Deepnote

Collaborative cloud notebook for Python, SQL and R analysis

AI Data AnalysisFree planOverseas★★★☆☆ 3.0

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What is Deepnote?

Deepnote is a browser-based notebook for data teams. Notebooks run in cloud environments that can be shared like documents, so a colleague opens the same notebook with the same dependencies instead of rebuilding a local setup from a README. Python, R and SQL run side by side, connectors reach warehouses such as Snowflake and BigQuery, and scheduled runs keep an analysis current without anyone pressing play. Finished notebooks can be published as small apps for readers who do not write code. An AI assistant drafts cells and explains tracebacks, but the product is a collaborative notebook at heart rather than an AI generator.

Last updated: 2026-09-20. This site only provides an index; for exact features, pricing, and licensing, see the official website.

Key features

Cloud notebooks with Python, R and SQL in one document
Real-time multiplayer editing, comments and change history
Managed environments with dependency and integration blocks
Scheduled notebook runs and notebooks published as data apps
Warehouse connectors for Snowflake, BigQuery, Redshift and Postgres
AI assistant for drafting code and explaining error messages

Pros & cons

Strengths

Removes local environment setup for collaborative analysis
Connectors and scheduling make notebooks useful in production
Publishing a notebook as an app helps non-coders read results

Watch out for

Compute and integrations are less flexible than a self-hosted stack
Heavy jobs cost more than running the same code on your own machine
Very large data work still belongs in a dedicated pipeline tool

Best for & use cases

collaborative data analysis, notebook sharing, sql exploration, scheduled reporting

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FAQ

How is Deepnote different from Jupyter?

Jupyter runs on your own machine and sharing means exporting a file plus its environment. Deepnote hosts the notebook, manages dependencies, and lets several people edit the same document at once, so the analysis stays reproducible without setup notes.

Does it connect to a data warehouse?

Yes. Built-in connectors cover warehouses such as Snowflake, BigQuery, Redshift and Postgres, and SQL results can be pulled straight into dataframes and charts inside the same notebook.

Is the AI assistant the main feature?

No. It is a convenience that drafts cells or explains a traceback. The centre of the product is the collaborative notebook, managed compute and scheduling, so treating the assistant as the headline would overstate it.