Guide

OpenAI Batch API + Go 降本实战：离线拆批、失败重放与成本边界

一句话结论：如果你的调用是可延迟、可批处理、可回放，就该把在线请求下沉到 Batch API；省钱最明显，但前提是你把拆批、失败分流和回放链路先做好。很多团队把 Batch API 当“便宜版同步接口”来用，结果不是省钱，而是把失败样本堆成事故池。真正的保守做法是：先定义成本边界和SLO，再做离线拆批与失败回放。 ...

OpenAI Batch API with Go: Offline Batching, Failure Replay, and Cost Boundaries

Short answer: if your workload is delay-tolerant, batchable, and replay-safe, move it from online calls to Batch API. The savings are real, but only if you design splitting, failure routing, and replay first. Many teams treat Batch API as a cheaper sync endpoint. That usually creates a replay mess instead of stable savings. A conservative rollout starts with cost boundaries and SLOs, then implements offline batching and controlled replay. ...