What is an LLM gateway?
A large language model (LLM) gateway sits between AI applications and the models they call. When requests are routed through it, teams can see which application used which model, track usage and cost, and apply configured checks to requests and responses.
Without a shared gateway, teams may need to track and manage model calls from a support chatbot, an internal assistant, and AI agents in different places. As more applications are added, it becomes harder to see overall usage and keep controls consistent. Here is how an LLM gateway works and how it differs from API and MCP gateways.
What an LLM gateway does
A gateway can help manage several parts of a model request:
- Usage and cost: The gateway can record which application or team made a call, which model handled it, and how many input and output tokens were used. Tokens are the units of text a model processes and generates. Many models are billed by token, so tracking input and output tokens makes spending easier to understand and manage.
- Model routing: The gateway sends requests to a configured model and, if set up to do so, can retry with another model when the first is unavailable.
- Caching: The gateway can return a stored response instead of making a new call. Some gateways can match questions with similar meaning, but teams need to decide where reuse is appropriate; an answer that depends on a user’s private data or the latest information should not be served indiscriminately.
- Streaming: Responses pass through as the model generates them.
- Content checks: Guardrails can scan prompts and responses for sensitive data or injected instructions. These checks can help, but they are not a guarantee that every unsafe prompt or response will be caught.
LLM gateway vs API gateway
A standard Application Programming Interface (API) gateway can authenticate callers, route requests, enforce limits, and even inspect request bodies. An LLM gateway adds controls built for model traffic: model selection, token usage, streaming output, and prompt checks.
How is it different from an MCP gateway?
Model Context Protocol (MCP) is a standard that lets AI applications connect to external tools and data sources. An MCP gateway sits on that connection and can help manage which tools are available or how calls to them are handled. An LLM gateway manages traffic to and from the model.
| Gateway | Main traffic | Example question |
|---|---|---|
| API gateway | Calls to an application’s APIs | Is this client allowed to call this endpoint? |
| LLM gateway | Requests to and responses from a model | Which model handled this call, and how many tokens were used? |
| MCP gateway | Connections between an AI application and tools or data sources | Can this agent use this connected tool? |
For example, an agent’s application may send a request to a model. If the model requests a connected tool, the application can route that tool call through an MCP gateway. The application may then send the tool’s result back to the model.
What changes with an LLM gateway?
Without a shared gateway, a team can still build usage tracking, routing, and checks into its applications. But each new application then needs its own setup, and the team has more places to inspect when a cost spike or an unexpected response appears. Routing model calls through one gateway gives the team a consistent place to apply those controls and review what happened.
That is the practical role of an LLM gateway: it makes model traffic easier to manage as AI moves beyond one chatbot or one experiment.
It does not, by itself, control every tool an agent can use or every credential that tool can access. Those are separate decisions to account for when evaluating the wider AI stack.