I spent months building an LLM scoring pipeline that processed 10,000 job listings a day. It worked beautifully in staging. Then it hit production and the bills started climbing fast.
The problem wasn't the model. The problem was that I had built a demo, not a production system. The gap between "it works" and "it works reliably at scale" is where most AI agent projects die. Founders burn their runway on API bills. Engineering teams ship something that works for the first 100 requests and falls apart by request 1,000.
Here's what I learned about building a reliability layer that actually survives production.
The Cost Explosion Nobody Warns You About
My first mistake was treating the OpenAI API like a utility. I sent prompts, got responses, moved on. No tracking. No budgets. No cost-per-request visibility.
