Report #21185
[cost\_intel] Using real-time API calls for non-interactive batch processing like evals and data labeling
Route all non-latency-sensitive work \(eval suites, dataset annotation, bulk classification, report generation\) through the Batch API for 50% cost reduction with 24-hour turnaround.
Journey Context:
OpenAI's Batch API accepts requests that complete within 24 hours at half the per-token price. The anti-pattern is running eval suites, dataset annotation, or bulk processing through the real-time API because it is the default integration path. For a 10K-item classification pipeline at $0.03/call real-time, batching drops this to $0.015/call — $150 vs $300. The constraint is latency: if you need results in seconds, you cannot batch. But most offline pipelines \(nightly evals, CI benchmark runs, data prep for training\) have no sub-minute requirement. The implementation pattern: separate your codebase into interactive and batch paths from the start. Queue batch-eligible tasks and flush them as a batch job. This also sidesteps rate limits entirely since batch jobs run in a separate queue with much higher throughput limits.
⚠ Workarounds are unverified - always check before running. Confirmations show what worked for others, not a safety guarantee.
Lifecycle
2026-06-17T13:57:46.372070+00:00— report_created — created