MetricFlow
MetricFlow is a self-hosted, open-source tool for defining metric logic and generating SQL to query metrics across data dimensions.
What MetricFlow is
MetricFlow is a self-hosted, open-source tool for defining metric logic and generating SQL to query metrics across data dimensions. Teams define metrics and semantic models in YAML files within a dbt project. Supported metric types include simple, conversion, cumulative, derived and ratio metrics. MetricFlow uses entity types and parameters to construct joins while avoiding fan-out and chasm joins.
It is compatible with dbt 1.6 and higher and lists Snowflake, BigQuery, Databricks and Redshift as supported data platforms; Postgres is listed for dbt v1 only. A working dbt project and a dbt adapter are required. MetricFlow is distributed under Apache 2.0 and can be installed from PyPI with `pip install dbt-metricflow`. It does not currently support dbt built-in functions or packages. Custom integrations can use JDBC, ADBC, GraphQL or the Python SDK. The separate dbt Semantic Layer, powered by MetricFlow, 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+ plans; Starter costs $100 per user/month.
Good
- Defines metrics and semantic models in YAML
- Supports five metric types
- Builds joins using entity types and parameters
- Offers JDBC, ADBC, GraphQL and Python SDK integration options
Watch out
- Requires a working dbt project and adapter
- Does not support dbt built-in functions or packages
- Postgres support is listed for dbt v1 only
Who it suits
MetricFlow may suit data teams that use dbt and want to define reusable metrics and generate SQL across dimensions. It requires a working dbt project and adapter.
Verdict
MetricFlow provides a self-hosted way to manage metric definitions and generate SQL within a dbt project. Teams should check its dbt requirements, data-platform support and unsupported built-in functions or packages before adopting it.
Plans and prices
From MetricFlow's own pricing page, checked 15 Oct 2026.
| Plan | Price | What it gives you |
|---|---|---|
| Developer | Free | 1 developer seat · 1 project · 3,000 successful models/month |
| MetricFlow | Free | Apache 2.0 licensed · requires a working dbt project and dbt adapter |
| Starter | $100 a month | 5 developer seats · 1 project · 15,000 successful models/month · 5,000 queried metrics/month · per user/month |
| Enterprise | Priced on request | Custom seats · 30 projects · 100,000 successful models/month · 20,000 queried metrics/month |
| Enterprise+ | Priced on request | Custom seats and projects · 100,000 successful models/month · 20,000 queried metrics/month |
The facts
Each one read from MetricFlow's own pages, with the date it was checked.
What it does
- AI integration
- Claude and ChatGPT can connect through the dbt MCP server to query governed metrics.checked 15 Oct 2026
- API
- The hosted Semantic Layer exposes GraphQL and JDBC interfaces for metric queries.checked 15 Oct 2026
- APIs
- Custom integrations can use JDBC, ADBC, GraphQL, and the Python SDK library.checked 4 Oct 2026
- CLI capabilities
- Commands can list metrics, dimensions, dimension values, entities, and saved queries, query metrics, validate configurations, and run exports.checked 15 Oct 2026
- Configuration
- Metrics and semantic models are authored in YAML, with Ossie documents supported as an alternative from dbt 1.12.checked 15 Oct 2026
- dbt compatibility
- MetricFlow is compatible with dbt version 1.6 and higher.checked 4 Oct 2026
- Definitions
- Metrics and semantic models can be defined in YAML files in a dbt project.checked 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.checked 15 Oct 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.checked 4 Oct 2026
- Hosted execution
- On dbt Platform, MetricFlow commands run remotely through dbt sl and platform-managed versions.checked 15 Oct 2026
- Installation
- Self-hosted installation uses pip with the dbt-metricflow package and optional adapter extras.checked 15 Oct 2026
- Integrations
- Official documentation lists downstream integrations including Tableau, Hex, Dot, Mode, and Google Sheets.checked 15 Oct 2026
- Joins
- MetricFlow uses entity types and parameters to construct joins and avoid fan-out and chasm joins.checked 4 Oct 2026
- Licence
- MetricFlow versions 0.209.0 and newer use Apache 2.0; earlier releases used AGPL or BSL licensing.checked 15 Oct 2026
- License and installation
- MetricFlow is distributed under Apache 2.0 and can be installed from PyPI with `pip install dbt-metricflow`.checked 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.checked 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.checked 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.checked 15 Oct 2026
The company
- Founded
- 2016checked 28 Sep 2026
- Headquarters
- Oakland, California, USAchecked 28 Sep 2026
- Maker
- MetricFlow is developed and maintained by dbt Labs.checked 4 Oct 2026
- Maker founded
- Crunchbase says dbt Labs was founded in 2016.checked 15 Oct 2026
- Maker headquarters
- Crunchbase lists Philadelphia, Pennsylvania as dbt Labs' headquarters.checked 15 Oct 2026
- Maker legal name
- Crunchbase lists the maker's legal name as dbt Labs, Inc.checked 15 Oct 2026
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