Deepnote is an open-source project positioned as a modern successor to Jupyter, designed for the era of artificial intelligence. Used by more than 500,000 data professionals at companies such as Estée Lauder, SoundCloud, and Statsig, Deepnote combines Jupyter compatibility with AI-native functionality and an advanced collaborative experience.
Format Innovation
The .deepnote format replaces the verbose JSON of .ipynb with a human-readable YAML structure optimized for version control. This format organizes multiple notebooks, integrations, and settings within a single .deepnote project, facilitating structure and collaboration. The @deepnote/convert CLI tool enables seamless bidirectional conversion between Jupyter and Deepnote formats.
Extensible Architecture
Deepnote introduces a block-based architecture beyond traditional code cells. Through the @deepnote/blocks package, users access blocks for SQL queries, interactive inputs, visualizations, buttons, big numbers, images, and separators. These blocks are defined and validated in TypeScript, providing type safety and extensibility. Reactive notebook execution ensures dependent blocks automatically re-run when inputs or data change, maintaining consistency and reproducibility.
Multi-IDE Ecosystem
The open-source project provides official extensions for modern AI-native editors: VS Code, Cursor, Windsurf, and JupyterLab. This "work wherever" strategy allows data scientists to develop locally in their preferred IDE, then scale to Deepnote Cloud for real-time collaboration with robust cloud compute.
Hybrid Cloud-Local Strategy
Deepnote Open Source enables complete local development, while Deepnote Cloud offers managed compute, a native AI agent, link-based sharing, native database/API integrations, and synchronous collaboration. This hybrid approach addresses the needs of individual data scientists (local, free, full control) and teams (collaboration, scalable compute, governance).
Roadmap and Vision
The roadmap includes the local Deepnote Cloud UI, a local AI agent, bring-your-own-keys support for AI services, and run-your-own-compute capability. These developments aim to offer the full Deepnote Cloud experience locally for users requiring data sovereignty or working with sensitive data.
Positioning vs. Jupyter
The comparison table highlights zero setup (cloud or local vs. local installation), native AI features (agent and code completion vs. third-party extensions), integrated version control (native Git vs. manual workflow), simplified sharing (link vs. manual export), managed compute (cloud vs. local resources only), and native integrations (databases/APIs vs. manual configuration).
Academic Community
Deepnote Cloud is free for students and teachers, with unlimited access to core features, cloud compute, and real-time collaboration. The project encourages academic citations and contributes to the open-source data science ecosystem.
Jupyter Legacy
The acknowledgements pay tribute to the Jupyter community and its impact since 2013, positioning Deepnote as a natural extension of this legacy toward an AI-native, collaborative future, while actively contributing to the same open ecosystem.