Why Your AI API Throws CORS Errors (And What to Do About It)
I'll be honest — I've spent more time debugging CORS errors than I care to admit. Last quarter, a single misconfigured header cost my team about six hours of debugging. And the kicker? We were doing nothing exotic. Just calling an LLM from a single-page app. You'd think that in 2026, this would be a solved problem, but fwiw, it isn't.
This isn't a hand-wavy "just set Access-Control-Allow-Origin to *" tutorial. I'm going to walk you through what actually happens under the hood, why the CORS spec exists the way it does (yes, there's an RFC), and how to architect your backend so that your frontend devs stop Slack-ing you at 2 AM.
We're going to do it using Global API as our reference provider because they expose 184 AI models at prices ranging from $0.01 to $3.50 per million tokens, which makes them a great testing ground. But the patterns I describe apply to any vendor.
The CORS Spec in 30 Seconds (Or, "Why Is This Even a Problem?")
