GILD verdict
Choose Looker for governed dashboards and AI answers on one LookML model shared across departments and tools.
Choose Hex for SQL and Python analysis published as notebooks and apps, with listed seat prices and credit caps. A move to Hex needs a plan for LookML definitions, which its migration tool turns into SQL cells and guides by default.
Whichever you pick, budget for written definitions first. In an April 2026 benchmark, three frontier models gained 17 to 23 points of accuracy when handed a 4 KB semantic document. Looker keeps definitions, joins, and access in LookML, runs dashboards live against the warehouse, and has Gemini compose queries from the model. Hex's agents read semantic models and endorsed data beside SQL and Python notebooks and published apps.
Choosing between Looker and Hex
Choose Looker if
- Departments need governed dashboards and self-serve Explores on one set of metric definitions, with model changes reviewed in Git.
- AI answers must follow the same joins, access grants, and user attributes as every dashboard.
- Business users work in Google Sheets, Excel, Power BI, or Gemini Enterprise and should reach the same model from there.
Choose Hex if
- Analysts spend most of their time in SQL and Python analysis that ends in notebooks and published apps.
- You want per-seat pricing with published list prices and AI credits you can cap by user and group.
- Your metric definitions already live in dbt MetricFlow, Cube, or Snowflake Semantic Views, which Hex syncs as semantic models.
Side by side
| Dimension | Looker | Hex |
|---|---|---|
| Where definitions live | LookML models, views, and Explores in Git, with access grants and user attributes applied to every query | Semantic models authored in Hex or synced from dbt MetricFlow, Cube, or Snowflake Semantic Views, plus endorsed data and workspace guides; LookML is not a sync source |
| AI for business users | Conversational Analytics in Looker, data agents over up to five Explores with instructions and verified queries, dashboard agents (pre-GA), and agents published to Gemini Enterprise (pre-GA) | Threads for people outside the data team, in Endorsed Mode by default for Explorers |
| How AI builds a query | Gemini picks fields, filters, and sorts; Looker composes and runs the SQL from the LookML model, up to 50,000 rows per query | The agent writes SQL and Python against connected data, prioritizing semantic models and endorsed assets |
| Deeper analysis | Advanced Analytics runs Python on query results inside data agents; Explores, table calculations, and SQL Runner for analysts | SQL, Python, and no-code cells in notebooks, published as apps, with the Notebook Agent for technical users |
| PricingAs published by each vendor. | Annual platform edition through sales (Standard for fewer than 50 users, Enterprise, or Embed). Each includes 10 Standard and 2 Developer users, plus Developer, Standard, and Viewer licenses. Google publishes no list price | Community free; Professional US$36 and Team US$75 per Editor per month, billed monthly on self-serve; Enterprise custom, billed annually |
| AI usage limits | Conversational Analytics data tokens are pooled per instance and expire monthly without rollover. Monthly allowances: 60M input and 1.2M output on Standard; 300M and 6M on Enterprise; 1.2B and 24M on Embed. Overage of US$3.00 per million input and US$20.00 per million output tokens applies only after the promotional period ends, with at least 90 days' notice | Monthly credit grants per seat (30 on Professional, 40 per Team Editor, 10 per Team Explorer) that do not roll over; auto top-ups buy 50 credits for US$25 |
| Data handling | Gemini does not train on prompts or responses; Conversational Analytics data at rest stays in the instance's region, with model processing on a global service unless EU localized processing applies | Model providers under zero data retention by default and no training on customer data; Hex may use AI session data depending on plan, with an opt-out |
| Moving between them | BI connectors let Sheets, Excel, Power BI, Tableau, and Data Studio read Looker models | A Looker migration skill and the Hex CLI rebuild Looker dashboards as Hex apps and compare the numbers with Looker's own results |
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 Hex +65, 73 points above Looker at −8. Most of the gap is in praise, with 22 of Hex's 33 sources positive against 7 of Looker's 27. Hex has 0 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 Looker's −8 rests on votes split 7 positive, 10 mixed and 9 negative and Hex's +65 on 20, 11 and 0.
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
Both cover the same 7 layers at the same depth. 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.
| Stage | Looker | Hex |
|---|---|---|
| Assist1 layer | Touches 1 | Touches 1 |
| Find2 layers | Touches 2 | Touches 2 |
| Create2 layers | Touches 2 | Touches 2 |
| Analyze1 layer | Core 1 | Core 1 |
| Automate1 layer | Supports 1 | Supports 1 |
Show all 7 layers
| Layer | Looker | Hex |
|---|---|---|
| Assist | ||
| AI assistant | Touches | Touches |
| Find | ||
| Web research | Touches | Touches |
| Enterprise search | 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, Hex alone has four praise themes that reach 5 sources from 3 authors. AI Agents and Analysis Workspace lead, at 15 of 17 and 11 of 12. 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.
| Praised for | Looker | Hex |
|---|---|---|
| Governed Metrics | 9 of 11 | Not a theme in its sources |
| Self-Serve Exploration | 5 of 8 | Not a theme in its sources |
| Gemini & AI Agents | 2 of 7 | Not a theme in its sources |
| AI Agents | Not a theme in its sources | 15 of 17 |
| Analysis Workspace | Not a theme in its sources | 11 of 12 |
| Data Apps & Sharing | Not a theme in its sources | 7 of 10 |
| Collaboration | Not a theme in its sources | 5 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, LookML Learning Curve and Cost reach 5 sources from 3 authors only for Looker, at 10 of 11 and 8 of 9. Hex needs a pilot on the same questions before you choose on LookML Learning Curve and has 2 of 3 on Cost so far. Speed reaches 5 sources from 3 authors only for Hex, at 6 of 8. Looker needs a pilot on the same questions before you choose on it.
| Raised as a concern | Looker | Hex |
|---|---|---|
| LookML Learning Curve | 10 of 11 | Not a theme in its sources |
| Cost | 8 of 9 | 2 of 3 so far |
| Speed | Not a theme in its sources | 6 of 8 |
14 sources
- Looker pricing
- Looker docs: Conversational Analytics in Looker overview
- Looker docs: Create and manage Explore data agents
- Looker docs: Enable and use Advanced Analytics
- Looker docs: Admin settings, BI Connectors
- Google Cloud docs: How Gemini products in Google Cloud use your data
- Hex pricing
- Hex docs: Credits
- Hex docs: Data connections introduction
- Hex docs: Introduction to semantic modeling
- Hex docs: Threads
- Hex docs: AI data privacy
- Hex docs: Migrate Looker dashboards
- arXiv: Semantic Layers for Reliable LLM-Powered Data Analytics, by Rumiantsau and Fokeev (Apr 28, 2026)