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Data Analytics & Predictive Modeling

Hex

Collaborative data notebooks with an AI copilot

Freemium - from $36 per editor per monthRuns in the cloud

Pricing

Freemium - from $36 per editor per month

$36 per editor per month

Platforms

Web

Hosted app

Category

Data Analytics & Predictive Modeling

13 tools

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Last updated

Sep 23

Listing updated

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Turn notebooks into stakeholder apps

Publish an analysis with dropdowns and filters so product and business teams self-serve answers.

Pricing checked Sep 2026

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About Hex

Hex is a collaborative data notebook that combines SQL, Python, and no-code cells in a single canvas, where cells reference each other's outputs instead of copy-pasted glue code. Finished notebooks publish as interactive apps with dropdowns and filters, so stakeholders consume an analysis without touching the underlying cells.

Hex Magic, the built-in AI toolkit, autocompletes SQL with awareness of your live schema, fixes errors, and generates transformation code from natural language. Native connections cover Snowflake, BigQuery, Databricks, Postgres, and Redshift, with version control support for team workflows.

It fits analytics and data science teams that ship findings to non-technical audiences. A free Community workspace covers individuals; the Team plan is $36 per editor per month, and viewers are free, which keeps sharing cheap. Very large notebooks get unwieldy, and editor seats cost more than generic notebook tooling.

  • SQL, Python, and no-code cells share one canvas
  • Published apps make analyses self-serve for stakeholders
  • Magic AI is schema-aware, not generic autocomplete
  • Viewers are free, so sharing widely is cheap
  • Editor seats cost more than generic notebook tools
  • Very large notebooks get slow and hard to navigate
  • SSO and audit features sit in the Enterprise tier

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Platforms that turn raw data into decisions: automated forecasting, churn and risk models, CRM-native predictions, and plain-English BI copilots. Listings document how models handle messy real-world data, where they deploy (cloud or self-hosted), and what governance tooling exists for regulated teams.

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