Building Production-Ready GPT Integrations: Error Handling, Rate Limits, and Cost Control

Your GPT-powered feature works perfectly in development. Then it hits production: rate limits crush your throughput, a single malformed response crashes your service, and your monthly API bill looks like a phone number. The gap between a working prototype and a production-ready GPT integration is wider than most engineers expect. I've watched teams ship GPT features that sailed through staging, only to implode within hours of launch. The failure modes are predictable but rarely anticipated. A burst of traffic triggers rate limiting, which triggers retries, which amplifies the rate limiting into a cascade that takes down your entire service. A response comes back with an unexpected JSON structure—maybe the model decided to add helpful commentary outside the expected format—and your parser throws an unhandled exception. Your carefully tuned prompts that cost $0.02 per request in testing suddenly cost $0.15 when real users ask questions three times longer than your test fixtures. Traditio