Hex

Hex is an AI-powered data analysis and notebook platform that combines the flexibility of notebooks with the power of AI. It allows data teams to explore, analyze, and visualize data using natural language queries.

Hex offers a free plan for individual users. Professional plans start at $36 per editor per month, and Team plans at $75 per editor per month with advanced collaboration features. Enterprise plans with custom pricing include SSO, audit logs, and dedicated support.

Yes, Hex AI can generate SQL queries from natural language descriptions. You describe what data you want, and Hex writes and executes the appropriate SQL against your connected databases.

Hex provides a more polished, collaborative experience with AI assistance and direct database connections. Jupyter is free and more flexible for Python-heavy workflows. Hex suits data teams; Jupyter fits individual data scientists.

Yes, Hex connects to major databases including PostgreSQL, MySQL, BigQuery, Snowflake, Redshift, and Databricks. You can query databases directly within Hex notebooks.

Yes, Hex allows you to create interactive data visualizations and dashboards from your analyses. You can share these with stakeholders who can interact with filters and parameters without writing code.

Yes. Hex is designed around real-time multiplayer editing, so multiple people can work in the same notebook simultaneously the way a team would in a shared document. Cells, charts, and comments update live for everyone, which makes pairing on an analysis or reviewing how a teammate built it straightforward. Finished projects can then be published as interactive reports, so stakeholders who never touch the notebook still consume the results through dashboards or scheduled apps.

Partly. The AI natural language to SQL generation lets a non-technical teammate ask questions in plain English, and published dashboards are easy to consume, so analysts can serve the whole team from one notebook. The editing environment itself has a learning curve for non-technical users, since the reactive notebook assumes some comfort with data concepts. A practical split: technical users build the database connections, business users read dashboards and ask the AI questions, and the free tier covers the trial for both roles.

Hex connects directly to databases and warehouses without a separate extraction layer, so queries run against live data rather than stale copies. Connections are configured per workspace, and the reactive notebook refreshes results when the underlying data changes. Check the current connection list for supported engines before committing a team workflow, since availability varies by plan.

It covers the middle ground between notebooks and BI. You get interactive reports and dashboards that non-technical teammates can use, but the core strength is exploratory analysis where questions change constantly. Teams with fixed, repeating KPI reports often keep a dedicated BI tool alongside Hex, while ad hoc analysis and data science work consolidates into it. The free plan is enough to test both patterns before deciding.

Hex suggests charts and visualizations based on your query results, so you do not start from a blank canvas. You pick from suggested chart types, adjust the data mapping, and publish. The suggestions respect the shape of the result set, which matters when a query returns a time series versus categorical data. This removes most of the setup work that usually sits between a finished query and a shareable chart.

No. Published Hex reports and dashboards are interactive for viewers without any SQL, with filters and controls that let them slice data themselves. SQL knowledge matters only for the people authoring notebooks, and even there the natural language to SQL generation lowers the barrier. This split between authors and viewers is how data teams typically roll Hex out to the wider company.

Professional runs from 36 dollars per editor monthly and covers individual practitioners who need the AI notebook and publishing workflow. Team costs 75 dollars per editor monthly and adds the collaboration layer for groups working in shared workspaces. Start on Professional if one analyst owns most of the notebooks, and move to Team when multiple people regularly review and extend each other work.