Batch processing and caching solve different problems. Batching changes how independent requests are submitted and scheduled. Caching reuses work or results. A workload can benefit from either, both or neither, depending on its deadline and repetition.
Use batch work when waiting is acceptable
Examples include an overnight document classification job, a large offline evaluation or a scheduled content transformation. The user does not need each result before making the next decision, so the application can accept asynchronous completion.
OpenAI documents a Batch API for asynchronous groups of requests. Check its current model support, completion window, limits and pricing before planning a job. Those terms are provider-specific rather than a property of all batch systems.
Use caching when something repeats safely
Stable input may qualify for a provider’s prompt cache. A fresh request can still need a new answer, so this does not make every output reusable. Application response caching requires a separate decision about equivalence, permissions and freshness.
A hypothetical nightly job could batch 10,000 independent records while reusing stable instructions where the provider supports that combination. Do not assume two advertised discounts stack: check the applicable billing rules and confirm the observed usage.
Do not batch a live decision chain blindly
If step two depends on the result of step one, they are not independent requests ready to submit together. A browser session that reads a page and then chooses an action has a different shape from a list of offline classification tasks.
You may be able to batch independent preparation or later analysis, but preserve the dependencies of execution. Moving a user-facing confirmation into an unattended batch can change the task’s authorization model. Lower inference cost does not justify that change.
Track completion and partial failure
Assign stable identifiers to inputs and match results back to them. Do not rely on response order. Record which records completed, which failed and which need retry, using the batch provider’s documented result format.
Compare total job cost and completion time at the same quality target. Include result storage, validation and retries. If only a few records failed, a targeted retry is easier to audit than rerunning the entire job and hoping duplicates do not matter.
Build a retry list instead of rerunning everything
For a hypothetical 1,000-record job, suppose 980 records return validated results and 20 have explicit failures. Keep those outcomes by stable record ID and create a retry set from the 20 failures. If the application writes downstream records, make those writes idempotent independently of the model request. A successfully generated response is not proof that its downstream write committed, and an uncertain write should be reconciled before retry.