Model Context Protocol (MCP) is the next stage in the evolution of AI-driven tools like large language models (LLMs). MCP allows an LLM to connect to custom data sources, allowing developers to connect AI agents to interact with external tools and APIs. Even more importantly, MCP allows AI to do things, making it one of the most important components in agentic AI.
The sudden rise in MCP ‘s popularity is creating a need for API gateways that support MCP as well. Integrating MCP into an API gateway allows developers to easily test new environments, format responses, and take advantage of enhanced API security.
Quick Overview of Gateways That Support MCP Gateway Open Source MCP Support Best For Key Strength Azure API Management No Partial Enterprise AI deployments with Microsoft stack Tight Azure integration and compliance APIPark Yes Extensible Lightweight AI apps and prototyping Simple setup and flexible REST proxying Apache APISix Yes Extensible K8s-native LLM routing with plugin flexibility Lua plugins & WASM for AI-centric extensions Kong Konnect Yes Extensible High-scale AI/LLM platform backends Federated APIs & rich plugin ecosystem Solo.io Agent Gateway Partially Strong Model/agent orchestration in service meshes Envoy filters + AI-first request routing Moesif No Companion Monitoring LLM requests & MCP metadata Deep observability for AI API flows WunderGraph MCP Server Yes Strong Frontend-driven LLM composition (BFFs) GraphQL + session/context-aware routing MCP Gateway (Ecosystem) Yes Full Native MCP agent routing and experimentation Protocol-native with session/memory handling Tyk API Gateway Yes Extensible Enterprise-grade contextual API management JS middleware and Open Policy Engine support Gravitee.io Agent Mesh Yes Extensible Distributed AI agent networks and gateways Java policy engine with agent mesh support Azure API ManagementMicrosoft Azure API Management (APIM) is positioning itself at the forefront of API gateways that support MCP. APIM allows developers to connect their own data sources and streamline the process of automating various implementations. It’s particularly adept at managing OAuth tokens and securing endpoints, making it an ideal solution for designers and developers working with MCP who are concerned about cybersecurity.
APIM also allows developers to make use of Azure Functions, Logic apps, or API routing using MCP metadata. This adds an invaluable layer of additional context using routing rules or enrichment layers. It’s an ideal pick for anyone already firmly embedded in Microsoft’s ecosystem or developers looking for scalability, identity federation, or API security.
APIParkAPIPark is an open-source API gateway specifically designed to work with AI-driven tools like LLMs as well as MCP. Its specialized application makes it particularly adept at formatting exchanges between different platforms like AI models or clients like Claude for Desktop.
As an API gateway designed after AI and MCP were already released, APIPark is particularly well-suited for bridging the gap between AI, MCP, and APIs. Not only does it automatically wrap AI prompts into a RESTful format, but it also bundles context into the message. This makes APIPark invaluable for integrating AI-driven processes like translation or sentiment analysis into API workflows.
Last but not least, APIPark features complete lifecycle management, allowing developers to create, manage, monitor, and decommission APIs when they’re no longer needed using one tool.
Apache APISixApache APISix is an open-source API gateway that’s well-known for its plugin architecture and dynamic routing capabilities. It’s one of the few open-source API gateways that offers native support for WebAssembly or allows reconfiguration via etcd. This makes it ideal for injecting MCP headers, modifying payloads, and dynamically shaping API behavior.
Apache APISix even allows you to create custom plugins for extracting and interpreting context metadata, making it ideal for working with MCP. Its flexibility and openness make it a great choice for collaborative teams or anyone deploying MCP servers on Kubernetes environments.
Apache APISix also lets you retrieve and delete resources. It allows you to manage routes, services, and upstream resources from the API gateway.
Kong KonnectKong Konnect recently released the Kong Konnect API Server, an invaluable bridge between Kong Konnect, Kong’s API gateway, APIs, and AI-driven tools like LLMs. This allows users to interact with their APIs using natural language, including all manner of useful customizable filters. Advanced API monitoring is also available using natural language, unlocking the potential for advanced analytics for users with limited technical experience.
Solo.io Agent GatewaySolo.io’s Agent Gateway aspires to be the first complete connectivity solution for AI agents. It achieves this by providing a unified data plane, solving many of the problems caused by decentralized teams and distributed environments. Solo.io Agent Gateway also supports drop-in security solutions, observability, and monitoring for both agent-to-agent and agent-to-tool operations like Agent2Agent (A2A) and Model Context Protocol (MCP). To make a long story short, Solo.io’s Agent Gateway aspires to be a service mesh for agentic AI.
MoesifObservability is an important concept in MCP. Observability for MCP not only lets AI ecosystems know what an MCP server does, but it also lets it know how users are using the tool. This is particularly important for MCP, which is far less constrained by formatted inputs than tools reliant on traditional user interfaces. Usage patterns can be far more complex and hard to monitor and track than other architectures.
Moesif’s API Analytics adds an important context observability component to MCP systems, creating detailed analytics profiles between users and context flows. This allows Moesif to provide traditional API monitoring services like rate limiting or detecting suspicious usage patterns for a particular user despite its complex architecture. Moesif API Gateway also allows detailed analysis of JSON-RPC objects, providing even greater insight into how users are interacting with an MCP server. Moesif API Gateway can be integrated seamlessly with Kong, Tyk, or NGINX.
WunderGraph MCP ServerWunderGraph’s is built to be a different kind of API gateway, being fully customizable and acting as a backend-for-frontend for GraphQL, REST, and gRPC. Bringing all of these features together makes it ideal for adding context to both resources as well as user behavior. Given their unconventional, forward-thinking outlook, it’s little surprise that WunderGraph has been quick to create an MCP Server. This makes WunderGraph especially useful for bridging the gap between AI applications and GraphQL APIs. The emphasis on frontend behavior and multi-user systems also makes WunderGraph’s MCP Server useful for providing and interpreting context.
MCP Ecosystem MCP GatewayMCP Ecosystem MCP Gateway is a lightweight gateway service that can translate any API or MCP Server into an MCP endpoint. While maybe not as robust and fully-featured as some of the other tools we’ve mentioned, it’s still worth mentioning for its accessibility and ease of use. You don’t have to write a single line of code to use MCP Gateway. It’s even available as a pre-built Docker deployment, letting you get started with the tool in a matter of moments if you’re familiar with Docker.
Tyk API GatewayTyk API Gateway offers an API to MCP feature, acting as a bridge between existing APIs and AI assistants like Claude for Desktop or Cursor. This allows users to interact with APIs using natural language, making it ideal for users with limited technical experience. It also allows developers to specify what API endpoints are exposed and configure access control, making Tyk’s API to MCP secure as well as efficient and easy to use.
Gravitee Agent MeshGravitee offers a native tool for integrating with and managing MCP across the enterprise. Gravitee’s Agent Mesh allows both users as well as agents themselves to interact with other agents by using Google’s A2A protocol. This makes agents discoverable as well as making them more autonomous. Finally, Agent Mesh allows Gravitee to perform many of the tasks performed by API gateways, such as user authentication, monitoring, and rate limiting via context-aware guardrails.
Final Thoughts on API Gateways That Support MCPCombining API gateways and MCP offers the best of all worlds. API gateways provide the security and customizability that users need for an API to be effective. Combining API gateways with MCP provides a bridge between traditional API ecosystems and AI-driven tools like LLMs, serving as an essential component for an AI to become truly autonomous.
Here’s a final rundown on what gateway is best for what circumstance:
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