MetriQL
Open-source metrics layer for your data warehouse
Founded
2021
Starting Price
Free
Category
Analytics & BI
Last updated March 8, 2026
About MetriQL
MetriQL is an open-source metrics store that lets you define your business metrics as code and share them consistently across all your BI and data tools. Built as an extension of dbt, it serves as a headless BI layer that centralizes metric definitions and syncs them to downstream tools via JDBC and REST APIs.
Pros & Cons
Pros
- Fully open-source with a permissive Apache 2.0 license, more flexible than alternatives like MetricFlow's AGPL
- Deep native dbt integration eliminates the need to learn a separate transformation framework
- Supports 19+ downstream tool integrations, significantly more than competing metrics layers
- No data movement — operates as a metadata layer on top of your existing data warehouse
- Trino-compatible JDBC interface enables connection from virtually any SQL-capable BI tool
Key Features
Metrics as Code
Define business metrics centrally in code using dbt-compatible YAML, ensuring a single source of truth across your organization
dbt Integration
Works as a native extension of dbt, reading models directly from dbt's manifest.json artifact without requiring a separate transformation layer
BI Tool Sync
Synchronize metric definitions to downstream BI tools including Tableau, Looker, Google Data Studio, and Google Sheets
JDBC & Trino Interface
Exposes a Trino/Presto-compatible JDBC driver so any BI tool that supports Trino can connect natively
REST API
Full REST API for building embedded analytics applications, custom dashboards, or integrating metrics into Python workflows
Aggregation Roll-ups
Automatically creates dbt roll-up models to pre-aggregate data, optimizing query performance and reducing warehouse costs
MQL Reporting
Reference measures and dimensions using MetriQL Query Language (MQL), which compiles to optimized SQL for your target data warehouse
Pricing
Open Source
Free
- Full metrics layer functionality
- dbt integration
- JDBC and REST API access
- All BI tool integrations
- Aggregation roll-ups
Best For
Centralized Metric Definitions
Data teams define metrics once in code and share consistent definitions across Tableau, Looker, Google Sheets, and custom applications
Headless BI Architecture
Organizations building a modern data stack use MetriQL as the semantic layer between dbt transformations and multiple downstream BI consumers
Embedded Analytics
Product teams leverage MetriQL's REST API to build embedded analytics dashboards within their SaaS applications without duplicating metric logic
Warehouse Cost Optimization
Data engineers use automatic aggregation roll-ups to pre-compute common queries, reducing data warehouse compute costs and improving dashboard performance
