Does Lightdash require dbt?

No. Lightdash YAML can define a semantic layer without dbt, although many production deployments use dbt models and MetricFlow metrics.

How much does Lightdash cost?

Self-hosting is free. Cloud Pro costs $3,000 monthly for unlimited users and has a 21-day trial. Enterprise pricing is custom.

Can Lightdash query multiple warehouses at once?

No. Each project connects to one warehouse. Combining Snowflake and Postgres requires separate projects rather than one cross-source query.

All comparisons

Dot vs Lightdash

Dot connects multiple sources and serves business users across chat and the web. Lightdash suits dbt teams seeking open-source BI.

What are the differences between Dot and Lightdash?

Showing 25 of 43 capabilities across 10 categories

01 / 10

Who can use it to get answers?

Built for business users, not just analysts

Dot: Yes. Chat, Slack, Teams, and web for non-technical users
Lightdash: Partly. AI chat sits atop an analyst-built dbt layer Source

02 / 10

Where can users receive answers?

Native Microsoft Teams app

Dot: Yes. Native Teams app, same depth as Slack
Lightdash: Partly. Marketing mentions Teams; only Slack has setup docs Source

03 / 10

Which data sources can it access?

Warehouse and database connectors

Dot: Yes. 31 integrations across warehouses and files
Lightdash: Partly. ~10 warehouses; no MySQL, SQL Server, Oracle Source

Automatically routes questions to the right data

Dot: Yes. Routes each question across connected sources automatically
Lightdash: Partly. Chosen agent automatically selects relevant models and metrics Source

Joins data across separate sources

Dot: Yes. Multiple sources joined in one workspace
Lightdash: No. One warehouse per project; no cross-warehouse joins Source

Analyze uploaded files

Dot: Yes. CSV, Excel, Google Sheets upload as sources
Lightdash: No. No CSV/Excel upload; Sheets sync is export-only Source

04 / 10

Which business context grounds its answers?

Grounded in LookML

Dot: Yes. Grounds directly in LookML explores and fields
Lightdash: No. No LookML/Looker integration; dbt or standalone YAML only Source

Other semantic layers

Dot: Yes. Also grounds in Cube and Steep semantic layers
Lightdash: Partly. Standalone YAML skips dbt; no Cube support Source

Your own metric and term definitions

Dot: Yes. Org glossary and playbooks taught once
Lightdash: Partly. One project-context YAML file; no glossary system Source

05 / 10

How do you know the data is correct?

Shows the SQL behind every number

Dot: Yes. Click any answer number to see its SQL
Lightdash: Partly. Returns a semantic query, not SQL by default Source

06 / 10

Who owns and maintains the business logic?

Context and apps use Git and pull requests

Dot: Yes. Every publish is versioned through Git and PRs
Lightdash: Partly. Writeback only edits the connected dbt repo Source

07 / 10

Which outputs can it produce?

Persistent, filterable dashboards and metric views

Dot: Yes. Pixel-perfect dashboards and polished metric views
Lightdash: Partly. Established dashboards; generated Data Apps remain beta Source

Exports decks and PDFs

Dot: Yes. Exports finished PowerPoint and PDF business-review decks
Lightdash: Partly. PDF, image, CSV, and xlsx export; no PowerPoint Source

Runs Python for statistical work

Dot: Yes. Runs Python for statistical analysis
Lightdash: Partly. Python SDK supports external notebooks, not agent execution Source

White labeling and embedded analytics

Dot: Partly. Dashboard embedding; limited white-label controls
Lightdash: Yes. Iframe and SDK embedding with custom branding Source

Notebook or IDE for analysts

Dot: No. Runs Python internally; no analyst notebook workspace
Lightdash: Partly. SQL Runner: admin-only, single query, no Python Source

08 / 10

Which governance and security controls are included?

Single sign-on

Dot: Yes. OIDC SSO from the $800 monthly tier
Lightdash: Partly. Google login only; full SSO needs Enterprise Source

Security audit log

Dot: Yes. Security audit log with a SIEM API
Lightdash: Partly. User/download activity logs exist; no SIEM export documented Source

Self-hosting available

Dot: Partly. Self-hosting available, but only on the Enterprise tier
Lightdash: Yes. MIT-licensed core, self-hostable via Docker/Kubernetes for free Source

SOC 2 Type II

Dot: Yes. SOC 2 Type II certified
Lightdash: Partly. "Certification available" on Enterprise only; type unspecified Source

Open source

Dot: No. Proprietary; source not public
Lightdash: Yes. MIT-licensed core on GitHub, self-hostable via Docker/Kubernetes Source

09 / 10

What is the product built to become?

Purpose-built as a full AI analytics platform

Dot: Yes. Purpose-built for AI analytics across the full workflow
Lightdash: Partly. An open-source dbt BI platform expanding into AI agents

10 / 10

How do pricing and cost controls work?

Public, predictable pricing

Dot: Yes. Free, Pro, Team prices public; Enterprise custom
Lightdash: Partly. Cloud Pro price public; Enterprise is custom Source

Hard usage limits, spend visibility, and ROI

Dot: Yes. Set usage limits; track spend and ROI
Lightdash: Partly. Infrastructure spend is self-managed; ROI tracked separately Source

Free tier or trial

Dot: Yes. Hosted Free plan, no credit card required
Lightdash: Partly. Free only self-hosted; hosted Cloud starts at $3,000/month Source

Across all 43 capabilities

Dot36 supported2 partial5 not available

Lightdash18 supported19 partial6 not available

Category	Decision point	Dot	Lightdash
01 / 10Who can use it to get answers?	Built for business users, not just analysts	Yes. Chat, Slack, Teams, and web for non-technical users	Partly. AI chat sits atop an analyst-built dbt layer Source
02 / 10Where can users receive answers?	Native Microsoft Teams app	Yes. Native Teams app, same depth as Slack	Partly. Marketing mentions Teams; only Slack has setup docs Source
03 / 10Which data sources can it access?	Warehouse and database connectors	Yes. 31 integrations across warehouses and files	Partly. ~10 warehouses; no MySQL, SQL Server, Oracle Source
	Automatically routes questions to the right data	Yes. Routes each question across connected sources automatically	Partly. Chosen agent automatically selects relevant models and metrics Source
	Joins data across separate sources	Yes. Multiple sources joined in one workspace	No. One warehouse per project; no cross-warehouse joins Source
	Analyze uploaded files	Yes. CSV, Excel, Google Sheets upload as sources	No. No CSV/Excel upload; Sheets sync is export-only Source
04 / 10Which business context grounds its answers?	Grounded in LookML	Yes. Grounds directly in LookML explores and fields	No. No LookML/Looker integration; dbt or standalone YAML only Source
	Other semantic layers	Yes. Also grounds in Cube and Steep semantic layers	Partly. Standalone YAML skips dbt; no Cube support Source
	Your own metric and term definitions	Yes. Org glossary and playbooks taught once	Partly. One project-context YAML file; no glossary system Source
05 / 10How do you know the data is correct?	Shows the SQL behind every number	Yes. Click any answer number to see its SQL	Partly. Returns a semantic query, not SQL by default Source
06 / 10Who owns and maintains the business logic?	Context and apps use Git and pull requests	Yes. Every publish is versioned through Git and PRs	Partly. Writeback only edits the connected dbt repo Source
07 / 10Which outputs can it produce?	Persistent, filterable dashboards and metric views	Yes. Pixel-perfect dashboards and polished metric views	Partly. Established dashboards; generated Data Apps remain beta Source
	Exports decks and PDFs	Yes. Exports finished PowerPoint and PDF business-review decks	Partly. PDF, image, CSV, and xlsx export; no PowerPoint Source
	Runs Python for statistical work	Yes. Runs Python for statistical analysis	Partly. Python SDK supports external notebooks, not agent execution Source
	White labeling and embedded analytics	Partly. Dashboard embedding; limited white-label controls	Yes. Iframe and SDK embedding with custom branding Source
	Notebook or IDE for analysts	No. Runs Python internally; no analyst notebook workspace	Partly. SQL Runner: admin-only, single query, no Python Source
08 / 10Which governance and security controls are included?	Single sign-on	Yes. OIDC SSO from the $800 monthly tier	Partly. Google login only; full SSO needs Enterprise Source
	Security audit log	Yes. Security audit log with a SIEM API	Partly. User/download activity logs exist; no SIEM export documented Source
	Self-hosting available	Partly. Self-hosting available, but only on the Enterprise tier	Yes. MIT-licensed core, self-hostable via Docker/Kubernetes for free Source
	SOC 2 Type II	Yes. SOC 2 Type II certified	Partly. "Certification available" on Enterprise only; type unspecified Source
	Open source	No. Proprietary; source not public	Yes. MIT-licensed core on GitHub, self-hostable via Docker/Kubernetes Source
09 / 10What is the product built to become?	Purpose-built as a full AI analytics platform	Yes. Purpose-built for AI analytics across the full workflow	Partly. An open-source dbt BI platform expanding into AI agents
10 / 10How do pricing and cost controls work?	Public, predictable pricing	Yes. Free, Pro, Team prices public; Enterprise custom	Partly. Cloud Pro price public; Enterprise is custom Source
	Hard usage limits, spend visibility, and ROI	Yes. Set usage limits; track spend and ROI	Partly. Infrastructure spend is self-managed; ROI tracked separately Source
	Free tier or trial	Yes. Hosted Free plan, no credit card required	Partly. Free only self-hosted; hosted Cloud starts at $3,000/month Source
Across all 43 capabilities		Dot36 supported2 partial5 not available	Lightdash18 supported19 partial6 not available

Conclusion

Choose for the analytics stack you want to end up with

The decision is not only which tool answers questions today. It is which product can become your analytics platform over time.

Choose Lightdash when an open-source dbt-native BI layer is the goal

MIT-licensed core that can be self-hosted for free.
Native support for dbt MetricFlow metrics.
Deep research delegates work to two isolated data workers.

Watch out

Scheduled deliveries can fail. GitHub issues document image and PDF delivery failures tied to browser navigation, CORS, and long-query timeouts in hosted and self-hosted deployments. ^[1][2][3]

Pricing

Self-hosted open source: free, MIT-licensed, no AI agents.
Cloud Pro: $3,000/month flat, unlimited users; SSO is Google-only.
Enterprise: custom pricing adds full SSO, SCIM 2.0, on-prem deployment.

Source

Choose Dot for best-of-breed AI analytics that can replace BI

Connect Dot to the data sources and semantic models you already have, then start with one agent. Context, apps, and business logic stay version-controlled in a repository you own, so they never become a Dot-only asset. Add dashboards, reporting, and governed workflows as adoption grows, then retire BI software you no longer need.

Watch out

Dot is growing quickly. Its smaller team may not offer the same level of hands-on support as a larger vendor in every region.

Pricing

Free plan, no credit card
Pro at $200/month, or $180/month billed annually. Unlimited users.
Team at $800/month, or $720/month billed annually. Adds single sign-on and row-level security.
Enterprise is custom: self-hosting, SLAs, and volume terms

Full pricing

Why the pricing model matters

Frontier AI has variable inference cost. A vendor that relies only on seat pricing must eventually meter AI, absorb the cost, or limit capability. Dot makes usage visible, lets teams set limits, and tracks the return on that spend.

Receipts

Benchmarked against human analysts, and measured in production

DABStep is Adyen’s public data-analysis benchmark. Dot scored above trained human analysts. It is independently published and available to inspect. In production, customers report these outcomes.

18× ROI

Choco, from time saved on analytics

12,000+ hours

saved every year at Duolingo, across 5,000+ tables

99% faster

time-to-insight at Emerge

€800K found

in a single investigation at KRY

Every Dot answer shows its work. Click any number to inspect the query.