📄 中文摘要
在 LLM 驱动的产品获得市场关注后，随之而来的高昂费用常常让人头疼。处理每日 50 万个请求的流水线，按照 GPT-4o 的定价，月费用可达 1.5 万至 2.5 万美元，且随着使用量的增加，这一数字只会攀升。虽然转向更便宜的模型似乎是解决方案，但这往往会在用户反馈中显现出质量的下降。采用语义缓存、请求批处理和智能模型路由三种技术，可以在不牺牲质量的前提下，将推理成本降低 40% 至 60%。
构建成本高效的 LLM 流水线：缓存、批处理与模型路由
📄 English Summary
Building Cost-Efficient LLM Pipelines: Caching, Batching and Model Routing
As an LLM-powered product gains traction, the associated costs can become overwhelming. A pipeline processing 500,000 requests per day at GPT-4o pricing can easily incur monthly costs of $15,000 to $25,000, and this figure only increases with usage. While switching to a cheaper model may seem like a solution, it often results in quality trade-offs that manifest as user complaints later. Three techniques—semantic caching, request batching, and intelligent model routing—can effectively reduce inference costs by 40-60% without sacrificing quality.