Batch processing guide
Batch API Cost Calculator Guide
Batch APIs are useful when a large AI job can wait. Estimate the normal interactive cost first, then apply the current batch pricing rules only to requests that can run asynchronously.
Good batch workloads
Batch processing fits non-urgent work such as document summarization, data extraction, offline classification, evaluation runs, translation backlogs, nightly enrichment, and synthetic test generation. It is a poor fit for chat, live search, user-facing agents, or any flow where the user expects an immediate answer.
The batch cost formula
Planning formula
Interactive baseline = input token cost + output token cost for every request
Batch estimate = eligible input token cost + eligible output token cost at the provider's current batch rate
Total project cost = batch estimate + real-time requests + validation reruns + storage or orchestration overhead
Use the calculator to estimate input and output tokens per item, then multiply by the number of records in the job. Keep input and output separate because output-heavy jobs can still be expensive even after a batch discount.
Collect these inputs
- Records per batch: documents, rows, tickets, images, or test cases processed together.
- Average input tokens: prompt instructions plus the record content sent for each request.
- Expected output tokens: summaries, JSON fields, labels, extracted evidence, or rewritten text.
- Failure and rerun rate: invalid JSON, policy refusals, low-confidence extraction, or duplicate jobs.
- Latency tolerance: whether the product can wait for the provider's batch turnaround window.
Example
A nightly support-ticket job processes 40,000 records. Each record sends 1,200 input tokens and asks for 180 output tokens. Before reruns, the job uses 48 million input tokens and 7.2 million output tokens. If 6% of records need a corrected retry, add another 2.9 million input tokens and 432,000 output tokens before applying provider pricing.
Compare batch, cache, and real time
Batch pricing and prompt caching solve different problems. Batch helps when the work can run later. Caching helps when many real-time requests reuse the same prefix. For a backlog with repeated instructions, estimate both options separately and confirm whether the provider allows any discounts to stack before assuming combined savings.
Some workflows should be split: run historical backfills through batch, then keep new user-facing traffic on a real-time endpoint. Track the two paths separately so launch traffic does not hide the true cost of the backlog.
Operational checklist
- Validate prompts on a small sample before submitting the full job.
- Store stable job IDs so the same records are not submitted twice.
- Keep enough source metadata to audit low-quality outputs later.
- Budget for a human or automated review pass when extraction accuracy matters.
- Check current provider docs before purchase decisions because supported models, rate limits, and pricing can change.
Official pricing references
Confirm the latest details in the OpenAI Batch API guide, Claude batch processing guide, and Gemini Batch API guide.