Sidemantic
Sidemantic is an open-source semantic layer for defining metrics and querying them consistently across tools.
What Sidemantic is
Sidemantic is an open-source semantic layer for defining metrics and querying them consistently across tools. It sits on top of a data warehouse; it is not a BI or ETL tool. Models can be authored in YAML, SQL, or Python, or imported from more than 15 formats, including Cube, dbt MetricFlow, LookML, Malloy, and Snowflake Cortex. Documented warehouse connections include Snowflake, BigQuery, Postgres, ClickHouse, Databricks, and Spark, with additional databases supported through ADBC. Queries can use semantic SQL, Python, a CLI, an MCP server, or the Postgres wire protocol. For cross-model queries, it infers join paths from declared relationships, while symmetric aggregates are designed to prevent double-counting across one-to-many joins. MCP also makes metrics available to LLMs with built-in guardrails for governed access. The interactive workbench includes demo data and works without a database setup. Sidemantic is free, self-hosted, and licensed under AGPL-3.0, with web and API platforms. The project describes itself as young and notes potential rough patches in format conversion and its Postgres protocol server.
Good
- Models can be authored in YAML, SQL, or Python.
- Imports semantic models from more than 15 formats.
- Supports several warehouse connections and ADBC databases.
- Infers cross-model joins from declared relationships.
- Workbench demo data needs no database setup.
Watch out
- It is neither a BI nor an ETL tool.
- Format conversion may have rough patches.
- The Postgres protocol server may have rough patches.
- Pre-aggregation routing is disabled by default.
Who it suits
Sidemantic suits teams that need to define governed metrics and query them across tools on top of a warehouse. Its workbench can also help people explore it using demo data without setting up a database.
Verdict
Sidemantic offers multiple ways to author and query a shared semantic layer, including governed access through MCP. Its young status and noted rough patches are worth weighing for production use.
The facts
Each one read from Sidemantic's own pages, with the date it was checked.
What it does
- Aggregation correctness
- Symmetric aggregates are used to prevent double-counting across one-to-many joins.checked 4 Oct 2026
- AI access
- Sidemantic can expose metrics to LLMs through MCP, with built-in guardrails for governed access.checked 9 Oct 2026
- Format imports
- It imports semantic models from formats including Cube, dbt MetricFlow, LookML, Hex, Rill, Superset, Omni, Malloy, Snowflake Cortex, OSI, AtScale, GoodData, Holistics, and ThoughtSpot.checked 9 Oct 2026
- Interactive demo
- The site offers an interactive workbench with built-in demo data that does not require a database.checked 4 Oct 2026
- Join handling
- Cross-model queries can infer join paths from declared relationships, and symmetric aggregates are designed to prevent double-counting across one-to-many joins.checked 9 Oct 2026
- Join inference
- Cross-model queries infer join paths from declared relationships without requiring a JOIN keyword in semantic SQL.checked 4 Oct 2026
- License
- The GitHub repository identifies the project as open source under the AGPL-3.0 license.checked 4 Oct 2026
- Maturity
- The project describes itself as young and notes that format conversion and its Postgres protocol server may have rough patches.checked 4 Oct 2026
- Model authoring
- You can define semantic models in YAML, SQL DDL, or Python.checked 9 Oct 2026
- Model imports
- It imports semantic models from more than 15 formats, including Cube, MetricFlow, LookML, Malloy, and Snowflake Cortex.checked 4 Oct 2026
- Not a BI or ETL tool
- Sidemantic runs on top of a warehouse and is described as neither an ETL tool nor a BI tool.checked 4 Oct 2026
- Pre-aggregation limit
- Pre-aggregation routing is disabled by default and must be explicitly enabled.checked 9 Oct 2026
- Purpose
- Sidemantic is a semantic layer for defining metrics once and querying them consistently across tools.checked 9 Oct 2026
- Query interfaces
- Sidemantic provides a SQL interface, Python API, CLI, MCP server, and Postgres wire protocol.checked 9 Oct 2026
- Support
- The maker links to documentation and a Discord community for the project.checked 4 Oct 2026
- Warehouse connections
- Documented database connections include DuckDB, MotherDuck, PostgreSQL, BigQuery, Snowflake, ClickHouse, Databricks, and Spark SQL.checked 9 Oct 2026
- Warehouse support
- Native adapters are listed for Snowflake, BigQuery, Postgres, ClickHouse, Databricks, and Spark, with additional databases supported via ADBC.checked 4 Oct 2026
- Workbench
- The workbench can run with built-in demo data without a database setup.checked 9 Oct 2026
The company
- Maker
- The site footer identifies Sidequery Corporation as the company.checked 4 Oct 2026
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Looker
AndroidiOSWebAPI6.9