MCP Gateway vs LLM Gateway: Don't Confuse Them
Short answer: an LLM gateway sits between your application and the model providers, routing and governing model API calls. An MCP gateway sits between your agent and its tools, routing and governing tool calls. Both are called "AI gateways" in marketing, several vendors sell both in one product, and buyers regularly evaluate one when they need the other. Ten minutes here saves that meeting.
What an LLM gateway does
An LLM gateway is a proxy for model traffic. Your apps call one API, and the gateway forwards to OpenAI, Anthropic, Gemini, or your own hosted models behind it. The value is aggregation and control on the model side:
- One API surface for many providers, so swapping models does not mean rewriting apps
- Routing and failover (send this workload to that model, fall back when a provider is down)
- Cost controls: per-team budgets, rate limits, usage analytics per key
- Caching of repeated prompts, logging of requests
Established examples include LiteLLM's proxy and the LLM sides of Kong's AI Gateway and agentgateway. The unit of work is a model request: prompt in, completion out.
What an MCP gateway does
An [MCP gateway](/blog/what-is-an-mcp-gateway) is a proxy for tool traffic. Your agent connects to one MCP endpoint, and behind it the gateway federates many MCP servers: merged tool catalog with namespaces, brokered credentials per upstream, per-connection revocation, an audit trail of what the agent did. The unit of work is a tool call: the agent invokes crm__search_contacts, the gateway routes it to the CRM's server with the right token, the result comes back.
Different layer, different risks, different buyers' questions. An LLM gateway answers "which models are we calling and what does it cost?" An MCP gateway answers "which systems can our agents touch, with whose credentials, and what did they do?"
The table to keep them straight
| LLM gateway | MCP gateway | |
|---|---|---|
| Sits between | Your apps and model APIs | Your agents and their tools |
| Governs | Model requests (prompts, completions) | Tool calls (actions on real systems) |
| Typical features | Routing, failover, caching, spend limits | Federation, credential vault, revocation, audit |
| Cost lever | Model choice, caching, budgets | Tool-schema load, and (rarely) memory |
| Failure it prevents | Provider outage, runaway model spend | Credential sprawl, unaccountable agent actions |
| Examples | LiteLLM proxy, Kong AI Gateway (LLM side) | Docker MCP Gateway, IBM ContextForge, Tulimoa |
Why the confusion exists
Three reasons, all observable in the 2026 market.
Vendors do both under one name. Kong governs "LLM, MCP, and agent-to-agent traffic with the same Kong AI Gateway". LiteLLM added MCP governance inside its LLM proxy. agentgateway advertises "one high-performance gateway for service, LLM, and MCP traffic". Real products, deliberately converged, and the umbrella term "AI gateway" flattens the distinction.
Names mislead. Glama, known for its MCP server directory, sells a "gateway" that is an OpenAI-compatible LLM gateway (model access, cost analytics, prompt caching), not an MCP gateway. Nothing wrong with the product; everything wrong with assuming the word means the tool layer.
Both promise cost control. LLM gateways cut spend via routing and caching. MCP gateways cut it via the tool-schema side (what we call [the context tax](/blog/the-context-tax)) and, in our case, via memory. Same word "cheaper", different mechanisms, easy to conflate in a slide.
Which one do you need?
Answer with the pain, not the category name.
- Model bills are opaque, you juggle providers, you want failover: LLM gateway.
- Agents reach real tools, credentials are multiplying, nobody can say what the agent did: MCP gateway. Our [honest decision guide](/blog/do-you-need-an-mcp-gateway) covers when that moment arrives.
- Both pains, one platform preference: the converged products above are credible; evaluate each half on its own merits rather than assuming excellence transfers.
One more distinction while we are at it: an MCP gateway is also not the same thing as a single MCP server, a confusion we untangled in [MCP gateway vs MCP server](/blog/mcp-gateway-vs-mcp-server).
For completeness and with the bias stated: [Tulimoa](/gateway) is an MCP gateway, tool layer only, with a memory layer attached, in early beta. If what you need is model routing, we are genuinely not your product.
Frequently asked questions
Is an AI gateway the same as an MCP gateway?
"AI gateway" is the umbrella marketing term and usually means the LLM side, sometimes both. When a vendor says AI gateway, ask which traffic it governs: model requests, tool calls, or both. The answer sorts the product in one sentence.
Can one product be both an LLM gateway and an MCP gateway?
Yes, and several are (Kong, LiteLLM, agentgateway). The layers are adjacent, not identical: evaluate the model-routing half and the tool-governance half separately, because being good at one implies nothing about the other.
Does an MCP gateway route my model API calls?
No. Your agent's model traffic goes wherever it went before. The MCP gateway only sees the tool calls the agent makes, which is exactly why its audit trail is about actions, not prompts.