Cost Control

Go + OpenAI Responses Agent Memory Layering: Short-Term Context, Long-Term Index, and Cost Caps

In production Go agents, the first thing that breaks is usually not model quality. It is memory management: context grows, bills spike, and answers drift. Use a 3-layer memory design: L1: short-term conversational window (seconds) L2: rolling summary (minutes) L3: long-term retrieval memory (days) ...

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. ...