If you have been building AI agents for any amount of time, you have probably come across the terms API gateway, LLM gateway, and MCP gateway.
On the surface, they sound like variations of the same thing. In practice, they solve very different problems, and picking the wrong one for the wrong job will cost your team time.
This article breaks down what each gateway actually does, where each one fits in an AI system, and how to decide which ones you need.
Is an MCP, LLM and AI Gateway The Same Thing?
All three are control layers that sit between clients and servers. They handle routing, auth, and observability. That is where the similarity ends.
An API gateway manages regular HTTP/gRPC traffic between services.
An LLM gateway manages calls to language models.
An MCP gateway manages tool and context traffic for AI agents using the Model Context Protocol.
Think of it this way: the API gateway protects and routes "normal app traffic," the LLM gateway governs "thinking traffic," and the MCP gateway governs "acting traffic."
What Is an API Gateway?
An API gateway is the original concept. It sits at the edge of your infrastructure and handles incoming requests to your backend services. Every request goes through it before hitting anything else.
It handles:
Authentication and authorization
Rate limiting and traffic shaping
Request routing to the right service
Load balancing
Logging and monitoring
If you are running a standard microservice platform, you almost certainly have one already. Tools like Kong, AWS API Gateway, and NGINX fit this description.
The API gateway was designed for stateless, request/response HTTP traffic. That design made a lot of sense for REST APIs. It works less well when you need to manage model invocations with token budgets, or stateful sessions between an agent and a tool.
What Is an LLM Gateway?
As teams started calling language models in production, new problems came up that a standard API gateway was not built to handle.
You might route to OpenAI on standard requests, but fall back to Anthropic if rate limits hit. You might want to cache identical prompts so you are not paying to generate the same response twice. You need to track token usage per user, per team, and per model. You need guardrails that inspect both the prompt going in and the response coming out.
An LLM gateway handles all of that. It sits in front of model providers and adds controls that are specific to language model traffic.
It handles:
Model selection and routing (which provider, which model)
Fallback when a provider is unavailable
Token-aware rate limiting
Prompt and response logging
Caching based on request content
Cost tracking per team or user
Content guardrails
Cloudflare AI Gateway, Portkey, and similar tools live in this space.
The key distinction: an LLM gateway controls which brain your agent uses. It operates before the model produces any output.
What Is an MCP Gateway?
After the model decides what to do, something needs to actually do it. That is where MCP comes in.
The Model Context Protocol (MCP) is an open standard for how AI agents communicate with external tools and data sources. It uses JSON-RPC 2.0 messages over transports like HTTP with Server-Sent Events (SSE) or stdio. Connections are stateful, meaning a session persists across multiple tool calls, unlike a standard REST request.
An MCP gateway sits between your agents and your MCP servers (the actual tool implementations). It handles all the agent-to-tool traffic.
It handles:
Routing to the correct MCP server
Authentication for each tool
Session and state management
Streaming via SSE
Access control per agent or team
Audit logging of tool calls
Input validation against JSON-RPC schemas
The MCP specification itself requires explicit user consent and authorization before tool actions. A gateway is the practical place to enforce that.
Without a gateway, every agent manages its own credentials for every tool it uses. You end up with API keys and OAuth tokens scattered across multiple codebases. One compromised agent can expose credentials for every service it touches. An MCP gateway centralizes credential management so agents never handle raw secrets.
A Real Problem the MCP Gateway Solves
Imagine you have five agents: a customer support agent, a sales agent, a data analysis agent, a code review agent, and a scheduling agent. Each one needs access to a different set of tools: Slack, GitHub, Linear, HubSpot, Google Calendar, Jira, and others.
Without a gateway, you have a tangled web of direct connections. Each agent stores its own credentials. Security is only as strong as the least-secured agent. Debugging is painful because you have no centralized view of what tool calls happened.
This is the N x M integration problem: N agents connecting directly to M tools creates connections that are impossible to manage at scale.
An MCP gateway fixes this with a single control point. Every agent connects to the gateway. The gateway handles routing, auth, and visibility for all tool traffic.
Composio's MCP Gateway is built specifically for this. It gives each team a scoped MCP endpoint with the right tools, credentials, and access controls. Engineering, sales, support, and finance teams can each get a gateway endpoint with only the tools they need, with no credential overlap.
How the Three Gateways Work Together
In a production agentic system, all three typically coexist. They operate at different layers:
User Request
|
v
[API Gateway] <-- controls service traffic
|
v
[LLM Gateway] <-- controls which model gets called
|
v
LLM Model
|
v
[MCP Gateway] <-- controls which tools get used
|
v
MCP Tools (GitHub, Slack, Linear, etc.)The API gateway protects your services. The LLM gateway governs the model call. The MCP gateway governs everything that happens after the model decides to act.
For a microservice platform with occasional AI calls, you can often get away with an API gateway and selective AI middleware. Once you have multiple models, multiple agents, and multiple tool backends, the layered architecture becomes necessary.
Capability Comparison
Capability | API Gateway | LLM Gateway | MCP Gateway |
|---|---|---|---|
Request routing | Yes | Yes | Yes |
Auth and access control | Yes | Yes | Yes |
Rate limiting | Yes | Yes, often token-aware | Yes, often session/agent-aware |
Observability | Yes | Yes, often token and cost focused | Yes, often session and tool-call focused |
Streaming / SSE | Sometimes | Common | Core requirement |
Model and provider routing | No | Core feature | Not its purpose |
Prompt guardrails | No | Common | Can inspect tool traffic |
Tool and context brokering | No | Sometimes adjacent | Core feature |
Credential management for tools | No | No | Yes |
Session state management | No | No | Yes |
Security Notes for Each Layer
API gateway security focuses on perimeter controls: who can access your services, what traffic gets through, and how requests are routed.
LLM gateway security adds governance for prompts and responses. It catches unsafe outputs, enforces content policies, and prevents runaway model usage.
MCP gateway security differs in character because the risk surface differs. Tools represent arbitrary code execution and real data access. The MCP specification is explicit: tool actions require user consent and authorization before execution. The OWASP guidance on LLM security identifies prompt injection as one of the highest-risk attack vectors for AI systems. An MCP gateway is a practical enforcement layer for this through input validation, allowlisted actions, PII redaction, and real-time inspection.
Real-World Examples of Each Gateway
API Gateway Tools
These are the tools teams use to protect and route standard service traffic:
Kong is the most widely adopted open-source API gateway. It is built on NGINX with a plugin-driven architecture covering authentication, rate limiting, traffic transformation, and observability. Over 70 official plugins exist. Kong's enterprise offering, Kong Konnect, adds a cloud-hosted control plane and developer portal. It is the go-to choice for platform teams running Kubernetes.
AWS API Gateway is a fully managed service that integrates tightly with Lambda, IAM, and other AWS services. It is the natural pick if your infrastructure already lives on AWS and you want serverless scalability with zero infrastructure management.
Google Apigee is aimed at large enterprises. It covers the full API lifecycle: design, security, analytics, and monetization. It has deep integration with Google Cloud Platform and includes ML-powered traffic analysis.
NGINX is both a web server and a reverse proxy that many teams configure as a lightweight API gateway. It handles extremely high throughput and is the foundation that Kong itself is built on.
Traefik is a cloud-native gateway that integrates directly with Docker, Kubernetes, and other container runtimes. It handles routing configuration automatically based on container labels and is popular in microservice setups.
LLM Gateway Tools
These are the tools teams use to manage, route, and observe language model calls:
Portkey is a feature-rich LLM gateway targeted at enterprises. It supports over 1,600 LLMs through a unified API, includes semantic caching, 50-plus guardrails, prompt versioning, real-time cost tracking, and compliance controls. It is a good fit for teams in regulated industries that need governance over every model call.
LiteLLM is an open-source Python proxy that translates OpenAI-compatible requests to 100-plus providers. It supports per-team budget controls, API key management, and fallback chains. Teams comfortable with self-hosting and Python will find it easy to integrate.
Helicone started as an observability platform and extended into gateway capabilities. It offers request-level tracing, user tracking, cost forecasting, caching, and load balancing. It is a strong choice for teams that want detailed visibility with lower operational complexity than full enterprise platforms.
Cloudflare AI Gateway sits at Cloudflare's edge network. It provides caching, fallback, and usage analytics with no infrastructure to manage. It works well for teams already on Cloudflare that want a production-ready gateway without custom setup.
OpenRouter routes requests across dozens of model providers through a single API. It handles provider selection and fallback automatically. It is the fastest path to multi-model access and is popular for prototyping.
MCP Gateway Tools
These are the tools teams use to govern agent-to-tool traffic over the Model Context Protocol:
Composio is the broadest integration-first MCP gateway. It provides over 1,000 managed integrations and 20,000-plus pre-built tools, unified authentication, and a Tool Router that dynamically discovers and loads the right tools per task through a single MCP endpoint. It is SOC 2 Type II and ISO 27001 certified. Teams that want to move fast without building integration infrastructure from scratch will find it the most practical starting point.
TrueFoundry MCP Gateway focuses on performance and unified control. It adds under 5ms of latency and combines LLM gateway and MCP gateway capabilities into a single control plane. It suits teams that want low-latency tool calls and are comfortable owning their own tool integrations.
Docker MCP Gateway is container-native. It runs tools in isolated, cryptographically signed containers, which makes it appealing for teams with Kubernetes-first infrastructure and strong supply chain security requirements.
IBM ContextForge is an active open-source MCP gateway with full customization. It has a higher setup cost but is well suited to platform teams that want source-level control over their MCP infrastructure.
Lunar MCPX is built for enterprise governance. It provides granular access control, identity-aligned attribution, credential isolation, and end-to-end visibility across the agent stack through Lunar's AI Gateway layer.
Lasso Security approaches MCP from the security angle. It runs real-time behavioral analysis across MCP client connections, detects unsafe patterns, and blocks prompt injection payloads. It is a good fit for organizations already using Lasso for broader LLM security coverage.
When to Use Each One
Use an API gateway when your primary concern is protecting and routing standard service APIs. This applies to almost every production system, regardless of whether AI is involved.
Use an LLM gateway when you work with multiple model providers, need token and cost governance, want prompt caching, or need AI-specific telemetry. If your team is spending money on model calls without visibility into what those calls cost per user, this is where to start.
Use an MCP gateway when you are giving agents access to tools through MCP and need to manage stateful protocol traffic, SSE, sessions, and tool authorization. If agents are connecting directly to tool backends today, this is the layer that makes that production-ready.
Getting Started with Composio
If you are building agentic workflows, Composio sits at the MCP gateway layer with more than 1,000 managed integrations and over 20,000 pre-built tools. It handles authentication, tool discovery, and execution so you can focus on agent logic instead of plumbing.
Here is a basic example of connecting an agent to Composio's Tool Router via MCP using Python:
import asyncio
from composio import Composio
from claude_agent_sdk import ClaudeSDKClient, ClaudeAgentOptions
# Initialize Composio and create a Tool Router session
composio = Composio(api_key="your-composio-api-key")
session = composio.create(user_id="your-user-id")
url = session.mcp.url
options = ClaudeAgentOptions(
permission_mode="bypassPermissions",
mcp_servers={
"tool_router": {
"type": "http",
"url": url,
"headers": {
"x-api-key": "your-composio-api-key"
}
}
},
system_prompt="You are a helpful assistant with access to Composio tools.",
max_turns=10
)
async def main():
async with ClaudeSDKClient(options=options) as client:
await client.query("Create a GitHub issue for the login bug we found")
async for message in client.receive_response():
if hasattr(message, "content"):
for block in message.content:
if hasattr(block, "text"):
print(block.text)
asyncio.run(main())The Tool Router gives your agent a single MCP endpoint that dynamically discovers and loads tools from 500+ integrations based on the task at hand. You are not locked into a fixed set of tools for a given session.
For enterprise teams that need RBAC, audit trails, and SOC 2 / ISO 27001 compliance, Composio's MCP Gateway adds governance controls on top of the integration layer. Each team gets its own scoped endpoint. The gateway handles credential storage, rotation, and policy enforcement centrally.
You can also explore Composio's full toolkit library to see what integrations are available out of the box, from GitHub and Slack to HubSpot, Linear, Jira, and more.
If you are new to MCP and want to understand the protocol from the ground up, Composio's guide to MCP is a good starting point. For a comparison of MCP gateway options by use case and performance, their developer comparison covers the landscape in detail.
Summary
The three gateways are not competitors. They are complementary layers, each owning a specific part of the traffic in an AI system.
The API gateway owns service traffic. The LLM gateway owns model traffic. The MCP gateway owns tool traffic.
If you are building a small system with one model and a couple of tools, you can start with just one or two of these layers. As your system grows, the separation between them starts to matter more, not less. Centralizing auth, observability, and policy at each layer is what lets you scale without the whole thing becoming a security and debugging problem.