Batch APIs let you submit a large collection of requests for asynchronous processing instead of waiting on a synchronous response, and in exchange providers typically offer a substantial discount off the standard rate. For any workload that does not need an answer in the next few seconds, batching is close to free money, yet many teams never migrate eligible jobs off the real-time endpoint simply because it was the default during prototyping.
How Batch Processing Works
Instead of calling the API once per request and blocking for a response, you submit a file or a list of requests as a single batch job. The provider processes the batch on its own schedule, typically within a bounded time window such as several hours, and returns results as a downloadable set once complete rather than streaming them back individually.
Because batch jobs are processed opportunistically against spare capacity, the turnaround time is variable and not guaranteed to be fast. This is the trade you are making: a lower, more predictable per-token cost in exchange for giving up latency guarantees and real-time streaming.
Which Workloads Are Good Candidates
Bulk classification, tagging, and extraction jobs run against a stored dataset are ideal, since nothing about them requires the result within seconds. Nightly summarization of the day's content, offline evaluation of model outputs against a test set, and one-time data migrations or backfills all fit the same pattern: known input, no user waiting on the other end.
Anything a user is actively waiting on in a live session, like a chat response or an inline code suggestion, is a poor fit regardless of the cost savings, because the latency trade-off directly harms the product experience. Do not batch what a human is staring at a loading spinner for.
Migrating a Job From Real-Time to Batch
Start by auditing your call sites for any job triggered by a cron schedule, a queue worker, or an offline pipeline rather than a live user request. These are the low-risk, high-value candidates to migrate first, since they already tolerate asynchronous execution in your existing architecture.
When migrating, build in a completion check, either polling the batch status endpoint or handling a webhook callback if the provider supports one, and design your downstream pipeline to consume results once the batch finishes rather than assuming an immediate return. This usually requires only a modest refactor of an existing queue-based worker.
Combining Batching With Other Optimizations
Batch processing and prompt caching address different cost levers and stack well together: a batch job that repeatedly uses the same system prompt or reference document across many items can often combine a batch discount on the whole request with a caching discount on the static prefix, compounding the savings.
Batching also pairs naturally with model tiering. Since batch jobs already tolerate delay, they are a low-risk place to test whether a cheaper, smaller model produces acceptable quality for a bulk task before committing to a lower-cost model for your real-time traffic as well.
Key takeaways
- Audit cron jobs, queue workers, and offline pipelines first; they are the easiest batch migration candidates.
- Never batch anything a live user is actively waiting on in the current request.
- Design a polling or webhook-based completion check rather than assuming immediate results.
- Combine batching with prompt caching when a batch job reuses the same static context repeatedly.
- Use batch jobs as a low-risk testing ground for cheaper models before rolling them out to real-time traffic.
Bottom line
Batch APIs are one of the simplest cost levers available because they require no change to model choice or prompt quality, only a willingness to decouple a job from real-time latency. Any team running offline or scheduled LLM work without using batch processing is likely leaving a meaningful discount on the table.