Batch LLM APIs: Slash Costs on Background Work
Batch LLM APIs explained: how async batch endpoints cut costs by up to 50%, when to use them, and how to design workloads around them.
Batch LLM APIs let you submit a large set of requests that run asynchronously, typically at a 50% discount. For background work — re-scoring, bulk generation, data enrichment — they can cut your bill in half.
What batch means
- You submit many requests in one job.
- The provider processes them asynchronously.
- Results land in hours or a day, not seconds.
- Cost is usually significantly lower than real-time.
When to use batch
- Bulk content generation (drafts, meta, summaries).
- Data enrichment and classification at scale.
- Re-running evals and scoring.
- Backfill and re-processing jobs.
- Anything without a synchronous user waiting.
Designing a batch workflow
- Prepare inputs as JSONL files.
- Submit the batch job.
- Poll for completion or use webhooks.
- Download results and map back to inputs.
- Handle partial failures and retry only failed rows.
Caveats
- Latency is hours, not seconds — no interactive use.
- Different providers have different formats and limits.
- Quotas may be separate from real-time quotas.
- Not all models support batch mode.
FAQ
How much does batch API save?+
Most providers discount batch calls by about 50% compared to real-time. Savings depend on provider and workload.
When should I use a batch API?+
When results aren't needed immediately: bulk generation, enrichment, evals, and backfills.
Can I use batch for chat?+
Not for real-time chat — batch runs take hours. Use real-time endpoints for anything interactive.
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