MetricFlow vs Synmetrix
MetricFlow
github.comSynmetrix
synmetrix.orgSide by side
| Figure | MetricFlow | Synmetrix |
|---|---|---|
| Price from | $100/mo | Free |
| Free plan | Yes | Yes |
| Free trial | Yes | — |
| Open source | Yes | — |
| Web | Yes | Yes |
| Windows | Yes | — |
| macOS | Yes | Yes |
| Linux | Yes | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 5 | 1 |
| Spec score · Semantic Layer Software | 9.1 (#3) | 8.4 (#8) |
| Installation | Self-hosted installation uses pip with the dbt-metricflow package and optional adapter extras. | The repository's setup instructions use Docker and Docker Compose to deploy Synmetrix with PostgreSQL. |
| Integrations | Official documentation lists downstream integrations including Tableau, Hex, Dot, Mode, and Google Sheets. | The maker says Synmetrix connects to more than 50 data sources and can integrate with any BI tool. |
| Purpose | MetricFlow defines and manages metric logic and generates SQL for querying metrics across data dimensions. | It centralizes metric definitions so applications can use a consistent view of organizational data. |
| Requirements | Using MetricFlow requires a working dbt project and a dbt adapter. | The repository lists a 3.2 GHz or higher processor, at least 8 GB RAM, and at least 30 GB free disk space as hardware requirements. |
| Security | dbt Labs reports ISO certifications, a SOC 2 Type II examination, and GDPR compliance for dbt. | The site describes upstream data access controls for uniform security and governance, and lists role-based access control. |
| What it does | MetricFlow is a semantic layer that defines metrics and compiles requests into reusable, warehouse-specific SQL. | Synmetrix is an open-source data engineering platform and semantic layer for centrally modeling, integrating, transforming, aggregating, and distributing metrics. |
Plans
MetricFlow
- DeveloperFree
- MetricFlowFree
- Starter$100 a month
- EnterprisePriced on request
- Enterprise+Priced on request
In their own words
MetricFlow
MetricFlow is a free, open-source tool from dbt Labs for defining metric logic and generating SQL across data dimensions. Metrics and semantic models are written in YAML within a dbt project, using simple, conversion, cumulative, derived and ratio types. Entity types and parameters construct joins while avoiding fan-out and chasm joins. MetricFlow works with dbt 1.6 and higher and lists Snowflake, BigQuery, Databricks and Redshift, plus Postgres for dbt v1 only. A working dbt project and adapter are required. It is distributed under Apache 2.0 and installs from PyPI with `pip install dbt-metricflow`. The tool does not currently support dbt built-in functions or packages. Custom integrations can use JDBC, ADBC, GraphQL or the Python SDK. The dbt Semantic Layer, powered by MetricFlow, lists integrations including Power BI, Tableau, Google Sheets, Excel, Omni, Hex, Mode and Sigma. MetricFlow is self-hosted and costs nothing; hosted Semantic Layer Starter access is listed at $100 per user/month.
Synmetrix
Synmetrix is an open-source data engineering platform and semantic layer for defining and sharing organizational metrics from a central model. It uses Cube data models to define metrics and dimensions, then applies transformations and aggregations so applications can use consistent definitions. The maker says it connects to more than 50 data sources and can integrate with any BI tool. Users can explore metrics in its interface or distribute the model through a SQL API to BI tools, dashboards, reports and data science consumers. Model versioning and branching track changes, while query logs, caching and pre-aggregations support data workflows. Role-based access controls govern access to models and metrics; the project also describes upstream data controls for shared security and governance. Synmetrix supports scheduled reports and configurable alerts, with reports deliverable by email, Slack or webhooks. The core is Apache License 2.0 and the platform is intended for self-hosted, including on-premise, deployment. Setup uses Docker and Docker Compose with PostgreSQL. Listed hardware requirements are a 3.2 GHz or faster processor, at least 8 GB RAM and at least 30 GB free disk space.