---
title: What is Feature store?
url: https://markin.ai/glossary/feature-store
category: Data and activation stack
---

# Feature store

> A feature store computes, versions and serves the model inputs used in training and in production, guaranteeing that both see the same definition of a feature. It removes training-serving skew, the most common silent cause of models that degrade after launch.

## Why it matters for ARPU

Decisions are only as trustworthy as the features behind them, and a decision layer that cannot reproduce its own inputs cannot explain its own outcomes.

## Related terms

- [Propensity model](https://markin.ai/glossary/propensity-model), A propensity model estimates the probability that a customer takes an action, such as buying, upgrading or cancelling.
- [Signal](https://markin.ai/glossary/signal), A signal is an observed change in customer behaviour, product state, payment health or market context that carries information about future revenue.
- [Real-time decisioning](https://markin.ai/glossary/real-time-decisioning), Real-time decisioning evaluates a customer's current context and returns an action within the latency budget of the moment, typically tens of milliseconds inside an app or call.
- [Hypothesis provenance](https://markin.ai/glossary/hypothesis-provenance), Hypothesis provenance is the complete, inspectable chain behind a decision: which signals raised it, which analysis sized it, who or what wrote the hypothesis, which experiment tested it, and what the causal read-out was.

## Go deeper

- [Markin product](https://markin.ai/product), The data foundation of the layer.
- [Integrations](https://markin.ai/integrations), Where the inputs come from.

Source: https://markin.ai/glossary/feature-store