MetricFlow

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What MetricFlow is

MetricFlow is a free, self-hosted tool from dbt Labs for defining metric logic and generating SQL to query metrics across data dimensions. Teams define metrics and semantic models in YAML within a dbt project. Supported metric types include simple, conversion, cumulative, derived, and ratio metrics. MetricFlow uses entity types and parameters to construct joins and avoid fan-out and chasm joins. It is compatible with dbt 1.6 and higher; documented data platforms include Snowflake, BigQuery, Databricks, Redshift, and Postgres for dbt v1 only. Use requires a working dbt project and adapter. The Apache 2.0-licensed package is available through PyPI as `dbt-metricflow`. It does not currently support dbt built-in functions or packages. Custom integrations can use JDBC, ADBC, GraphQL, and the Python SDK. The separate dbt Semantic Layer lists integrations including Power BI, Tableau, Google Sheets, Excel, Omni, Hex, Mode, and Sigma. Hosted access to that layer is listed for dbt Starter, Enterprise, and Enterprise+; Starter costs $100 per user/month.

Who it suits

MetricFlow suits teams with a working dbt project and adapter that want to define metrics in YAML and generate SQL across data dimensions. It also offers integration options for teams building custom connections.

What it does well

Supports five metric types.
Constructs joins using entity types and parameters.
Works with dbt 1.6 and higher.
Custom integrations can use four listed interfaces.
Free and Apache 2.0 licensed.

What to know first

Requires a working dbt project and adapter.
Does not support dbt built-in functions or packages.
Postgres support is for dbt v1 only.

Limits

MetricFlow requires a working dbt project and adapter and does not currently support dbt built-in functions or packages. Hosted access through the dbt Semantic Layer is listed for dbt Starter, Enterprise, and Enterprise+; Starter is $100 per user/month.

Plans

Plan	Billed and limits	Price
MetricFlow	Apache 2.0 licensed · requires a working dbt project and dbt adapter	Free
Developer	1 developer seat · 1 project · 3,000 successful models/month	Free
Starter	per user/month · 5 developer seats · 1 project · 15,000 successful models/month · 5,000 queried metrics/month	$100 a month
Enterprise	Custom seats · 30 projects · 100,000 successful models/month · 20,000 queried metrics/month	On request
Enterprise+	Custom seats and projects · 100,000 successful models/month · 20,000 queried metrics/month	On request

As github.com lists them, read 4 Oct 2026. The maker's own page has the final word.

Getting started

Open the MetricFlow GitHub repository.
Prepare a working dbt project and dbt adapter.
Check that your dbt version is 1.6 or higher.
Install from PyPI with `pip install dbt-metricflow`.
Define metrics and semantic models in YAML files in your dbt project.

Verdict

MetricFlow provides self-hosted metric definitions and SQL generation for compatible dbt projects. Its dbt and platform requirements, along with the lack of support for dbt built-in functions and packages, are important constraints.

Questions

Does MetricFlow cost anything?

MetricFlow is free and distributed under the Apache 2.0 license.

What does MetricFlow require?

It requires a working dbt project and a dbt adapter, and is compatible with dbt version 1.6 and higher.

Which data platforms are supported?

The documentation lists Snowflake, BigQuery, Databricks, and Redshift. Postgres is listed for dbt v1 only.

Can MetricFlow be self-hosted?

Yes. MetricFlow is self-hosted and can be installed from PyPI with `pip install dbt-metricflow`.

What is the dbt Semantic Layer price?

Hosted access is listed for dbt Starter, Enterprise, and Enterprise+ plans. The Starter plan is $100 per user/month.

Which integrations are listed for the dbt Semantic Layer?

It lists Power BI, Tableau, Google Sheets, Excel, Omni, Hex, Mode, Sigma, and other integrations.

Facts from MetricFlow's own pages

AI integration: Claude and ChatGPT can connect through the dbt MCP server to query governed metrics. · docs.getdbt.com, 15 Oct 2026
API: The hosted Semantic Layer exposes GraphQL and JDBC interfaces for metric queries. · docs.getdbt.com, 15 Oct 2026
APIs: Custom integrations can use JDBC, ADBC, GraphQL, and the Python SDK library. · docs.getdbt.com, 4 Oct 2026
CLI capabilities: Commands can list metrics, dimensions, dimension values, entities, and saved queries, query metrics, validate configurations, and run exports. · docs.getdbt.com, 15 Oct 2026
Configuration: Metrics and semantic models are authored in YAML, with Ossie documents supported as an alternative from dbt 1.12. · docs.getdbt.com, 15 Oct 2026
dbt compatibility: MetricFlow is compatible with dbt version 1.6 and higher. · docs.getdbt.com, 4 Oct 2026
Definitions: Metrics and semantic models can be defined in YAML files in a dbt project. · docs.getdbt.com, 4 Oct 2026
Documentation depth: The developer documentation includes guides for joins, time spines, validations, semantic models, metrics, saved queries, advanced modeling, deployment, caching, exports, integrations, and APIs. · docs.getdbt.com, 15 Oct 2026
Founded: 2016 · github.com, 28 Sept 2026
Headquarters: Oakland, California, USA · github.com, 28 Sept 2026
Hosted access: The dbt Semantic Layer is listed for dbt Starter, Enterprise, and Enterprise+ plans; the pricing page lists Starter at $100 per user/month. · getdbt.com, 4 Oct 2026
Hosted execution: On dbt Platform, MetricFlow commands run remotely through dbt sl and platform-managed versions. · docs.getdbt.com, 15 Oct 2026
Installation: Self-hosted installation uses pip with the dbt-metricflow package and optional adapter extras. · docs.getdbt.com, 15 Oct 2026
Integrations: Official documentation lists downstream integrations including Tableau, Hex, Dot, Mode, and Google Sheets. · docs.getdbt.com, 15 Oct 2026
Joins: MetricFlow uses entity types and parameters to construct joins and avoid fan-out and chasm joins. · docs.getdbt.com, 4 Oct 2026
Licence: MetricFlow versions 0.209.0 and newer use Apache 2.0; earlier releases used AGPL or BSL licensing. · github.com, 15 Oct 2026
License and installation: MetricFlow is distributed under Apache 2.0 and can be installed from PyPI with `pip install dbt-metricflow`. · github.com, 4 Oct 2026
Limit: hosted dependency: The independent review states that the managed semantic service and API require dbt Cloud; dbt Core can define metrics locally but lacks the hosted registry and API. · analyticsengineering.com, 15 Oct 2026
Limit: modeling effort: The independent review says teams must ensure correct keys and grains and that upfront semantic modeling is a tradeoff. · analyticsengineering.com, 15 Oct 2026
Limit: performance: The independent review warns that large windowed calculations can be expensive and still require warehouse partitioning, rollups, or pre-aggregation. · analyticsengineering.com, 15 Oct 2026
Maker: MetricFlow is developed and maintained by dbt Labs. · docs.getdbt.com, 4 Oct 2026
Maker founded: Crunchbase says dbt Labs was founded in 2016. · crunchbase.com, 15 Oct 2026
Maker headquarters: Crunchbase lists Philadelphia, Pennsylvania as dbt Labs' headquarters. · crunchbase.com, 15 Oct 2026
Maker legal name: Crunchbase lists the maker's legal name as dbt Labs, Inc. · crunchbase.com, 15 Oct 2026

Where MetricFlow stands on Freedom251

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