Google Looker
Looker is Google Cloud's business intelligence and embedded analytics platform. Its website calls it an agentic BI platform built on a trusted semantic layer. You define business metrics once in LookML, and dashboards, conversational analytics and other tools share them.
What Looker offers
The details below come from the Looker page on Google Cloud, its pricing page and the Looker documentation, read on October 24, 2026. Looker is sold on annual commitments, so Google Cloud sales has the actual numbers.
| Looker capability | What Google Cloud says | Reference |
|---|---|---|
| Semantic layer (LookML) | Sits above your cloud data warehouse and translates complex SQL into governed business metrics once. People and AI agents work from the same definitions | Semantic layer |
| Conversational Analytics | Ask questions in natural language. Gemini-powered agents interpret the data and can trigger follow-up actions. APIs let you add this to your own apps | Conversational Analytics |
| Looker Embedded | Build customer-facing data experiences and AI-first apps | Looker website |
| Looker on Google Cloud | Create instances in minutes, with Google Cloud IAM single sign-on, Private Service Connect, customer-managed encryption keys, native BigQuery integration and unified billing | Looker website |
| Self-service analysis | Blend ad hoc CSVs and local files with governed LookML models | Looker website |
On pricing, the pricing page lists three platform editions: Standard, Enterprise and Embed. All use annual commitments and require a quote from sales. Each platform includes ten Standard users and two Developer users. User licenses come as Developer, Standard and Viewer.
Who it is for
Looker's website addresses data teams that want one definition of each metric:
- Companies with a data warehouse that want metric definitions in one place so every dashboard shows the same numbers.
- BigQuery users. The website says Looker integrates natively with BigQuery.
- Teams building data products for customers with embedded analytics in Looker Embedded.
LookML metrics for SaaS competitor reviews
Looker does not store data. It connects to a database or warehouse and models reports on top of it (see connecting to your database and BigQuery setup). So the plan is to put outside data in BigQuery first and then define it once in LookML. A product marketing team at a B2B software company could work like this:
- Use the EveryInfra G2 reviews, G2 alternatives and Capterra reviews endpoints to pull ratings, review text and reviewer industry for your product and a handful of competitors.
- Add website traffic and audience profiles for each vendor with the Similarweb competitors and audience endpoints.
- Write the results to a few BigQuery tables every day: reviews, rating snapshots and traffic snapshots.
- In LookML, define dimensions and measures such as 90-day average rating, share of negative reviews, or reviews that mention pricing. Sales, product and leadership then see the same number instead of three spreadsheets with three answers.
- Build a dashboard that compares rating trends and traffic by quarter and use it to prepare competitive battle cards.
Review sites add only a few new reviews a day, so incremental pulls are cheap, and requests that find nothing new are not charged.
Before you contact Google Cloud
All three Looker editions, Standard, Enterprise and Embed, are quoted by sales; see the Looker pricing page. Load a sample of G2 reviews into BigQuery first, so you can discuss users and editions with concrete metrics in hand. Terms are set on the Google Cloud website; this page only collects what Google publishes.