7 Things Your AI Gateway Should Be Doing in Production (Most Aren't Doing 3 of Them)

Most teams set up an AI gateway the same way they set up a reverse proxy in 2012: route the traffic, add a key, move on. It works until it doesn't — and when it stops working in production, it stops working loudly. An AI gateway is not an API proxy with a language model on the other end. It's the control plane for everything your AI systems do in production: how they access models, how much they spend, how they behave when a provider goes down, what data leaves your infrastructure, and how you debug it when something goes wrong at 2am. The gap between what most AI gateways are doing and what they should be doing is wide. Here are the seven things a production AI gateway needs to do, including the three that most teams haven't gotten to yet — and what it costs them when they don't. 1. Unified Multi-Provider Access With a Single API Contract ✅ Most are doing this This is the baseline. A production AI gateway should give your engineers a single endpoint and a single authentication method