Batch Processing
One-line definition: A method that submits multiple non-urgent AI requests as one asynchronous job. Price and completion time depend on the provider.
Quick Take
- In brief: Batch processing runs many independent requests asynchronously.
- Useful for: Large, non-urgent workloads such as offline evaluation or data labeling.
- Main limit: Latency, limits, and discounts depend on the provider and service.
Overview
Batch processing groups independent requests into an asynchronous job. The provider returns results later, so the caller does not wait for each response in real time. Time limits and prices depend on the provider.
Core Definition
A batch API accepts many independent model requests as one job. The provider processes the items asynchronously and returns a result for each item. The caller checks job status, collects results, and handles item-level errors.
Some providers offer lower prices or a completion window for batch jobs. Those terms belong to that provider. For example, OpenAI currently documents a 50% discount and a completion window of up to 24 hours for its Batch API.
Background and Evolution
Teams use batch jobs when they can trade immediate responses for simpler large-scale processing. Common workloads include evaluation, classification, and data enrichment.
A batch endpoint is not the same as sending many concurrent real-time requests. It usually adds a job state, result retrieval, and item-level error handling.
How It Works
- Prepare independent requests in the provider’s required format.
- Submit the file or request collection and save the returned job ID.
- Poll for status or receive a completion event, if the provider supports one.
- Download the output and error files.
- Match each result to its input and retry only eligible failures.
Providers set their own limits, expiration rules, and completion targets.
Applications in Software Development and Testing
- Offline evaluation: Run a large set of prompts against a model.
- Log classification: Label historical test failures for triage.
- Test data preparation: Create draft fixtures for later review.
- Documentation analysis: Summarize or tag a collection of documents.
Do not merge model-generated code into a repository without review and tests.
Strengths and Limitations
Strengths
- One job can process many independent requests.
- It frees an interactive application from waiting on each item.
- Some providers offer lower prices than real-time endpoints.
Limitations and Risks
- Results do not arrive immediately.
- A job may fail, expire, or return item-level errors.
- Provider limits and pricing differ.
- Results can become stale if source data changes while the job runs.
Comparison with Similar Terms
| Method | How it runs | Best fit |
|---|---|---|
| Batch processing | A group of requests runs as an asynchronous job | Large, non-urgent workloads |
| Flex processing | A supported API request uses a lower-priority service tier | Individual requests that can tolerate delay |
| Concurrent requests | The caller sends separate real-time requests | Work that needs responses as each request finishes |
Check each provider’s job limits and service terms before estimating savings.
Best Practices
- Test the request format with a small sample first.
- Store the job ID and map each item to a stable input ID.
- Track successes and failures for each item.
- Retry only failed items, and make retries safe to repeat.
- Check that the source data is still current before applying results.
Common Pitfalls
Do not retry a whole batch as if nothing succeeded. Record each item status and make retries safe to repeat.
FAQ
When should a team use batch processing?
Use it for independent requests that can finish later, such as offline evaluation or log classification.
Does every batch API offer the same discount or deadline?
No. Prices, limits, and completion windows depend on the provider. Check the current API documentation.