Batch LLM API for Batch Processing: Slash Costs on Background Work

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

1. Prepare inputs as JSONL files.
2. Submit the batch job.
3. Poll for completion or use webhooks.
4. Download results and map back to inputs.
5. 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