Lightdash
Lightdash is BI with metrics in code — in your dbt project or in Lightdash's own YAML. AI-native, flat-rate, and way cheaper than Looker at scale.
What It Is
Lightdash is an open-source BI tool where metrics and dimensions live in YAML. You can define them in your dbt project (Lightdash's original design) or in Lightdash's own native semantic layer (added for teams without dbt). Either way, your metric definitions stay in version control — not locked inside a dashboarding UI.
Lightdash Cloud now adds AI Analyst, which answers natural-language questions over the governed semantic layer. It operates largely on metadata rather than raw warehouse data and lets you bring your own LLM provider (OpenAI, Anthropic, or self-hosted). Cloud also ships dashboards-as-code — dashboard definitions in Git — which pairs well with AI coding agents writing dashboards directly from the repo.
Why We Chose It
For teams that want version-controlled metrics plus AI-native BI without Looker-tier spend, Lightdash Cloud is the right middle ground. Pricing is flat-rate for unlimited users — a fundamentally different shape from per-seat tools — which makes the economics flip in your favor once you're past ~10–15 users. A self-hosted Core edition is also available for teams that want full control.
Worth knowing: most of Lightdash's AI features (AI Analyst, dashboards-as-code, Slack assistant) are in the paid Cloud product rather than the open-source Core.
How We Use It
Connect Lightdash to BigQuery — with dbt YAML integration or Lightdash's native semantic layer
Define explores, metrics, and dimensions in dbt models or Lightdash's own YAML
Build dashboards for operational and executive reporting
Configure AI Analyst with the client's LLM provider (OpenAI, Anthropic, or self-hosted) for natural-language queries
Use Lightdash's dashboards-as-code workflow so coding agents can build and iterate on dashboards directly in Git
Set up Lightdash Cloud or self-hosted Core deployment on GCP
When Lightdash is the right BI tool — and when it isn't
Choose Lightdash when:
- You want metrics defined in code — in dbt YAML or Lightdash's native semantic layer
- You want flat-rate pricing that doesn't scale with headcount (cheaper than Looker past ~10–15 users)
- You want to choose your own LLM provider for governed AI analytics
- Dashboards-as-code fits your team — AI coding agents can work the dashboard layer directly
Choose Looker instead when:
- You need enterprise governance and 20+ users across departments
- You need LookML's full semantic layer capabilities or deep BI Engine tie-in
- Embedded analytics or multi-tenant reporting at scale is on your roadmap
Choose Metabase instead when:
- You need broader BI (dashboards, embedding, SDK) beyond metric-first
- Your team isn't committed to dbt-native workflows
- You want self-host optionality with AI features included — Metabot is now open-source
Choose Steep instead when:
- Primary audience is non-technical users
- A metric-first UX matters more than version control
- Budget is under €500/month for a small team