Is Bruin open source?

The Apache-2.0 pipeline CLI is open source and free to self-host. Bruin Cloud, its AI analyst, and AI dashboards are proprietary hosted products.

How much does Bruin cost?

The free tier includes $100 in credits and 50 AI tasks without a card. Paid Cloud plans require a demo for a quote.

What's the difference between Dot and Bruin?

Bruin combines pipelines, quality, lineage, and an AI analyst. Dot focuses on analysis grounded in existing dbt models, Looker semantics, and BI tools.

All comparisons

Dot vs Bruin

Dot fits teams keeping dbt, Looker, or an existing BI stack. Bruin combines pipelines and analysis in one platform.

What are the differences between Dot and Bruin?

Showing 15 of 43 capabilities across 8 categories

03 / 10

Which data sources can it access?

Automatically routes questions to the right data

Dot: Yes. Routes each question across connected sources automatically
Bruin: No. Users pick an agent with preset connections Source

04 / 10

Which business context grounds its answers?

Grounded in LookML

Dot: Yes. Reads Looker/LookML semantics directly for answers
Bruin: No. Looker is a data source only, not semantics Source

05 / 10

How do you know the data is correct?

Regression tests for important questions

Dot: Yes. Per-org eval suites plus feedback-driven improvement
Bruin: No. No evaluation or accuracy-training framework found Source

Learns from feedback with human approval

Dot: Yes. Feedback proposes improvements for human approval
Bruin: No. No feedback learning or approval workflow documented 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
Bruin: Partly. Versioned glossary plus on-demand external wiki context Source

Context stays current when schemas change

Dot: Yes. Schema changes trigger assisted context review
Bruin: Partly. Schema refresh requires rerunning import and enhancement Source

07 / 10

Which outputs can it produce?

Multi-step investigations, not just lookups

Dot: Yes. Parallel analysis with a visible plan
Bruin: No. Single-turn conversational Q&A; no multi-step mode found Source

Exports decks and PDFs

Dot: Yes. Automated Reports export as finished PowerPoint or PDF
Bruin: Partly. Deck output named once; no confirmed PDF/PPT format Source

Runs Python for statistical work

Dot: Yes. Audit trail includes the Python behind an answer
Bruin: Partly. Python is a pipeline asset, not ad hoc Source

White labeling and embedded analytics

Dot: Partly. Dashboard embedding; limited white-label controls
Bruin: No. No embedded or white-label analytics product found Source

Notebook or IDE for analysts

Dot: No. No analyst notebook or code IDE
Bruin: Yes. VS Code extension supports SQL, Python, query previews Source

08 / 10

Which governance and security controls are included?

Open source

Dot: No. Proprietary; source not public
Bruin: Partly. CLI is Apache-2.0 OSS; AI analyst/Cloud proprietary 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
Bruin: Partly. An analytics engineering platform adding AI analysis

10 / 10

How do pricing and cost controls work?

Public, predictable pricing

Dot: Yes. Public per-seat pricing published on the pricing page
Bruin: Partly. PAYG rate published; enterprise pricing needs sales Source

Hard usage limits, spend visibility, and ROI

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

Across all 43 capabilities

Dot36 supported2 partial5 not available

Bruin25 supported9 partial9 not available

Category	Decision point	Dot	Bruin
03 / 10Which data sources can it access?	Automatically routes questions to the right data	Yes. Routes each question across connected sources automatically	No. Users pick an agent with preset connections Source
04 / 10Which business context grounds its answers?	Grounded in LookML	Yes. Reads Looker/LookML semantics directly for answers	No. Looker is a data source only, not semantics Source
05 / 10How do you know the data is correct?	Regression tests for important questions	Yes. Per-org eval suites plus feedback-driven improvement	No. No evaluation or accuracy-training framework found Source
05 / 10How do you know the data is correct?	Learns from feedback with human approval	Yes. Feedback proposes improvements for human approval	No. No feedback learning or approval workflow documented 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. Versioned glossary plus on-demand external wiki context Source
06 / 10Who owns and maintains the business logic?	Context stays current when schemas change	Yes. Schema changes trigger assisted context review	Partly. Schema refresh requires rerunning import and enhancement Source
07 / 10Which outputs can it produce?	Multi-step investigations, not just lookups	Yes. Parallel analysis with a visible plan	No. Single-turn conversational Q&A; no multi-step mode found Source
	Exports decks and PDFs	Yes. Automated Reports export as finished PowerPoint or PDF	Partly. Deck output named once; no confirmed PDF/PPT format Source
	Runs Python for statistical work	Yes. Audit trail includes the Python behind an answer	Partly. Python is a pipeline asset, not ad hoc Source
	White labeling and embedded analytics	Partly. Dashboard embedding; limited white-label controls	No. No embedded or white-label analytics product found Source
	Notebook or IDE for analysts	No. No analyst notebook or code IDE	Yes. VS Code extension supports SQL, Python, query previews Source
08 / 10Which governance and security controls are included?	Open source	No. Proprietary; source not public	Partly. CLI is Apache-2.0 OSS; AI analyst/Cloud proprietary 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 analytics engineering platform adding AI analysis
10 / 10How do pricing and cost controls work?	Public, predictable pricing	Yes. Public per-seat pricing published on the pricing page	Partly. PAYG rate published; enterprise pricing needs sales Source
10 / 10How do pricing and cost controls work?	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
Across all 43 capabilities		Dot36 supported2 partial5 not available	Bruin25 supported9 partial9 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 Bruin when analytics engineering should stay close to code

Apache-2.0 CLI, free to self-host, with 1,670+ GitHub stars.
500+ connectors, including Stripe, HubSpot, Salesforce, and Shopify.
Reaches 8 chat channels, including WhatsApp, Discord, and Telegram.

Watch out

ODBC setups can miss dependencies. Two GitHub issues trace ingestr failures to missing Python or native ODBC dependencies in local and Docker setups. ^[1][2]

Pricing

Free tier: $100 in credits plus 50 AI tasks.
Startup program: $10,000 in credits over 12 months.
Standard Cloud plans: no public price, demo required.

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.