How to Reduce OpenAI Bill Without Hurting Quality: A Practical Audit Framework

Most teams try to reduce an OpenAI bill by cutting prompts, lowering max_tokens, or swapping to a cheaper model. That sometimes works for a week. Then answer quality drops, escalations rise, and the team quietly puts the cost back. The problem is not cost reduction. The problem is cutting cost without a diagnostic model. If you do not know where spend comes from, which workloads need quality headroom, and what guardrails define success, your "optimization" is just budget-driven degradation.