Batch vs Real-Time Inference

Batch inference applies a model to a collection of records, often on a schedule or when a dataset arrives. It can process large volumes efficiently, use elastic compute for a bounded time and write results to storage for later consumption. It fits use cases such as nightly forecasts, periodic risk scoring, recommendation candidate precomputation or document embedding. Freshness is limited by the schedule and data arrival; downstream systems must know which scoring window produced each output. Real-time inference serves one request or a small set within an interactive latency budget. It requires an always-available endpoint, request validation, concurrency handling, autoscaling and robust failure behavior. It can use fresh context, but per-request overhead and idle capacity may make it more expensive. Latency includes network, feature retrieval, preprocessing, model execution and postprocessing, not only model computation. Streaming inference sits between these patterns: events are processed continuously with bounded delay, often using stateful windows. It can keep results fresher than batch while avoiding synchronous request latency, but introduces ordering, late-event, replay and exactly-once or at-least-once concerns. Hybrid designs are common: precompute stable features or candidates in batch, then apply a smaller real-time model using session context. Select the architecture from the decision's time requirement and input availability. If a decision can wait until a scheduled update, batch may reduce serving complexity. If the user must receive a response immediately, real-time may be required. For every pattern, track model version, feature freshness and prediction timestamp. Define fallback behavior if scoring fails and plan for backfills and reprocessing. Compare cost per prediction including infrastructure idle time, data movement and orchestration. Fast inference is not automatically better when stale inputs or failure handling dominate; low-latency services also need service-level monitoring and model-quality evaluation.