Building Production-Ready OpenAI Integrations: From First API Call to Scalable Architecture

Your proof-of-concept worked beautifully in development. The ChatGPT integration responded in under a second, the code was clean, and stakeholders were impressed during the demo. Then you deployed to production. Within the first hour of real traffic, everything fell apart. Rate limit errors started cascading at 2 PM when users actually needed the feature. Your monthly OpenAI bill projection jumped from $500 to $15,000 because nobody optimized the prompts or implemented caching. Response times ballooned to 30 seconds under load, and users started rage-clicking the submit button—each click spawning another expensive API call. The overnight batch job that worked fine with 100 records now times out at 10,000. This isn't a skill problem. The gap between "working API integration" and "production-ready system" is enormous, and the OpenAI documentation doesn't cover it. Tutorials show you how to make a single API call; they don't show you how to handle 10,000 concurrent users, implement intell