If your application calls a model provider’s API directly, the provider is baked into your code. Every model change is a code change. Every price rise lands straight on your bill. You find out about an outage when your users do.

An inference gateway is a small service that sits between your application and every model you use. Your code talks to one endpoint. The gateway decides where each request goes.

It’s not exotic. It’s the same idea as a load balancer or an API gateway, applied to models. For a small team it’s usually the first piece of AI infrastructure worth building, because it makes every later decision cheaper.

What it does

One API for every model. Your application speaks one format, usually the OpenAI-compatible chat API that most providers and open-source servers now accept. Behind the gateway that might be a frontier API, an open-weight model through OpenRouter, or a model you host yourself on vLLM. Swapping between them is a configuration change.

Routing. Rules decide which model handles which request. Classification and extraction go to a small, cheap model. Long reasoning goes to a large one. Internal tools can use a cheaper tier than customer-facing features.

Fallbacks. When a provider times out or rate-limits you, the gateway retries against a second provider. Your users see a slightly slower response instead of an error.

Cost tracking. Every request is logged with its model, token counts and cost, tagged by feature or customer. For the first time you can answer “what does this feature cost us per user?” with a number rather than a guess.

Limits and keys. Budgets per team or per feature, and one place to rotate provider keys, rather than secrets scattered across services.

Caching. Identical or near-identical requests can be answered from cache. How much this saves depends entirely on your traffic, so measure before you count on it.

What it doesn’t do

It won’t make a bad prompt good or a small model clever. It adds a hop, so it has to be close to your application and properly monitored. It’s one more service to run, which is why it should be boring: a single container, stateless apart from its log store, deployed the same way as everything else.

Build or adopt

You don’t need to write one from scratch. LiteLLM is the common open-source starting point: it speaks the OpenAI-compatible API, supports most providers and open-weight servers, and handles routing, fallbacks and spend tracking. Managed routers such as OpenRouter cover part of the same ground as a hosted service.

The work isn’t installing it. It’s deciding the routing rules, wiring cost data into something people actually look at, and making sure the gateway itself never becomes the outage.

When it’s worth it

As soon as you use more than one model, or spend enough on inference that someone has asked why. In practice, that’s most teams within a few months of shipping their first AI feature.

It’s also the right first step before any self-hosting decision. Once traffic goes through a gateway, trying an open-weight model on 5% of requests is a configuration change, and you get real cost and quality numbers instead of a guess.

Setting one up is usually the first week of my AI Inference Setup engagement.