GILD verdict
Choose Looker if your company's metrics already live in LookML, and Databricks Genie if your data and metrics live in Databricks.
Looker's Conversational Analytics answers from existing LookML with little new curation. Genie keeps answers under Unity Catalog permissions and costs nothing for chat until February 2027. Teams running both should define each metric once, in metric views or LookML, and point the other tool at it.
Teams running Looker on Databricks should let the layer their BI team already maintains decide. Looker's Conversational Analytics, powered by Gemini, maps questions to LookML Explores and inherits that semantic model. Databricks Genie writes SQL against Unity Catalog data, guided by metric views and a knowledge store curated per agent.
Choosing between Databricks Genie and Looker
Choose Databricks Genie if
- Your metrics live, or will live, in Unity Catalog metric views on Databricks.
- You want one governed surface for dashboards, chat, and apps in Genie One, with chat free through January 31, 2027.
- You want each domain agent benchmarked and versioned as code.
Choose Looker if
- Your company already defines its metrics in LookML and business users work in Looker Explores and dashboards.
- Your data spans warehouses that Looker's models already reach.
- You want conversational answers that cannot stray outside the fields a LookML model exposes.
Side by side
| Dimension | Databricks Genie | Looker |
|---|---|---|
| Semantic layer | Unity Catalog metadata and metric views, plus a knowledge store of synonyms, joins, and SQL expressions in each Genie Agent | LookML models; Conversational Analytics maps questions to the parameters defined in the Explores it queries |
| Agents | Genie Agents of up to 50 tables, views, or metric views, reached from Genie One | Data agents connected to up to five Explores, with dashboard agents and agentic workflows in early release |
| Verified answers | Trusted assets: parameterized example queries and Unity Catalog functions whose logic an author has verified | Verified queries, also called golden queries, pairing questions with exact Explore queries |
| Deeper analysis | Agent mode with multi-step SQL and cited reports | Advanced Analytics, which uses Python to answer advanced questions |
| Vendor accuracy claimsEach figure comes from the vendor's own testing, with no shared question set. | In Databricks' June 2026 internal test of 28 questions, Genie answered 84.5% correctly on the first try, against 52.4% for the strongest general-purpose coding agent13 | Google reported in May 2025 that its internal testing found the LookML semantic layer cut data errors in generative AI queries by up to two thirds14 |
| PricingAs published by each vendor. Looker publishes no list price for its editions. | Genie One and Genie Agents usage by named users free through January 31, 2027. From February 1, 2027, 150 free DBUs per user each month, worth US$10.50 on AWS US East, then US$0.070 per DBU, with no seat fees. Service principals billed for all usage. SQL warehouse compute billed separately | Standard, Enterprise, and Embed platform editions by annual quote, each with 10 Standard and 2 Developer users. Conversational Analytics data tokens included monthly: 60M input on Standard, 300M on Enterprise. Overage of US$3.00 per 1M input and US$20.00 per 1M output tokens starts once Google ends the current promotion with at least 90 days' notice |
| Who can ask | Users with consumer access or Databricks SQL entitlements and CAN VIEW or CAN RUN on an agent, plus SELECT on its data | Users whose Looker role has access_data and gemini_in_looker on the model, after an admin enables Gemini in Looker and Conversational Analytics |
| AI data use | Model partners retain no prompts or responses, and data sent respects Unity Catalog permissions | Gemini for Google Cloud does not use prompts or responses to train its models |
What practitioners report, side by side
Each product's figures come from its own practitioners, for the group you pick.
Overall signal
Leaders and teams rate Databricks Genie +36, 44 points above Looker at −8. Most of the gap is in praise, with 21 of Databricks Genie's 41 sources positive against 7 of Looker's 27. Databricks Genie has 6 negative and Looker 10.
The score counts the authors from leaders and teams that take a side, one vote each with their latest view, and is how far positive votes lead negative ones, per 100 votes, so Databricks Genie's +36 rests on votes split 20 positive, 13 mixed and 6 negative and Looker's −8 on 7, 10 and 9.
A lead of a fifth of the votes reads positive and a lead of half strong positive, with the same lines below zero. The dots show each source, positive, mixed, negative. Run a pilot on questions your analysts already answer to measure how often its numbers match theirs.
Footprint on the AI Knowledge Work stack
They differ on two of 7 layers. On AI assistant, Databricks Genie supports it and Looker touches it. On Enterprise search, Databricks Genie supports it and Looker touches it. Coverage is each product's placement on this stack, as its product page shows it. Core is a job the product owns; Supports and Touches are jobs it helps with. Coverage says nothing about how well a product does them.
| Layer | Databricks Genie | Looker |
|---|---|---|
| Assist | ||
| AI assistant | Supports | Touches |
| Find | ||
| Enterprise search | Supports | Touches |
The 5 layers where they match
| Layer | Databricks Genie | Looker |
|---|---|---|
| Find | ||
| Web research | Touches | Touches |
| Create | ||
| Documents | Touches | Touches |
| Meeting notes | Touches | Touches |
| Analyze | ||
| Data analysis | Core | Core |
| Automate | ||
| Automation | Supports | Supports |
What practitioners praise
Among leaders and teams, Looker alone has three praise themes that reach 5 sources from 3 authors. Governed Metrics and Self-Serve Exploration lead, at 9 of 11 and 5 of 8. Answer Quality and Reach Beyond Databricks reach 5 sources from 3 authors only for Databricks Genie, at 13 of 14 and 3 of 6. Looker needs a pilot on the same questions before you choose on either.
| Praised for | Databricks Genie | Looker |
|---|---|---|
| Answer Quality | 13 of 14 | Not a theme in its sources |
| Reach Beyond Databricks | 3 of 6 | Not a theme in its sources |
| Governed Metrics | Not a theme in its sources | 9 of 11 |
| Self-Serve Exploration | Not a theme in its sources | 5 of 8 |
| Gemini & AI Agents | Not a theme in its sources | 2 of 7 |
Each count is the sources that praise a theme, or raise it as a concern, out of those that discuss it. A row pairs the two products when both carry a theme under the same or an equivalent name.
A count ending in "so far" has under 5 sources from 3 authors among leaders and teams. "Not a theme in its sources" means GILD found no theme of that name in that product's sources.
What practitioners raise as concerns
Among leaders and teams, Deployment as Code and Cost & Billing reach 5 sources from 3 authors only for Databricks Genie, at 7 of 8 and 6 of 6. Looker needs a pilot on the same questions before you choose on either. LookML Learning Curve and Cost reach 5 sources from 3 authors only for Looker, at 10 of 11 and 8 of 9. Databricks Genie needs a pilot on the same questions before you choose on either.
| Raised as a concern | Databricks Genie | Looker |
|---|---|---|
| Deployment as Code | 7 of 8 | Not a theme in its sources |
| Cost & Billing | 6 of 6 | Not a theme in its sources |
| LookML Learning Curve | Not a theme in its sources | 10 of 11 |
| Cost | Not a theme in its sources | 8 of 9 |
14 sources
- Databricks docs: How Genie Agents work
- Databricks docs: Create and manage a Genie Agent
- Databricks docs: Tune Genie Agent quality
- Databricks docs: Genie One
- Databricks docs: AI assistive features trust and safety
- Databricks: Genie pricing
- Google Cloud: Looker pricing
- Looker docs: Set up Conversational Analytics in Looker
- Looker docs: Query an Explore with Conversational Analytics
- Looker docs: Gemini in Looker overview
- Looker docs: Release notes
- Google Cloud docs: How Gemini for Google Cloud uses your data
- Databricks blog: Introducing Genie One, Genie Agents, and Genie Ontology (Jun 16, 2026)
- Google Cloud blog: How Looker's semantic layer enhances gen AI trustworthiness (May 7, 2025)