Looker
Google Cloud's semantic-layer BI with embedded analytics and conversational AI
What is Looker?
Looker is Google Cloud's governed BI and embedded analytics platform built around a centralized semantic layer (LookML) that enforces consistent metric definitions across the organization. It supports self-service exploration, embedded analytics for product teams, and Conversational Analytics with a per-user data token quota. Information Gain: until September 30, 2026 Looker's Conversational Analytics data tokens are unlimited under fair use, after which overages bill at $3/1M input and $20/1M output tokens.
Knowledge bases, internal search, operations, data, finance, HR, and back-office tools with AI workflows.
See the full Knowledge & Ops guide to compare more tools, buyer criteria, and related workflows.
Use cases to evaluate
Define governed company-wide metrics in LookML used across teams
Embed white-labeled dashboards into a SaaS product via Embed edition
Power Conversational Analytics on top of BigQuery and other warehouses
Enforce a single source of truth for KPIs across Sales, Finance, and Product
Fit to evaluate
Mid-market to enterprise data teams standardizing on a semantic layer
Google Cloud customers consolidating analytics on BigQuery
Product teams embedding analytics for external customers
Organizations where metric drift between teams has become a real problem
Business fit
Right for you if metric consistency, governance, and a single source of truth matter more than fast self-serve dashboarding. Right for you if you embed analytics into a product or already run on Google Cloud. Skip if you need transparent per-seat pricing or want to start small without sales conversations. Skip if your data team prefers code-first dbt-native tools or a lightweight open-source BI.
How to evaluate Looker
Use this category when operational data, policies, tasks, or internal requests are spread across disconnected systems.
Confirm the exact workflow
Map Looker to one concrete workflow first, such as define governed company-wide metrics in lookml used across teams. Avoid buying before the owner, trigger, output, and success metric are clear.
Check category fit
Compare internal search, permissions, workflow support, and reporting.
Compare practical alternatives
Shortlist Looker against Glean, Guru, Slite so the decision is based on fit, effort, and workflow ownership rather than brand recognition alone.
Validate cost and rollout effort
Pricing not publicly listed; contact sales for quotes. Platform editions (Standard for <50 users, Enterprise, Embed) require 1-, 2-, or 3-year annual commitments and each include 1 production instance, 10 Standard Users and 2 Developer Users. User token quotas: Viewer 1M input/20K output per month, Standard 2M/40K, Developer 4M/80K. Post-Sept 30, 2026 overages: $3/1M input, $20/1M output tokens. Also confirm implementation time, support needs, and whether the medium setup matches your team.
Compare Looker with alternatives
Use this quick comparison before booking demos or moving data into a new system.
| Primary workflow | Define governed company-wide metrics in LookML used across teams, Embed white-labeled dashboards into a SaaS product via Embed edition |
|---|---|
| Best-fit team | Mid-market to enterprise data teams standardizing on a semantic layer, Google Cloud customers consolidating analytics on BigQuery |
| Implementation effort | Medium setup and maintenance profile |
| Pricing check | Contact sales |
| Closest alternatives | GleanGuruSliteSlab |
Looker pricing
| Model | Contact sales |
|---|---|
| Snapshot | Pricing not publicly listed; contact sales for quotes. Platform editions (Standard for <50 users, Enterprise, Embed) require 1-, 2-, or 3-year annual commitments and each include 1 production instance, 10 Standard Users and 2 Developer Users. User token quotas: Viewer 1M input/20K output per month, Standard 2M/40K, Developer 4M/80K. Post-Sept 30, 2026 overages: $3/1M input, $20/1M output tokens. |
| Checked |
Common questions about Looker
What is Looker?
Looker is Google Cloud's governed BI and embedded analytics platform built around a centralized semantic layer (LookML) that enforces consistent metric definitions across the organization. It supports self-service exploration, embedded analytics for product teams, and Conversational Analytics with a per-user data token quota. Information Gain: until September 30, 2026 Looker's Conversational Analytics data tokens are unlimited under fair use, after which overages bill at $3/1M input and $20/1M output tokens.
What is Looker used for?
Common use cases: Define governed company-wide metrics in LookML used across teams; Embed white-labeled dashboards into a SaaS product via Embed edition; Power Conversational Analytics on top of BigQuery and other warehouses; Enforce a single source of truth for KPIs across Sales, Finance, and Product.
How much does Looker cost?
Pricing not publicly listed; contact sales for quotes. Platform editions (Standard for <50 users, Enterprise, Embed) require 1-, 2-, or 3-year annual commitments and each include 1 production instance, 10 Standard Users and 2 Developer Users. User token quotas: Viewer 1M input/20K output per month, Standard 2M/40K, Developer 4M/80K. Post-Sept 30, 2026 overages: $3/1M input, $20/1M output tokens.
Who is Looker best for?
Looker fits Mid-market to enterprise data teams standardizing on a semantic layer, Google Cloud customers consolidating analytics on BigQuery, Product teams embedding analytics for external customers, Organizations where metric drift between teams has become a real problem. Right for you if metric consistency, governance, and a single source of truth matter more than fast self-serve dashboarding. Right for you if you embed analytics into a product or already run on Google Cloud. Skip if you need transparent per-seat pricing or want to start small without sales conversations. Skip if your data team prefers code-first dbt-native tools or a lightweight open-source BI.
What are alternatives to Looker?
Common alternatives to Looker include Glean, Guru, Slite, Slab, Tettra, Sana.