📄 中文摘要
许多团队尝试通过减少提示、降低 max_tokens 或切换到更便宜的模型来降低 OpenAI 账单，但这种方法往往只能维持一段时间，随后回答质量下降，支持请求增加，最终团队又会恢复原状。问题不在于降低成本，而在于缺乏诊断模型。如果无法明确支出来源、哪些工作负载需要质量余地以及成功的标准是什么，所谓的“优化”实际上只是预算驱动的降级。提供了一个实用的审计框架，帮助团队在不损害质量的前提下降低成本，框架包括：首先定义成功，其次按阶段分解支出等步骤。
如何在不影响质量的情况下降低 OpenAI 账单：实用审计框架
📄 English Summary
How to Reduce OpenAI Bill Without Hurting Quality: A Practical Audit Framework
Many teams attempt to reduce their OpenAI bills by cutting prompts, lowering max_tokens, or switching to cheaper models, but these strategies often only work temporarily. As answer quality declines and support escalations increase, teams tend to revert to their original spending. The issue lies not in cost reduction itself, but in the absence of a diagnostic model. Without understanding the sources of expenditure, which workloads require quality headroom, and what guardrails define success, optimization efforts lead to budget-driven degradation. A practical audit framework is provided to help teams reduce costs without compromising quality, which includes defining success first and decomposing spend by stage.