A fast model does not guarantee a fast prediction. In many production systems, the model waits on feature retrieval: user features from one store, item features from another, recent aggregates from a stream processor, and request context assembled in application code.

Feature serving is the online path that retrieves, computes, validates, and assembles features for inference. It must be fast, correct, and consistent with training.