---
title: "Outlier detection | Grafana Labs"
description: "Machine learning that compares the members of a group and flags any that behave differently from the rest."
---

> For a curated documentation index, see [llms.txt](/llms.txt). For the complete documentation index, see [llms-full.txt](/llms-full.txt).

## Outlier detection

If you’re watching average CPU across a 20-node cluster:

- One node at 95% barely moves the cluster average, so nothing looks wrong.
- An alert doesn’t fire, so you don’t know until it affects users.

When you’re trying to determine an instance that is behaving differently from its peers, a fixed threshold can’t help.

[Outlier detection](/docs/plugins/grafana-ml-app/latest/dynamic-alerting/outlier-detection/) solves these kinds of issues.

- You define the group of instances that should behave alike.
- Outlier detection flags any that drift from the rest.

[Several peer instances tracking together while one instance drifts away and is flagged as an outlier](outlier-detection.svg "Several peer instances tracking together while one instance drifts away and is flagged as an outlier")

[Try it out in Grafana Play](https://play.grafana.org/a/grafana-ml-app/outlier-detector)