Looker is made to show your data. Count uses it to show your people what to do next.

The Canvas Difference

Count: Figma Meets Your Data

An infinite, collaborative workspace where SQL, Python, and visualizations live together. Build metric trees, process flows, and narrative reports that show relationships between metrics—not just the metrics themselves.

Think:

• Place a chart next to the SQL that powers it
• Map your entire customer journey with live data
• Create a metric tree showing how revenue breaks down
• Add screenshots of your app next to usage analytics
• Collaborate in real-time like Google Docs, but for data

Looker: LookML-Powered Semantic Layer

Enterprise BI platform built around a proprietary modeling language (LookML) that defines metrics centrally. Excellent for governed, consistent reporting across organizations, but constrained by modeling overhead and steep learning curve.

Reality:

• Developers build LookML models (proprietary YAML-based language)
• Business users explore pre-modeled data via Explores
• Changes require LookML updates and deployment
• Reports are generated from the model, not created flexibly
• Strong governance, but slow iteration
• Can't mix SQL, Python, and business context in one workspace

Real-Time Collaboration: Built for How Teams Actually Work

Count's Multiplayer Analytics

Everyone in the canvas at once:

• See teammates' cursors as they work
• Comment directly on cells, visuals, or anywhere on the canvas
• Add sticky notes for context
• @mention colleagues to pull them into the conversation
• Watch queries update live as your team explores together

Role flexibility that makes sense:

• Analysts write SQL/Python and build complex analyses
• Explorers use low-code tools to dig deeper without SQL
• Members can adjust filters and explore without editing queries
• Viewers see everything, can comment, and use interactive filters
• Unlimited collaborators included in every plan

The result: Your CFO can explore revenue breakdowns alongside your analyst, in real-time, without needing to know SQL.

Looker's Model-Based Approach

The workflow:

• LookML developers define semantic models (requires training)
• Deploy models through version control
• Business users explore via pre-built Explores
• Users create Looks (reports) from modeled data
• Dashboards combine Looks into presentations

Looker Standard ($60,000+/year minimum):

• One production instance
• 10 Standard Users (can view, explore, create reports)
• 2 Developer Users (can write LookML)
• Up to 1,000 API calls/month
• For: Small teams with enterprise budget

Looker Enterprise ($150,000+/year average):

• Enhanced security features
• 10 Standard Users, 2 Developer Users (base)
• Up to 100,000 API calls/month
• Additional users sold separately
• For: Mid-to-large enterprises

The constraints:

• Developers work in LookML (separate from users)
• No real-time collaborative exploration
• Every change requires model updates
• Business users limited to pre-modeled data
• Can't easily add business context (screenshots, diagrams)

What's Possible: Use Cases That Shine on Canvas

1. Metric Trees: Show What Drives Growth

With Count: Create a visual hierarchy showing how your North Star metric breaks down. Revenue → Products → Channels → Campaigns, with live data at every node. Your CEO sees the whole picture, then drills into any branch.

With Looker: Define metrics in LookML. Create separate Looks for each level. Build dashboard linking them. Relationships exist in the model code, not visually for users.

2. Product Analytics Meets Product Screenshots

With Count: Place your app's onboarding flow screenshots directly next to the funnel analytics. Drop-off at step 3? The product screenshot is right there. Your PM, designer, and analyst see the same view.

With Looker: Create Look with funnel data. Screenshots and context live in separate documentation. Present in meetings.

3. Customer Journey Mapping

With Count: Map the entire journey—ads, website visits, signups, activation, retention—in a single canvas with real data flowing through it. Sales, marketing, and product see how their work connects.

With Looker: Model each stage in LookML. Create Explores for each. Build dashboard. Explain connections in documentation.

4. Deep-Dive Analysis That Tells a Story

With Count: Break complex SQL into connected cells that flow visually. Show your thinking, not just your charts. Stakeholders follow the logic from raw data to insight without a separate deck.

With Looker: Need something not in the model? Submit request to LookML developer. Wait for sprint planning. Developer updates model. Deploys to production. You explore. Timeline: days to weeks.

6. Collaborative Problem-Solving

With Count: "Let's figure this out together." Analyst, PM, and CFO work in the same canvas simultaneously. Analyst writes SQL. PM adds product screenshots. CFO asks questions via comments. Everyone contributes in real-time.

With Looker: "Let me build something and show you." Analyst creates Looks. Shares dashboard link. Team provides feedback async. Analyst iterates. Repeat. Days of back-and-forth instead of one collaborative session.

Quick Decision Framework

Choose Looker if:

• You're a 500+ person enterprise with BI budget over $100,000
• Central governance is more important than exploration speed
• You need embedded analytics for customer-facing products
• You have (or can hire) LookML developers
• You're deeply embedded in Google Cloud Platform
• Compliance requires enterprise-grade governance
• You can justify $60,000-$150,000+ annual commitment

Choose Count if:

• You're a 50-500 person company
• Budget is under $20,000/year for BI
• You need insights in minutes, not weeks
• You want collaboration, not just governance
• Learning LookML isn't worth the time/cost
• You prefer SQL/Python over proprietary languages
• Real-time co-creation matters more than central control
• Your team deserves a workspace, not just pre-modeled reports