AI Gateway, MCP Gateway, API Gateway — What's the Difference?

Introduction

With the rise of large language models (LLMs) and autonomous AI agents, we've seen terms like AI Gateway, MCP Gateway, and traditional API Gateway increasingly used in overlapping contexts. While each may emphasize different capabilities or target different users, under the hood, they share a common architectural foundation: they are all API gateways.

This article demystifies the terminology and clarifies why general-purpose API gateways—especially open-source, cloud-native ones like Apache APISIX—can fully support the needs of AI and MCP-based traffic without requiring an entirely new gateway category.

Terminology Breakdown

What Is an API Gateway?

An API gateway is a reverse proxy that sits between clients and services, providing:

• Request routing
• Authentication and authorization
• Rate limiting and traffic shaping
• Observability (logs, metrics, tracing)
• Caching, transformation, and more

What Is an MCP Gateway?

An MCP (Model Context Protocol) gateway is a specialized application of an API gateway designed to handle traffic to an MCP server. MCP is a session-based protocol (over HTTP or stdio) used by LLM agents to interact with context-aware backends.

Common MCP gateway responsibilities:

• Preserve SSE (server-sent events) streaming
• Authenticate agents via OIDC or API Key
• Enforce per-agent session quotas
• Retry or route based on upstream health

What Is an AI Gateway?

An AI gateway is a broader term encompassing gateways that:

• Interact with LLMs via REST or streaming APIs
• Route between multiple AI providers (OpenAI, DeepSeek, Claude, etc.)
• Provide fallback and retry logic across LLMs
• Track token usage, latency, and error rates

Architectural Similarities

Despite the differing labels, these gateways share common architecture and traffic patterns.

sequenceDiagram
  participant Agent
  participant Gateway
  participant LLM as LLM Provider (e.g., OpenAI)
  participant MCP as MCP Server

  Agent->>Gateway: POST /v1/request
  Gateway->>Gateway: AuthN/AuthZ, Rate Limit

  alt AI API (e.g. OpenAI, DeepSeek)
    Gateway->>LLM: Forward request
    LLM-->>Gateway: Streamed or batched response
  else MCP Session
    Gateway->>MCP: Forward MCP request
    MCP-->>Gateway: Streamed SSE or context-aware response
  end

  Gateway-->>Agent: Response relayed

Core Components in All Gateway Types

Feature	API Gateway	MCP Gateway	AI Gateway
Authentication	✅	✅	✅
SSE/Streaming Proxy	🔶 (Optional)	✅	✅
Rate Limiting	✅	✅	✅
Retry/Failover	✅	✅	✅
Plugin-based Control	✅	✅	✅

Note: 🔶 means available via plugin or config, not always enabled by default.

How Apache APISIX Serves All Three

Apache APISIX is a cloud-native API gateway with first-class support for:

• AI plugins for LLMs
• Traffic Management plugins
• Proxy MCP server

Whether you call it an AI Gateway or MCP Gateway, APISIX offers the building blocks required.

Flow with Retry and Fallback

sequenceDiagram
  participant Agent
  participant APISIX Gateway
  participant OpenAI
  participant DeepSeek
  Agent->>APISIX Gateway: /v1/ai/chat
  APISIX Gateway->>OpenAI: Forward request
  OpenAI-->>APISIX Gateway: 5xx Error
  APISIX Gateway->>DeepSeek: Retry request
  DeepSeek-->>APISIX Gateway: Response streamed
  APISIX Gateway-->>Agent: Final response

With plugin chaining and upstream control, APISIX supports multi-backend AI traffic handling.

Best Practices

1. Use a General-Purpose API Gateway

Avoid building your own 'AI gateway' from scratch. Start with a proven gateway and configure the behavior you need.

2. Preserve Streaming Semantics

For LLM workloads, ensure the gateway supports Transfer-Encoding: chunked and SSE headers.

3. Secure by Default

Use mutual TLS, API keys, or OIDC for agent and user authentication.

4. Retry Intelligently

Use plugin-based retries only for idempotent requests or when agent state is preserved.

5. Use Logs and Metrics

Expose gateway observability via Prometheus, SkyWalking, or Zipkin for debugging and scaling insights.

Conclusion

AI gateways and MCP gateways are not fundamentally new technologies—they are domain-specific applications of the well-established API gateway model. By leveraging a mature gateway like Apache APISIX, teams can support AI traffic, LLM agents, and session-based protocols like MCP with robust, extensible features.

Instead of reinventing the wheel, treat these gateways as specialized routes and plugins on top of an API gateway core. This approach saves time, ensures reliability, and keeps your architecture consistent across traditional APIs and AI-native protocols alike.

Next Steps

Stay tuned for our upcoming column on the API gateway Guide, where you'll find the latest updates and insights!

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