The AI assistance ensures that your APIs are designed according to best practices and standards. Why This Matters The Azure API Center plugin for GitHub Copilot for Azure is a game-changer for developers working on the Azure platform. By integrating AI-driven assistance into your API development workflow, you can: Save Time: Automate the creation and registration of API specifications. Ensure Quality: Design APIs that adhere to best practices and compliance standards. Enhance Productivity: Focus on higher-level tasks while the plugin handles routine API-related tasks. Get Started Today! We invite you to explore the public preview and experience how the Azure API Center plugin for GitHub Copilot for Azure can enhance your development workflow. Join us in this exciting journey to make API development smarter and more efficient! If you have any questions or would like to connect, feel free to reach out to Julia Kasper on LinkedIn.Built-in gateway support for workspaces in Azure API Management
Workspaces in Azure API Management let platform teams hand off API ownership to individual API teams while keeping centralized governance. Until now, using them meant deploying a dedicated workspace gateway on the Premium tier — adding cost, operational overhead, and limiting regional availability. That requirement is going away. Workspaces can now be associated directly with the built-in gateway, and this capability is generally available. What changes Available in more tiers. Use workspaces on the built-in gateway in any API Management tier except Consumption. Available in every region. Create workspaces in any region where API Management is supported. All built-in gateway capabilities apply. Workspaces deployed with the built-in gateway inherit features that dedicated workspace gateways don't offer today, including multi-region deployments, custom hostnames, and Private Link connectivity. Availability Rolling out now to v2 tiers, with Azure portal UI support expected around July. Rollout to classic tiers (Developer, Basic, Standard, Premium) will begin by August and may take up to a few months to complete. Get started Learn more about workspaces and how to deploy them on the built-in gateway.AI Gateway in Azure API Management Is Now Available in Microsoft Foundry (Preview)
For more than a decade, Azure API Management has been the trusted control plane for API governance, security, and observability on a global scale supporting more than 38,000 customers, almost 3 million APIs, and 3 trillion API requests every month. AI Gateway builds on this foundation, extending API Management’s proven governance, security, and observability model to AI workloads, including models, tools, and agents. Today, more than 1,200 enterprise customers use AI Gateway to safely operationalize AI at scale. As customers accelerate AI adoption, the need for consistent, centralized governance becomes even more critical. AI systems increasingly rely on a mix of models, tools, and agents, each introducing new access patterns and governance requirements. Enterprises need a unified way to ensure all this AI traffic remains secure, compliant, and cost-efficient without slowing down developer productivity. Today, we’re making that significantly easier. AI Gateway is now integrated directly into Microsoft Foundry. This gives Foundry users a simple way to govern, observe, and secure their AI workloads with the same reliability and trust as Azure API Management. This integration brings enterprise-grade AI governance directly into Microsoft Foundry right where teams design, build, and operate their AI applications and agents. It provides a streamlined experience that helps organizations adopt strong governance from day one while keeping full API Management capabilities available for advanced configuration. Governance for models With this integration, customers can create a new AI Gateway instance (powered by API Management Basic v2) or associate an existing API Management resource into their Foundry resource. Once configured, all model deployments in the Foundry resource can be accessed through the AI Gateway hostname, ensuring that calls to models, whether to Azure OpenAI or other models, flow through consistent governance and usage controls. Long-term token quotas and short-term token limits can be managed directly within the Foundry interface, enabling teams to set and adjust usage boundaries without leaving the environment where they build and deploy AI applications and agents. Learn more here. Governance for agents The integration also introduces a unified way to govern agents. Organizations can register agents running anywhere — in Azure, other clouds, or on-premises — into the Foundry Control Plane. These agents appear alongside Foundry-native agents for centralized inventory, monitoring, and governance. Teams can view telemetry collected by AI Gateway directly in Foundry or in Application Insights without any reconfiguration of agents at source. Administrators can block agents posing security, compliance, or cost risks within Foundry or apply advanced governance policies, like throttling or content safety, in Azure API Management. Learn more here. Governance for tools Tools benefit from the same consistent governance model. Foundry users can register Model Context Protocol (MCP) tools hosted across any environment and have them automatically governed through the integrated AI Gateway. These tools appear in the Foundry inventory, making them discoverable to developers and ready for consumption by agents. This reduces the operational overhead of securing and mediating tools, simplifying the path to building agentic applications that safely interact with enterprise systems. Learn more here. Unified governance across Foundry and API Management Together, these capabilities bring the power of AI Gateway directly into Microsoft Foundry removing barriers to adoption while strengthening governance. The experience is streamlined with simple setup, intuitive controls, and immediate value. At the same time, customers retain full access to the breadth and depth of API Management capabilities. When advanced policies, enterprise networking, federated gateways, or fine-grained controls are required, teams can seamlessly shift into the API Management experience without losing continuity. With AI Gateway now part of Microsoft Foundry, teams can build and scale AI applications with confidence knowing that consistent governance, security, and observability are built in from the start. AI Gateway in Microsoft Foundry gives every organization a consistent way to govern AI - models, tools, and agents - with the reliability of API Management and the velocity of Foundry. Getting started To set up and use AI Gateway in Foundry, follow the steps in this article. A new AI Gateway deploys an API Management Basic v2 instance for free for the first 100,000 calls. Explore these new capabilities in depth at Microsoft Ignite. Join the Azure API Management and Microsoft Foundry sessions. If attending the conference in person try the hands-on labs to experience how AI Gateway and Foundry help deliver secure and scalable AI applications and stop by our booths to meet the product teams behind these innovations. Session Speaker(s) Link BRK1706: Innovation Session: Build & Manage AI Apps with Your Agent Factory Yina Arenas, Sarah Bird, Amanda Silver, Marco Casalaina https://ignite.microsoft.com/en-US/sessions/BRK1706?source=sessions BRK113: Upskill AI agents with the Azure app platform Mike Hulme, Balan Subramanian, Shawn Henry https://ignite.microsoft.com/en-US/sessions/BRK113?source=sessions BRK119: Don’t let your AI agents go rogue, secure with Azure API management Anish Tallapureddy, Mike Budzynski https://ignite.microsoft.com/en-US/sessions/BRK119?source=sessions LAB519: Governing AI Apps & Agents with AI Gateway in Azure API Management Annaji sharma Ganti, Galin Iliev https://ignite.microsoft.com/en-US/sessions/LAB519?source=sessions
