From prototype to production. Infrastructure, monitoring, and scaling AI systems.
The hard parts of making a prototype into a real API.
Managed APIs, self-hosted inference, and the hybrid middle ground.
Rate limits, idempotency, streaming — the API patterns that save you later.
Where AI spend actually goes and where you can cut without regret.
What to trace, log, and alert on when the unit of work is a generation.
Your AI service will fail. These are the patterns for surviving it.
How to run valid experiments when every response is different.
What breaks first, what to batch, and when to switch providers.
