AI APIs

AI APIs is the operational reality most teams face before they ever think about training a model. Whether you are calling OpenAI, Anthropic, Google Gemini, Azure OpenAI, Mistral, Cohere, xAI, or one of the open-weight inference providers, the day-to-day work is managing latency, cost, rate limits, streaming, tool calling, JSON mode, structured outputs, safety filters, and the quirks that each provider's implementation of the same spec has accumulated.

This track covers each major provider in enough depth to get a production system running and stable. Beyond the per-provider lessons we cover the cross-cutting patterns that matter regardless of vendor: retries with jitter, circuit breakers, fallback routing, response caching, token accounting, and how to design your code so that switching providers is a two-hour job instead of a two-week migration. Lock-in on an AI API is real, and it is almost always avoidable if you plan for it on day one.