Batch Inference

Batch inference is the processing of many model requests together in a single run, trading higher latency per request for much greater throughput and lower cost per item.

Grouping requests lets hardware stay fully utilized, which raises throughput and reduces the cost of each result, suiting workloads that do not need an immediate response.

It is well suited to high-volume back-office AI labor where latency is less critical than cost per outcome.