What is an LLM gateway?

An LLM gateway is a single access point that sits between your applications and the various AI models they use, routing requests, enforcing rules, and tracking usage in one place. It's essentially a control tower for all your LLM traffic — one endpoint your apps call, with the messy job of talking to each provider handled behind it.

Why would you put a gateway in front of your models?

As a team starts using several models from different providers, calling each one directly gets messy fast — API keys scattered across services, slightly different code for every provider, and no central view of what anything costs. A gateway fixes this by giving every app one consistent endpoint. Behind that endpoint it routes each request to the right model and can switch providers without the apps needing to change a line.

That single point of control is the whole reason gateways exist.

What does an LLM gateway actually do?

Beyond routing, a gateway adds the guardrails a business needs to run AI responsibly:

• Key management — provider credentials live in one place, not sprinkled through your codebase.
• Cost control — spending limits, usage tracking, and rate limiting so one runaway job can't blow the budget.
• Caching — repeated queries are served from a store instead of paying to regenerate the same answer.
• Logging — a record of every request and response for audits and debugging.
• Safety filters — checks on inputs and outputs before they reach users.
• Failover — automatic switching to a backup model if one provider goes down.

Who actually needs one?

For a single app calling one model, a gateway is overkill — you'd be adding a layer for problems you don't have yet. It earns its place once you're running AI at scale across multiple apps and providers, where central control over cost, security, and reliability matters and vendor lock-in becomes a real risk. At that point a gateway is fast becoming standard infrastructure, the same way API gateways became standard for web services.