Building LLM-powered applications starts simple. You pick a model, connect an API, and ship a feature. Maybe it’s a chatbot, a summarizer, or an internal tool. At this stage, everything feels manageable. Then things grow. Another team wants to use a different model. Someone asks for cost tracking. Security wants to know where data is going. A provider has an outage, and suddenly your system depends on a single external service. What started as a straightforward integration turns into a scattered setup of API keys, inconsistent logging, and unclear ownership. This is where AI Gateways come in. They’re not just another layer of infrastructure  they’re what make LLM systems manageable once you move beyond a single team or use case. In this article, we’ll break down what to look for in an AI Gateway and compare seven platforms that teams are using today. What an AI Gateway Actually Does At a high level, an AI Gateway sits between your applications and your model providers.…