Batching
Aggregate requests into small micro-batches (10–30ms windows).
Caching
Cache features + outputs (with TTL) when safe; add cache keys for models.
Autoscaling
Use queue depth + latency as signals; scale to zero when idle.
May 20, 2025•1 min read
ML Inference on a Budget: Batching, Caching & Autoscaling
Keep latency tight and cost low with batching windows, feature caches, and predictive scaling.
