---
title: "Feature stores explained | Snowplow Signals"
description: "Online and offline features, training and serving skew, and where a warehouse stops."
source: https://signals.snowplow.io/learning/feature-stores
---

Learning Center

# Feature stores

A feature store computes and serves the inputs a model reads, the same way in training and in production. These explainers cover what it is, how online and offline features differ, where a data warehouse stops, and why the freshness of a feature decides what it can act on.

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## In this topic

[What is a feature store?A feature store is the system that computes and serves the features a model uses, in training and in production, from one definition. It exists so the values a model learns from match the values it reads when it runs.](https://signals.snowplow.io/learning/feature-stores/what-is-a-feature-store) [Online vs offline featuresOffline features are computed in bulk over historical data and used to train a model. Online features are the current values served to that model when it runs. The two describe the same thing at different points in time.](https://signals.snowplow.io/learning/feature-stores/online-vs-offline-features) [What is training-serving skew?Training-serving skew is the gap between the features a model was trained on and the features it reads in production. It makes a model perform worse than it did in testing, with no visible failure.](https://signals.snowplow.io/learning/feature-stores/what-is-training-serving-skew) [Feature store vs data warehouseA data warehouse holds large volumes of history and answers analytical queries. A feature store serves individual feature values to a model with low latency. A warehouse is where offline features are computed; it is not where online features are served.](https://signals.snowplow.io/learning/feature-stores/feature-store-vs-data-warehouse) [What is an online feature store?An online feature store is the part that serves fresh feature values to a model at request time. It holds the current value of each feature, ready to read within the latency budget of a live prediction.](https://signals.snowplow.io/learning/feature-stores/what-is-an-online-feature-store) [What is feature freshness?Feature freshness is the lag between something happening and the served feature reflecting it. The fresher a feature, the more recent the behavior a model can act on. Stale features make a model answer for a customer who has already moved on.](https://signals.snowplow.io/learning/feature-stores/what-is-feature-freshness) [Do you need a feature store?Not every team needs a feature store. The case for one grows with the number of models and features you run, the need to reuse features across them, and the cost of training-serving skew. For a single simple model, it can be overkill.](https://signals.snowplow.io/learning/feature-stores/do-you-need-a-feature-store) 

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