Evidence vs Lightdash

Evidence is an open-source business intelligence framework built around the idea of BI as code. Analysts write reports as Markdown files with embedded SQL queries and ready-made components for charts, tables and big numbers, and Evidence builds them into a fast static website. The project is MIT licensed and backed by a venture-funded company that also runs a hosted service.

Evidence connects to common warehouses and databases including Snowflake, BigQuery, Databricks, Redshift, Postgres, MySQL and DuckDB, as well as CSV files. Query results are cached and processed in the browser with DuckDB, so pages stay quick once built. Templated pages generate one report per customer, region or product from a single file, loops and conditionals add logic, and everything lives in git, so changes go through pull requests and code review. The open-source framework is free to self-host; Evidence Cloud adds managed builds, hosting, scheduled refreshes and authentication.

Evidence fits data teams that already think in SQL and git and want polished, reproducible reports such as board packs, KPI sites or customer-facing data pages. It is not a self-serve tool for business users who want to drag fields onto a chart. Metabase and Looker Studio are better for ad hoc exploration, Lightdash builds BI directly on dbt models, and Observable Framework is the closest alternative for code-first data apps with more JavaScript flexibility.

Lightdash is an open-source business intelligence tool built on top of dbt, made by a London-based startup. Instead of defining metrics in a separate BI layer, analysts declare dimensions, metrics and joins in the same dbt YAML files that describe their models, so business logic is version-controlled, reviewed in pull requests and reused everywhere.

Business users then explore those governed metrics through a point-and-click interface, build charts and dashboards, and schedule deliveries to Slack, email or Google Sheets. Lightdash also offers an AI analyst that answers natural language questions using the semantic layer, a SQL runner, spaces with permissions, dashboards-as-code and embedding for customer-facing analytics. The core is open source and can be self-hosted for free, while Lightdash Cloud adds managed hosting with unlimited viewers, and Enterprise adds SSO, embedding, advanced permissions and support.

It is a strong fit for data teams that already run dbt and want a semantic-layer BI without Looker's cost or a proprietary modeling language. It requires dbt, so teams without it are better served by Metabase or Power BI. Visualization options are less rich than Tableau, and cloud pricing starts at a level aimed at companies rather than individuals. Looker is the closest conceptual match, while Metabase and Superset are open-source alternatives that work directly on database tables.