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Lightdash

Open-source BI with a governed metrics layer and AI agents

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Lightdash is open-source BI that defines metrics once in dbt or YAML, then serves dashboards, AI agents and embedded analytics from cloud or self-hosted setups.

Pricing
Open source
Deployment
Self-hosted
Platforms
Web
6,224stars802forks
2views
Updated

What is Lightdash?

Lightdash is a business intelligence platform for data teams and business users. Its context layer defines metrics, dimensions, joins, business logic, caching, and access rules, using dbt projects or standalone Lightdash YAML connected to a data warehouse. Dashboards, AI agents, data apps, and embedded analytics draw on these shared definitions.

Data teams can maintain metrics, charts, and dashboards as files, preview changes through the CLI, validate projects in CI, and review changes in pull requests. Business users can explore dashboards or ask questions in plain English. Lightdash agents respect permissions and return inspectable queries.

Lightdash offers managed cloud hosting and an open-source core that can run on an organization's own infrastructure with Docker or Kubernetes. Commercial enterprise features and support are available, including licensed features for self-hosted deployments.

Inside the application

Lightdash screenshots

Explore the interface. Open a screenshot to view it at full size.

Lightdash screenshot 1
Lightdash screenshot 1

Source: official Lightdash project page.

Key capabilities

  • Context layer Shared metrics, joins, caching and access rules from dbt or Lightdash YAML.
  • BI as code Charts and dashboards as files, with CLI previews, CI validation and pull request review.
  • AI agents Plain-English questions that return inspectable queries and respect permissions.
  • Data apps Custom reports and customer-facing data products built on the same layer.
  • Embedding Dashboards, agents and data apps in your product through the Lightdash SDK, with row-level security.
  • Deployment choice Lightdash Cloud or a self-hosted core on Docker or Kubernetes.
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Where Lightdash fits

  • Governed metrics and semantic layer
  • Dashboards and self-serve exploration
  • Analytics as code with Git and CI
  • Conversational analytics through AI agents
  • Embedded analytics with row-level security
  • Cloud and self-hosted deployment

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