Can Dot use our existing LookML and Explores?

Yes. Dot reads your models, Explores, dimensions, and measures through Looker's API. Each metric goes back to Looker as a real query, so your LookML supplies the joins and the aggregation, and Looker runs it.

Does Gemini in Looker cost extra?

Not yet. Usage is included within fair-use limits through September 30, 2026. Afterward, overage costs $3 per million input tokens and $20 per million output tokens.

Can Looker's AI analyze data outside LookML?

No. It queries Looker Explores defined in LookML. Raw tables, spreadsheets, and data in other tools are unavailable until someone models them in LookML.

Can you ask Looker's AI questions from Slack or Teams?

Not conversationally. Action Hub can send scheduled dashboards to Slack, but Conversational Analytics has no native Slack or Teams chat app.

All comparisons

Dot vs Gemini in Looker

Dot queries your Looker Explores from Slack, Teams, and email, and reaches what LookML does not model. Gemini in Looker fits analytics inside LookML.

You don't have to leave Looker.

Looker

Your LookML, Explores, and database connection

Reads your Explores, dimensions, and measures

Sends a query, and Looker runs it

Dot

Answers in Slack, Teams, and the web

Reads through Looker's API, never your LookML repository.

What are the differences between Dot and Gemini in Looker?

Showing 24 of 43 capabilities across 9 categories

02 / 10

Where can users receive answers?

Native Slack app

Dot: Yes. Native Slack app with validated answers
Gemini in Looker: Partly. Action Hub pushes dashboards to Slack, not chat Source

Native Microsoft Teams app

Dot: Yes. Native Teams app, same governed answers
Gemini in Looker: No. Teams webhook sends content, not conversational answers Source

Native mobile apps

Dot: No. Web app works in mobile browsers
Gemini in Looker: Yes. Native Looker apps support iOS and Android Source

03 / 10

Which data sources can it access?

Warehouse and database connectors

Dot: Yes. Live connectors: Snowflake, BigQuery, Databricks, Redshift
Gemini in Looker: Partly. Needs LookML-modeled Explores before CA can query Source

Automatically routes questions to the right data

Dot: Yes. Routes each question across connected sources automatically
Gemini in Looker: No. Users choose an Explore or agent before asking Source

Joins data across separate sources

Dot: Yes. Cross-warehouse analysis in one workspace
Gemini in Looker: Partly. Up to 5 Explores, one connection each Source

04 / 10

Which business context grounds its answers?

Grounded in LookML

Dot: Yes. Reads your Explores, sends each metric to LookerDot writes only the SQL that combines Looker's results; your LookML supplies the joins and the aggregation.
Gemini in Looker: Yes. Purpose-built on LookML, Looker's governed semantic layer Source

Grounded in dbt models and metrics

Dot: Yes. Grounds answers in dbt models, metrics
Gemini in Looker: No. Uses LookML, not dbt's metric layer Source

Other semantic layers

Dot: Yes. Also connects to dbt, Cube, Steep, and Malloy
Gemini in Looker: No. LookML only; no other semantic layer Source

Your own metric and term definitions

Dot: Yes. Org glossary and playbooks, taught once
Gemini in Looker: Partly. LookML labels feed context, no glossary Source

05 / 10

How do you know the data is correct?

Regression tests for important questions

Dot: Yes. Per-org eval suites, feedback-driven improvement
Gemini in Looker: Partly. Official toolkit runs structured agent evaluation suites Source

Learns from feedback with human approval

Dot: Yes. Feedback proposes improvements for human approval
Gemini in Looker: Partly. Admins review feedback and curate verified queries Source

06 / 10

Who owns and maintains the business logic?

Context stays current when schemas change

Dot: Yes. Schema changes trigger assisted context review
Gemini in Looker: Partly. Validators catch breakage; LookML updates stay manual Source

Context remains portable if you leave

Dot: Yes. Readable context and apps stay in your repository
Gemini in Looker: Partly. LookML is readable; agent assets need API export Source

07 / 10

Which outputs can it produce?

Exports decks and PDFs

Dot: Yes. Exports PowerPoint and PDF files
Gemini in Looker: Partly. PDF export only, no PowerPoint Source

White labeling and embedded analytics

Dot: Partly. Dashboard embedding; limited white-label controls
Gemini in Looker: Yes. Embed edition supports white-label analytics Source

Notebook or IDE for analysts

Dot: No. No analyst notebook; Python runs internally only
Gemini in Looker: Partly. SQL Runner only, no Python notebook Source

08 / 10

Which governance and security controls are included?

Every query uses the asker's permissions

Dot: Yes. Row-level security across connected sourcesLooker sees one API service account, so an admin turns Dot's row-level rules on per Explore. Team plan.
Gemini in Looker: Yes. Access filters and grants scope rows per user Source

Self-hosting available

Dot: Partly. Self-hosting only at the Enterprise tier
Gemini in Looker: No. Customer-hosted Looker excludes Gemini features 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
Gemini in Looker: No. A governed LookML BI platform adding conversational analytics

10 / 10

How do pricing and cost controls work?

Public, predictable pricing

Dot: Yes. Simple public tiers: Free, Pro $200/mo, Team $800/mo
Gemini in Looker: No. No public price; annual contract negotiated via sales Source

Hard usage limits, spend visibility, and ROI

Dot: Yes. Set usage limits; track spend and ROI
Gemini in Looker: Partly. Capacity controls spend; ROI tracked separately Source

Free tier or trial

Dot: Yes. Free plan, no credit card, unlimited users
Gemini in Looker: No. No free tier; core Looker is sales-quote only Source

No new vendor to procure

Dot: No. New vendor: separate procurement, security review, contract
Gemini in Looker: Yes. Bundled into existing Standard/Enterprise/Embed license Source

Across all 43 capabilities

Dot36 supported2 partial5 not available

Gemini in Looker22 supported11 partial10 not available

Category	Decision point	Dot	Gemini in Looker
02 / 10Where can users receive answers?	Native Slack app	Yes. Native Slack app with validated answers	Partly. Action Hub pushes dashboards to Slack, not chat Source
	Native Microsoft Teams app	Yes. Native Teams app, same governed answers	No. Teams webhook sends content, not conversational answers Source
	Native mobile apps	No. Web app works in mobile browsers	Yes. Native Looker apps support iOS and Android Source
03 / 10Which data sources can it access?	Warehouse and database connectors	Yes. Live connectors: Snowflake, BigQuery, Databricks, Redshift	Partly. Needs LookML-modeled Explores before CA can query Source
	Automatically routes questions to the right data	Yes. Routes each question across connected sources automatically	No. Users choose an Explore or agent before asking Source
	Joins data across separate sources	Yes. Cross-warehouse analysis in one workspace	Partly. Up to 5 Explores, one connection each Source
04 / 10Which business context grounds its answers?	Grounded in LookML	Yes. Reads your Explores, sends each metric to LookerDot writes only the SQL that combines Looker's results; your LookML supplies the joins and the aggregation.	Yes. Purpose-built on LookML, Looker's governed semantic layer Source
	Grounded in dbt models and metrics	Yes. Grounds answers in dbt models, metrics	No. Uses LookML, not dbt's metric layer Source
	Other semantic layers	Yes. Also connects to dbt, Cube, Steep, and Malloy	No. LookML only; no other semantic layer Source
	Your own metric and term definitions	Yes. Org glossary and playbooks, taught once	Partly. LookML labels feed context, no glossary Source
05 / 10How do you know the data is correct?	Regression tests for important questions	Yes. Per-org eval suites, feedback-driven improvement	Partly. Official toolkit runs structured agent evaluation suites Source
05 / 10How do you know the data is correct?	Learns from feedback with human approval	Yes. Feedback proposes improvements for human approval	Partly. Admins review feedback and curate verified queries Source
06 / 10Who owns and maintains the business logic?	Context stays current when schemas change	Yes. Schema changes trigger assisted context review	Partly. Validators catch breakage; LookML updates stay manual Source
06 / 10Who owns and maintains the business logic?	Context remains portable if you leave	Yes. Readable context and apps stay in your repository	Partly. LookML is readable; agent assets need API export Source
07 / 10Which outputs can it produce?	Exports decks and PDFs	Yes. Exports PowerPoint and PDF files	Partly. PDF export only, no PowerPoint Source
	White labeling and embedded analytics	Partly. Dashboard embedding; limited white-label controls	Yes. Embed edition supports white-label analytics Source
	Notebook or IDE for analysts	No. No analyst notebook; Python runs internally only	Partly. SQL Runner only, no Python notebook Source
08 / 10Which governance and security controls are included?	Every query uses the asker's permissions	Yes. Row-level security across connected sourcesLooker sees one API service account, so an admin turns Dot's row-level rules on per Explore. Team plan.	Yes. Access filters and grants scope rows per user Source
08 / 10Which governance and security controls are included?	Self-hosting available	Partly. Self-hosting only at the Enterprise tier	No. Customer-hosted Looker excludes Gemini features 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	No. A governed LookML BI platform adding conversational analytics
10 / 10How do pricing and cost controls work?	Public, predictable pricing	Yes. Simple public tiers: Free, Pro $200/mo, Team $800/mo	No. No public price; annual contract negotiated via sales Source
	Hard usage limits, spend visibility, and ROI	Yes. Set usage limits; track spend and ROI	Partly. Capacity controls spend; ROI tracked separately Source
	Free tier or trial	Yes. Free plan, no credit card, unlimited users	No. No free tier; core Looker is sales-quote only Source
	No new vendor to procure	No. New vendor: separate procurement, security review, contract	Yes. Bundled into existing Standard/Enterprise/Embed license Source
Across all 43 capabilities		Dot36 supported2 partial5 not available	Gemini in Looker22 supported11 partial10 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 Looker when governed embedding is the core requirement

Uses the same LookML definitions as certified dashboards.
Usage is included through September 2026, subject to fair-use limits.
Uses Looker's roles, permission sets, and model sets.

Watch out

Failures can be difficult to diagnose. Users describe silent non-answers and unexplained permission errors that require support or infrastructure checks to diagnose. ^[1][2]

Pricing

Looker platform: annual contract, sales-quoted only, no public list price.
Conversational Analytics free within fair-use limits through September 30, 2026.
After that: $3 per 1M input tokens, $20 per 1M output.

Source

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

You don't have to leave Looker. 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.