Low-latency inference: Built for production speed

When an agent calls your prediction endpoint, latency matters. A 200ms response might be fine for a dashboard, but an autonomous agent making thousands of decisions per hour needs millisecond-level speed. Predictions on JITM.ai typically respond in under 25ms on the hot path, with the tightest small-model paths landing well below that. Here's what makes that possible.

Model caching

Trained models and their feature engineering pipelines are cached in memory after the first prediction. Subsequent calls skip all I/O because the model is already loaded and ready. Cache invalidation happens automatically when a model is retrained.

Optimised feature pipelines

The sklearn pipeline built during training handles all feature transformation at prediction time (encoding, imputation, scaling) in a single vectorised pass. No Python loops, no redundant copies, no DataFrame overhead.

Honest latency stats

The first call after a model idles out of memory pays a one-time load cost (typically 50-200ms depending on artifact size). That cold-start overhead is excluded from the latency reported in your dashboard and stored in usage stats — we measure and report only the hot inference path, which is what every caller actually experiences after the first request.

Why it matters

Low-latency inference means JITM.ai endpoints slot into real-time systems like trading bots, recommendation engines, fraud detection pipelines, and IoT control loops without adding meaningful latency. Your agent gets answers at the speed of thought.