Count is an agentic BI platform that replaces rigid dashboards with a multiplayer canvas where analysts, operators, and stakeholders explore data together in real time. It combines SQL, Python, and AI-driven analysis in a single notebook-style workspace connected directly to your data warehouse.
Product Overview
Count reimagines business intelligence as an open canvas rather than a static report — analysts write SQL or Python in cells, layer in visualizations, and share a live workspace where non-technical collaborators can add comments, sticky notes, and filters without touching the underlying queries. An AI agent can interpret natural-language prompts, write queries, and build charts autonomously, while DuckDB handles in-browser query caching so most downstream exploration never hits the warehouse. Count Metrics adds a governed semantic layer so that KPI definitions stay consistent across every canvas.
Key Features
- Multiplayer Canvas: A real-time shared workspace where analysts, business users, and stakeholders collaborate simultaneously — editing, commenting, and exploring the same living analysis.
- AI Agentic Analysis: An AI agent reads your data schema, writes SQL or Python, builds visualizations, and chains multi-step analytical operations from a plain-English prompt.
- SQL + Python Cells: Analysts write raw SQL or Python inline on the canvas, with results rendering immediately as tables or charts without leaving the workspace.
- Count Metrics Semantic Layer: Version-controlled, governed metric definitions that ensure every canvas and report references the same KPI logic, eliminating conflicting numbers across teams.
- In-Browser DuckDB Engine: After the initial warehouse query, downstream exploration runs entirely in the browser via DuckDB — making iteration fast without repeatedly hitting Snowflake, BigQuery, or Redshift.
Best For
Data teams at mid-market companies who want analysts and business stakeholders to collaborate inside the analysis itself rather than in separate Slack threads or slide decks.
Pricing
Free tier available; paid plans start at $34/editor/month. Viewer and collaborator roles available at lower cost. 14-day trial, no credit card required.
Key Integrations
Snowflake, BigQuery, Databricks, Redshift, PostgreSQL, MySQL, dbt, DuckDB
Pros
- Combines SQL, Python, and collaborative whiteboarding in one interface, eliminating context-switching between tools.
- AI agent is schema-aware, making it genuinely useful for exploratory analysis rather than just autocomplete.
- In-browser DuckDB caching speeds up iterative exploration without extra warehouse compute costs.
- Customer support is consistently praised for responsiveness and willingness to incorporate feedback quickly.
Cons
- Performance can degrade on very large initial dataset pulls when working with tens of millions of rows.
- The open-canvas model is less intuitive than traditional dashboard tools for purely report-consumption use cases.
- Chart styling and layout customization is limited compared to dedicated visualization tools like Tableau.
RevOps Jobs-to-Be-Done
- Collaborative Data Analysis for RevOps — Build SQL-based revenue analytics workflows in a collaborative notebook environment that non-engineers can read and run. KPI: Reduce time-to-insight for RevOps data questions from days to hours with shareable, executable notebooks
- No-Code Data Exploration — Allow marketing and sales operations to explore data and build charts without writing SQL using Count's visual canvas. KPI: Enable non-technical stakeholders to answer 70% of their own data questions without analyst involvement
- Pipeline and Funnel Analytics — Connect to CRM data warehouse exports and build live pipeline and funnel analytics dashboards in an exploratory canvas format. KPI: Replace static weekly pipeline reports with live explorable dashboards updated on demand
How It Fits Your Stack
Primary system of record: Data warehouses and databases
Key integrations: Snowflake, BigQuery, Redshift, PostgreSQL, MySQL, dbt
Data flows: Direct database/warehouse connections; SQL and visual analysis layers; results shared via canvas or embedded; no data copying required
Security & Compliance
- SSO / SAML: Google SSO and SAML
- RBAC / permissions: Yes
- Audit logs: Yes
- Certifications: SOC 2 Type II
- Data residency: US and EU
Implementation & Ownership
- Time to first value: 1–2 days
- Implementation complexity: Low — database connection and collaborative canvas
- Typical owners: Data Analyst, RevOps Analyst, Analytics Engineer
Strong for teams that want a collaborative data canvas beyond traditional BI; competes with Hex and Mode
Proof & Buyer Signals
Ratings: G2: 4.6/5 (50+ reviews)
What buyers praise:
- Collaborative notebook format is unique
- Great for mixed SQL/no-code teams
- Fast for exploratory analysis
Common complaints:
- Less suitable for large-scale dashboard distribution
- Smaller integration ecosystem vs major BI tools
Often Compared With
- Mode Analytics — Mode is more established with broader enterprise adoption; Count has a more modern collaborative canvas focused on exploration vs reporting
- Equals — Equals is spreadsheet-native; Count is database-native for teams who prefer SQL and collaborative data notebooks
- Observable — Observable uses JavaScript-based notebooks for developers; Count provides a more accessible SQL + visual canvas for business-facing analysts
