Let me start with an admission. I resisted using an AI gateway for longer than I should have. My reasoning was the kind engineers convince themselves is pragmatic. "I'll just call the APIs directly, it's faster to ship, I'll add abstraction later." And for a while, it worked. Until the night an Anthropic outage knocked my app offline for two hours. Until the morning a recursive agent loop racked up thousands of dollars in charges before anyone woke up. Until the security audit flagged raw API keys scattered across four different repos. At that point, "later" arrived. I've spent the past several months evaluating AI gateways seriously. Not as a researcher, but as someone trying to put them in front of real production workloads. This is what I found. First: What Does an AI Gateway Actually Do? Before the list, let me be specific about what we're talking about, because the category name is increasingly used to mean very different things.…