COMPEL Glossary / batch-inference
Batch Inference
Batch inference is the practice of running an AI model's predictions on a large collection of data items simultaneously, rather than processing them one at a time in real time.
What this means in practice
This approach is used when results do not need to be immediate, such as overnight customer segmentation, weekly risk scoring, or periodic report generation. For organizations, batch inference is typically more cost-effective than real-time inference because it can use cheaper compute resources during off-peak hours and process data more efficiently in bulk. In COMPEL, batch versus real-time inference is an architectural decision made during the Technology pillar assessment in the Calibrate stage and implemented during Produce, with cost implications analyzed as part of the AI FinOps practices in Module 3.3.
Why it matters
Batch inference is typically more cost-effective than real-time inference because it leverages cheaper off-peak compute resources and processes data more efficiently in bulk. Organizations that default to real-time inference for all AI workloads accumulate unnecessary infrastructure costs that undermine the economic case for AI. Understanding when batch processing is sufficient enables smarter infrastructure investment and better AI FinOps outcomes.
How COMPEL uses it
Batch versus real-time inference is an architectural decision made during the Technology pillar assessment in the Calibrate stage and formalized during Model as part of the AI platform design. During Produce, batch inference pipelines are implemented for appropriate use cases. The Evaluate stage monitors batch processing reliability and cost efficiency, with cost implications analyzed as part of AI FinOps practices.
Related Terms
Other glossary terms mentioned in this entry's definition and context.