How it works
A Batch API accepts multiple requests for batch processing instead of requiring an immediate response for each call. The application gathers tasks, sends a set of requests and checks the processing progress. Then it retrieves the results to associate them with the original items.
This pattern is useful when work can happen asynchronously. The application needs to keep identifiers to relate each response to the correct request, handle individual errors and decide what to do if a batch fails or is canceled. The exact input and output format depends on the provider.
OpenAI's Batch API is a specific implementation of this pattern. The official documentation describes sending groups of asynchronous requests in a file, tracking batch status and retrieving results. It cites examples such as evaluation tasks, dataset classification and embedding generation. See the official Batch API documentation for current formats, endpoints and limits.
Batch API does not mean any request can be sent without validation. The application still needs to check which endpoints and models are accepted, how errors are reported and whether the task can wait. Processes that require an immediate response may need common synchronous calls.
Practical example
A team wants to classify a set of customer reviews by topic. Instead of waiting for a response for each review during application usage, it prepares one request per record and sends the set to a batch processing service.
The application keeps its own identifier for each item. When results arrive, it associates each classification with the correct record, checks error responses and forwards ambiguous cases for review. If processing is interrupted, the team can identify which items still need to be handled, without repeating all the work blindly.
A responsible implementation also controls the data sent. The team removes unnecessary information, checks the provider rules and avoids including secrets or personal data without authorization. Results are validated before feeding reports, search systems or business decisions.
For small or interactive tasks, batch operation can add a preparation and waiting step with no benefit. The choice depends on volume, urgency, tolerance for delays and how failures are handled.
What it is for
Batch API is for processing groups of requests that do not need an immediate response. It can be useful for model evaluations, data transformations, vector representation generation, classifications or background administrative tasks, according to the provider's capabilities.
It can simplify control of large work queues, but it does not eliminate pipeline design. It is necessary to track states, retries, limits, errors, duplicates and result reconciliation. An idempotency key or a stable identifier helps avoid duplicate effects when a task is repeated, when the API offers appropriate support.
Before adopting it, identify the maximum acceptable wait time, the expected volume and the impact of partial responses. Test with a small set, check the output schema and set up alerts for failures. Do not use batch processing for a feature that needs to respond to the user during the same interaction.
The term can also refer to a specific provider feature, not a universal pattern. For that reason, confirm the documentation of the chosen API. The general principle is to group deferrable work, track its execution and validate results before using them.