What is Looker?

Looker is Google Cloud's enterprise BI platform, built around LookML, a semantic modelling layer where business metrics are defined once in code and reused across every dashboard, report and API call. That single-source-of-truth approach is what large organisations buy it for: it prevents the situation where three teams report three different revenue figures. It integrates tightly with BigQuery, supports embedded analytics, and adds Gemini AI for conversational querying.

Two things to be clear about. Looker and Looker Studio are different products despite the shared name: Looker Studio is free and aimed at marketing dashboards, while Looker is a governed enterprise platform with reported entry costs in the low thousands per month and quote-based contracts. And LookML is a real engineering commitment, so Looker rewards teams with data engineering capacity and frustrates those without it.

Key Features

LookML Semantic LayerGemini AI Conversational AnalyticsBigQuery Native IntegrationEmbedded Analytics SDKAPI-First ArchitectureRow-Level SecurityCustom VisualizationsScheduled DeliveriesDashboard FiltersGit Version ControlSSO and SAML AuthenticationMulti-Database SupportLooker Studio IntegrationGoverned Self-ServiceEnterprise Audit Logs

How to Use Looker

1Connect Looker to your data warehouse (BigQuery, Snowflake, Redshift, or others)

2Define your business metrics, dimensions, and relationships in LookML

3Users explore data through Looker Explores or ask questions using Gemini Conversational Analytics

4Build and publish branded dashboards with governed access controls

5Share dashboards via scheduled emails, Slack, or embed them inside your SaaS product via API

Who is Looker for?

✅ Best For

Data-mature enterprises already on Google Cloud and BigQuery that need a governed semantic layer defining company metrics once, so every dashboard and report uses the same trusted definitions.
SaaS companies building embedded, customer-facing analytics into their product who need a flexible, API-first BI platform that white-labels beautifully and scales to thousands of end users.

❌ Not For

Teams without SQL developers or data engineers who can build and maintain LookML models, as Looker requires significant technical investment before non-technical users can self-serve effectively.
Organizations on tight budgets, as Looker's base platform starts at $60,000 per year plus per-viewer and per-creator seat costs, making it one of the more expensive BI options in the market.

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Pricing

Is Looker free? No, there is no free plan.

StandardCustom pricing

✓1 instance
✓10 users
✓up to 1,000 query API calls/month

EnterpriseCustom

✓Enhanced security
✓100K query API calls/month
✓multi-instance

EmbedCustom

✓500K query API calls/month
✓external embedding
✓white-label

Prompts to Try

Show me monthly recurring revenue broken down by pricing plan and cohort

Which product features are most correlated with enterprise deal conversion?

Build a churn risk dashboard for our top 100 accounts by ARR

Embed a real-time performance dashboard in our customer portal

Which sales territories are underperforming against quota this quarter?

Use Cases

A SaaS company's data team defines all revenue metrics in LookML once and publishes them to 150 stakeholders via Looker, ensuring every department reports the same MRR and churn figures without conflicting spreadsheets.

A product analytics lead uses Gemini Conversational Analytics to ask which onboarding steps most strongly predict 90-day user retention, getting an AI-generated answer from live BigQuery data in seconds.

A B2B SaaS startup embeds Looker dashboards into their customer portal so each enterprise client can view their own product usage analytics with the startup's branding, driving a 34% increase in feature discovery.

An operations manager sets up automated Looker report deliveries to 8 department heads every Monday morning, replacing an analyst-intensive process that consumed 12 hours per week.

A data engineer uses Looker's Git version control to manage LookML changes with pull requests and code reviews, bringing software engineering best practices to the analytics layer.