Work out feature store size instantly with clear inputs, formula shown and shareable results.
A feature store has two tiers with very different sizes. The online store holds only the latest value per entity, so it is entities times features times bytes and must fit in low-latency storage. The offline store keeps a point-in-time-correct history for training, multiplying that footprint by the retention window, which is why 90 days of history costs 90 times the online tier.
Feature store sizing
online = entities x features x bytes; offline = online x history days; daily growth = one online snapshot
Training sets must be built with the feature values as they were at each event time. Without history, backfilling a training set introduces look-ahead leakage.
Serve only the features models actually consume, use compact types instead of doubles and strings, and expire entities that have been inactive beyond a sensible window.