Book: LLM Observability Pocket Guide: Picking the Right Tracing & Evals Tools for Your Team Also by me: Thinking in Go (2-book series) — Complete Guide to Go Programming + Hexagonal Architecture in Go My project: Hermes IDE | GitHub — an IDE for developers who ship with Claude Code and other AI coding tools Me: xgabriel.com | GitHub You have a 1,200-prompt eval set. It runs every night. You hit the regular Messages API in a tight asyncio loop. You manage retries. The rate limiter slaps you halfway through and you wake up to a half-finished CSV. The job had until standup, not five minutes. That is the case the Anthropic Message Batches API was built for. You hand it up to 100,000 requests in one POST. The docs describe most batches finishing in less than 1 hour, with a hard 24-hour expiration on anything that does not. You pay 50% of the standard token rate for everything in the batch. Same model, same outputs. Different endpoint. The trade is latency.…