From MVP to Enterprise: Architecting AI APIs That Don't Fail at 3AM
I've been on-call for enough production incidents to know that the difference between a startup's AI integration and an enterprise one isn't just budget. It's everything downstream — your p99 latency, your failover story, the size of your blast radius when a provider has a bad Tuesday. Most guides lump these two worlds together and that's exactly why teams end up rearchitecting at the worst possible moment.
Let me walk you through how I think about it now, after spending years shipping LLM-backed services for both early-stage teams and Fortune 500 procurement departments. The short version: I almost always route through Global API, and the tier I pick depends entirely on what keeps me up at night.
The Question Nobody Asks First: What Breaks When?
When I sit down with a founder, the conversation usually starts with "which model should we use?" That's the wrong first question. The right first question is: what's your tolerance for a 3 a.m. page?
