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Overview

Hex is a collaborative data workspace combining Jupyter Notebook-style coding with BI dashboards. It integrates SQL + Python + R execution. The AI features formerly branded in-product as "Hex Magic" are now organized as Hex AI, and a set of purpose-specific agents sits at the center of the product.

Key Features

Purpose-Specific AI Agents

There is a Notebook agent that writes SQL/Python cells, Threads for non-technical users asking questions in chat, a Modeling agent for building semantic models, a Chat with App agent for app consumers, and a Generative Apps agent (beta) that builds data apps.

Model Choice

As of August 2026 you can select GPT-5.6 (Sol / Terra / Luna), Claude Opus 5, and Fable 5, with a Fast Mode that speeds up token output on Opus models.

Agents from CLI and API

The Hex Agent can now be invoked from the command line and the API, so it fits into terminal-centric workflows.

Context Studio and Evals

Context Studio lets teams observe, test, and deploy the context given to agents, and an Evals framework measures agent performance before production.

Integrated Data Analysis

  • SQL — direct DB queries
  • Python — pandas, numpy, scikit-learn
  • R — statistical analysis
  • Charts — interactive visualizations
  • App deployment — turn notebooks into dashboards

Broad Integrations

Snowflake, BigQuery, Databricks, PostgreSQL, MySQL, dbt, etc.

Team Collaboration

Real-time co-editing, comments, version control, permissions.

Pricing

There are four tiers — Community, Professional, Team, and Enterprise — with Professional at $36/editor/month and Team at $75/editor/month.

PlanMonthlyHighlights
Community$0Basics; Notebook agent trial only
Professional$36/editorNotebook agent, unlimited notebooks, up to 5 published apps
Team$75/editorThreads agent, Modeling agent, unlimited published apps, scheduling
EnterpriseContactSSO, audit, governance, dedicated support

Large machines and GPUs are billed pay-as-you-go ($0.32–$6.70/hour).

Strengths

  • ✅ Purpose-specific agents cover both technical and non-technical users
  • SQL + Python + R integration
  • Rich team collaboration
  • ✅ Broad data warehouse integrations
  • ✅ Notebook-to-app deployment
  • ✅ Agents runnable from CLI and API

Weaknesses

  • $75/editor is expensive
  • ❌ Overkill for small teams
  • ❌ Steep learning curve
  • ❌ Extra compute costs on top of seats

Official Resources

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