An OpenAI API integration looks trivial in a prototype and turns out to be an engineering project in production. The proof of concept takes an afternoon: install the client library, paste a key, send a prompt, get a useful answer back. Then someone asks what happens when the request times out, who pays when a customer pastes a hundred-page contract into the box, and whether last quarter's invoices just left the building inside a system prompt.
This guide is about that second phase. It covers where the API belongs in an existing architecture, how to keep company data contained, how to stop costs running away from you, and how to know whether the feature is actually working. The audience is teams with a real application already in production, not a blank repository.
In short: A production OpenAI API integration is mostly ordinary engineering. Put the API behind your own backend, never in the browser. Pin a specific model version, cap what a single request can consume, treat the provider as an unreliable network dependency with retries and fallbacks, and measure output quality against a fixed set of test cases before and after every prompt change.
What OpenAI API Integration Actually Involves
