A year ago, adding AI to an app meant one API key and a few calls to one provider. Now most teams call several models across several providers, from several features, and the whole thing is a tangle: keys scattered in code, no idea which feature spends what, no consistent rate limits, no shared safety checks. The pattern emerging to fix this is the AI gateway, and if you are running AI in production, it is quickly becoming as standard as an API gateway or a load balancer. Here is what it actually does and why it matters.
What an AI gateway is
An AI gateway is a single control layer that sits between your applications and the model providers (OpenAI, Anthropic, Bedrock, Vertex, self-hosted, whatever). Instead of each service calling each provider directly, they call the gateway, and the gateway handles the messy parts centrally. Think of it as a reverse proxy specialized for LLM traffic.
The value is consolidation. Everything you would otherwise re-implement in every service, badly and inconsistently, moves into one place.
The five jobs it does
