GILD verdict
If your metrics live in LookML across several warehouses, start with Looker's Conversational Analytics.
It reuses that model and its access grants. Choose Snowflake CoWork when your data is in Snowflake, questions mix tables and documents, and you prefer usage billing to seat licenses. In both, the semantic layer and verified queries your data team writes decide answer quality more than the product does.
Pick by where your metric definitions live, because each vendor's own testing credits its semantic layer for accuracy. Google reports that LookML cut data errors in generative AI queries by up to two thirds in its internal tests. Snowflake reports that AI answered about 25% of its internal product analytics questions correctly without a context layer.
Choosing between Snowflake CoWork and Looker
Choose Snowflake CoWork if
- Your data is in Snowflake and you want metrics defined in Snowflake semantic views, outside any BI tool's model.
- Questions need documents as well as tables, which CoWork answers through Cortex Search in the same agent.
- You want usage-based billing with no seat licenses, capped by resource budgets and per-user quotas.
Choose Looker if
- Your metrics already live in LookML and your teams use Looker dashboards and Explores.
- Data sits in several warehouses, such as BigQuery, Redshift, Databricks, and Snowflake, behind one semantic model.
- You want conversational answers beside governed dashboards, with dashboard agents in beta that query a dashboard's own models.
Side by side
| Dimension | Snowflake CoWork | Looker |
|---|---|---|
| Data it reaches | Tables in the customer's Snowflake account through Cortex Analyst on semantic views, documents through Cortex Search, and uploaded files | Any database Looker connects to through LookML, including BigQuery, AlloyDB, Redshift, Snowflake, and Databricks, up to 50,000 rows per query |
| Where business context lives | Snowflake semantic views with metrics, synonyms, and verified queries, plus agent orchestration instructions | The LookML model, with data agent instructions, business glossaries, and verified queries for up to five Explores per agent |
| How queries are written | Cortex Analyst generates SQL against semantic views; an exact match to a verified query runs its validated SQL directly | Gemini picks fields, filters, and sorts, and Looker composes the SQL from LookML, so joins and access grants follow the model |
| Testing accuracy | Agent evaluations for answer correctness and logical consistency, with tool selection and execution metrics in beta, plus traces and user feedback | System Activity dashboards for engagement and token use, with query and feedback review in beta |
| PricingAs published by each vendor. Looker includes a monthly data token allowance per edition, from 60 million input tokens on Standard. | No seat fee. AI Credits cost US$2.00 per AI Credit on global routing and US$2.20 on regional routing, on demand. Contract-value discounts bring them down to US$1.88 and US$2.07. Orchestration bills per million tokens at the selected model's rate. For example, claude-sonnet-5 costs 1.30 AI Credits per million input tokens and 6.50 per million output tokens. Warehouse compute is billed on top | Platform editions (Standard, Enterprise, Embed) on annual contracts through sales, plus Developer, Standard, and Viewer user licenses. Conversational Analytics data tokens are currently promotional, with overage of US$3.00 per million input and US$20.00 per million output tokens after at least 90 days' notice |
| Access control | Each user's Snowflake roles, row access policies, and masking apply to every query; admins can restrict users to CoWork only | Looker roles with access_data, gemini_in_looker, and chat_with_agent permissions on the underlying models, plus LookML access grants |
| AI data use | Snowflake never uses Customer Data to train models offered to customers; CoWork inputs are classified as Usage Data and outputs as Customer Data | 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 Snowflake CoWork +36, 44 points above Looker at −8. Most of the gap is in complaints, with 10 of Looker's 27 sources negative against 4 of Snowflake CoWork's 26.
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 Snowflake CoWork's +36 rests on votes split 12 positive, 10 mixed and 3 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, Snowflake CoWork supports it and Looker touches it. On Enterprise search, Snowflake CoWork 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 | Snowflake CoWork | Looker |
|---|---|---|
| Assist | ||
| AI assistant | Supports | Touches |
| Find | ||
| Enterprise search | Supports | Touches |
The 5 layers where they match
| Layer | Snowflake CoWork | 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. Plain-Language Answers and Fast Setup reach 5 sources from 3 authors only for Snowflake CoWork, at 7 of 7 and 6 of 6. Looker needs a pilot on the same questions before you choose on either.
| Praised for | Snowflake CoWork | Looker |
|---|---|---|
| Plain-Language Answers | 7 of 7 | Not a theme in its sources |
| Fast Setup | 6 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
Looker's sources raise Cost more often than Snowflake CoWork's. It is raised in 4 of the 7 Snowflake CoWork sources that discuss it, against 8 of 9 for Looker. Access Setup & Availability and Answer Accuracy reach 5 sources from 3 authors only for Snowflake CoWork, at 6 of 6 and 4 of 5. Looker needs a pilot on the same questions before you choose on either. LookML Learning Curve reaches 5 sources from 3 authors only for Looker, at 10 of 11. Snowflake CoWork needs a pilot on the same questions before you choose on it.
| Raised as a concern | Snowflake CoWork | Looker |
|---|---|---|
| Cost | 4 of 7 | 8 of 9 |
| Access Setup & Availability | 6 of 6 | Not a theme in its sources |
| Answer Accuracy | 4 of 5 | Not a theme in its sources |
| LookML Learning Curve | Not a theme in its sources | 10 of 11 |
15 sources
- Snowflake docs: Overview of Snowflake CoWork
- Snowflake docs: Snowflake AI pricing
- Snowflake Service Consumption Table (effective Oct 15, 2026)
- Snowflake docs: Snowflake CoWork reference
- Snowflake docs: User access and settings for agents
- Snowflake docs: Cortex Agent evaluations
- Snowflake docs: Cortex Analyst Verified Query Repository
- Snowflake docs: Snowflake AI and ML
- Google Cloud: Looker pricing
- Google Cloud: Looker
- Looker docs: Conversational Analytics in Looker overview
- Looker docs: Set up Conversational Analytics in Looker
- Google Cloud docs: How Gemini products in Google Cloud use your data
- Snowflake blog: Cortex Sense for Enterprise AI Agents (Jun 30, 2026)
- Google Cloud blog: How Looker's semantic layer enhances gen AI trustworthiness (May 7, 2025)