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Count

data-analysis
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Modern analytics workspace combining SQL, Python, and narrative text in one collaborative document - AI generates charts and queries from plain English descriptions.

Count is a London-based analytics workspace that unifies SQL notebooks, Python cells, Markdown narrative text, and interactive charts in a single shareable document - replacing the fragmented workflow of writing queries in one tool, building charts in another, and writing commentary in a third. The AI layer generates SQL and Python code from natural language descriptions and suggests chart types based on the query output, significantly reducing the time from question to insight. Count is built around the "data story" concept: analyses are living documents rather than static dashboards, combining query results and explanatory text in a format that both data and non-data team members can understand. The team plan at $25/user/month includes private document sharing, version history, and AI access.

#data-analytics
#sql-notebooks
#data-visualization
#data-analysis
#developer-tools
Freemium

Free plan available

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count.co
Freemium
Pricing Model
Data & Analytics
Category
2020
Since
Free Plan
Access

Key Features

  • Mixed-cell notebooks - SQL, Python, and narrative Markdown cells in one document with live query results
  • AI code generation - writes SQL queries and Python data transformations from plain English descriptions
  • AI chart suggestions - automatically recommends the best chart type and configuration for each query output
  • Live data connections - connects to Snowflake, BigQuery, Redshift, Postgres, and other warehouses directly
  • Shareable data stories - publish analyses as readable, stakeholder-friendly documents instead of static exports
  • Version history - tracks all changes to queries, charts, and narrative so analyses are fully auditable over time

Use Cases

  • Data analysts combining SQL exploration, Python analysis, and stakeholder commentary in one document instead of three tools
  • Data teams presenting analysis results to non-technical stakeholders in a narrative format that explains the context
  • Analytics engineers documenting data pipeline logic alongside the actual query results for future reference
  • Product teams running their own SQL queries with AI assistance without depending on a data analyst for every question

Pros

  • SQL plus narrative in one document - eliminates the fragmented workflow of Jupyter, Google Docs, and Tableau in parallel
  • AI SQL generation from plain English lets analysts focus on the question rather than the query syntax
  • Shareable data story format is more readable than dashboards for contextualizing one-time or recurring analyses

Cons

  • Smaller than established BI tools - fewer pre-built data connectors and less community content than Metabase or Hex
  • $25/user/month for team private documents is comparable to but not cheaper than alternatives like Deepnote or Hex
  • Not designed for always-on dashboards - Count works best for exploratory analysis and reporting, not operational monitoring

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