> ## Documentation Index
> Fetch the complete documentation index at: https://docs.datafold.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Monitor Types

> Monitoring your data for unexpected changes is one of the cornerstones of data observability.

> [!TIP]
>
> Data Monitors functionality is available via [MCP](https://docs.datafold.com/classic/api-reference/mcp-server-setup.md) — connect your AI assistant to Datafold and manage monitors directly from your development environment.

Datafold supports all your monitoring needs through a variety of different monitor types:

1. [**Data Diff**](https://docs.datafold.com/classic/data-monitoring/monitors/data-diff-monitors.md) → Detect differences between any two datasets, within or across databases
2. [**Metric**](https://docs.datafold.com/classic/data-monitoring/monitors/metric-monitors.md) → Identify anomalies in standard metrics like row count, freshness, and cardinality, or in any custom metric
3. [**Data Test**](https://docs.datafold.com/classic/data-monitoring/monitors/data-test-monitors.md) → Validate your data with business rules and see specific records that fail your tests
4. [**Schema Change**](https://docs.datafold.com/classic/data-monitoring/monitors/schema-change-monitors.md) → Receive alerts when a table schema changes

Monitors can also be created, run, and managed via the [Datafold API](https://docs.datafold.com/classic/api-reference/introduction.md) or as [code](https://docs.datafold.com/classic/data-monitoring/monitors-as-code.md).

If you need help creating your first few monitors, deciding which type of monitor to use in a particular situation, or developing an overall monitoring strategy, please reach out via email ([support@datafold.com](mailto:support@datafold.com)) and our team of experts will be happy to assist.


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