LLM Batch API Offline Inference Production Guide: Reducing Costs with JSONL Queues, custom_id, and Result Backfill
This article explains how to transform non-real-time LLM tasks into a recoverable Batch API offline pipeline, covering JSONL queues, custom_id, status polling, error backfill, result reconciliation, cost budgeting, and a go-live checklist to help teams reduce costs and improve engineering reliability for large-scale LLM tasks.