What is MetricFlow used for?

MetricFlow is a semantic layer from dbt Labs that defines metrics in code and compiles requests into optimized, reusable SQL with consistent dimensions and joins. It is a good fit for a governed metrics layer for BI tools, and consistent KPIs across dashboards and notebooks.

Is MetricFlow open source?

Yes. MetricFlow is open source under the Apache-2.0 licence. Its source code is on GitHub at dbt-labs/metricflow and is written mainly in Python.

Yes. MetricFlow is open source, so the software itself is free to use.

What is MetricFlow an alternative to?

MetricFlow is an open-source alternative to Looker and AtScale. Other open-source alternatives to Looker include Cube, Saiku and Quary.

Is MetricFlow actively maintained?

Yes. The most recent commit to MetricFlow was on 29 September 2026, and the latest release is v0.213.0, published on 10 September 2026. The project has 1.8k stars on GitHub.

MetricFlow: open-source alternative to Looker, AtScale

About MetricFlow

MetricFlow is a semantic layer that makes it simpler to define and manage metrics in code. It compiles those metric definitions into clear, reusable SQL, so results stay consistent and accurate when analyzed by relevant attributes, or dimensions. It is maintained by dbt Labs, written in Python, and released under the Apache-2.0 license.

The name reflects its method: a request for a metric is compiled into a dataflow-based query plan that is optimized and translated into engine-specific SQL. It helps with complicated logic such as multi-hop joins between fact and dimension sources and complex metric types, generating queries dynamically instead of requiring hand-written SQL for every cut of the data.

Documentation is hosted in the dbt docs, and a changelog tracks updates. MetricFlow suits analytics engineers and data teams who want a single source of truth for business metrics that can feed dashboards, notebooks, and other tools.

Key features

Metric definitions managed as code
Compiles metrics to engine-specific SQL
Dataflow-based query planning
Multi-hop joins across fact and dimension sources
Support for complex metric types
Consistent dimensions across queries

Good fit for

→A governed metrics layer for BI tools
→Consistent KPIs across dashboards and notebooks

Built with: Python
Tags: semantic-layer; metrics; dbt; analytics; sql; data-modeling; business-intelligence; python

MetricFlow: questions and answers

What is MetricFlow used for?: MetricFlow is a semantic layer from dbt Labs that defines metrics in code and compiles requests into optimized, reusable SQL with consistent dimensions and joins. It is a good fit for a governed metrics layer for BI tools, and consistent KPIs across dashboards and notebooks.
Is MetricFlow open source?: Yes. MetricFlow is open source under the Apache-2.0 licence. Its source code is on GitHub at dbt-labs/metricflow and is written mainly in Python.
Is MetricFlow free?: Yes. MetricFlow is open source, so the software itself is free to use.
What is MetricFlow an alternative to?: MetricFlow is an open-source alternative to Looker and AtScale. Other open-source alternatives to Looker include Cube, Saiku and Quary.
Is MetricFlow actively maintained?: Yes. The most recent commit to MetricFlow was on 29 September 2026, and the latest release is v0.213.0, published on 10 September 2026. The project has 1.8k stars on GitHub.