What is batch inference?
Batch inference is running a trained model over many records in one job, usually on a schedule, and writing the results to a table or file for later use. Nothing waits on the answer in the moment. A nightly job scores every open account, every stock item, or every customer, and the business reads the results the next morning from a report, a dashboard, or a field in the CRM.
Because the records are processed together, batch inference uses hardware efficiently. The model loads once, the data streams through, and a GPU or a large CPU server runs at full utilization for an hour and then stops. It fits naturally into an existing data pipeline alongside the nightly loads into a data warehouse or lakehouse.
Why batch inference matters
Most business predictions do not need to be instant. A distributor’s demand forecast for next week, a collections agency’s ranking of which accounts to call, and a utility’s list of transformers most likely to fail can all be produced overnight. Choosing batch inference for these cases avoids the cost and complexity of a service that must be up and fast at all hours. It also makes model updates simple, because the new model runs tomorrow night.
The limits are freshness and reactivity. A score produced at two in the morning does not reflect the payment that arrived at nine. When a decision depends on what happened seconds ago, as in card fraud screening, real-time inference is required. Many deployments use both, with batch scores as the default and real-time inference for the few decisions that need it.