Deepnote Notebooks
Deepnote is a cloud-based, collaboration-first data platform designed for data teams that need to work together on notebooks in real time, similar to Google Docs.
Publisher review
Deepnote is a cloud-based, collaboration-first data platform designed for data teams that need to work together on notebooks in real time, similar to Google Docs. It targets data analysts, scientists, and engineers who want a managed environment that reduces setup overhead and emphasizes teamwork. The platform supports Python and R, is fully compatible with Jupyter notebooks (.ipynb import/export), and offers a powerful web interface with built-in data connectors for major cloud platforms like Google BigQuery, Snowflake, and AWS. Deepnote is ideal for organizations that value real-time multi-user editing, commenting, and shared datasets, but it does not support self-hosting—users must use the managed cloud offering.
Deepnote provides a rich set of capabilities that go beyond standard notebooks. It includes AI code completion powered by Codeium, which offers up to 10 completions per month on the free plan and unlimited on paid tiers. Users can create interactive apps and dashboards directly from notebooks, schedule runs (available on Team and Enterprise plans), and access a built-in schema explorer, terminal, and variable explorer. The platform supports fine-grained permissioning, revision history (7 days on free, 30 days on Team, unlimited on Enterprise), and version control integration with git. Each notebook can be shared as a public project, and the Notebook API allows programmatic access. Deepnote also offers white-labeled notebooks for enterprise clients.
In the data notebook market, Deepnote competes directly with Hex and Databricks Notebooks. Compared to Hex, Deepnote offers AI code completion via Codeium and a more generous free tier (up to 3 editors and 5 projects). Databricks Notebooks provides self-hosting options and deeper integration with the Databricks ecosystem, but Deepnote focuses on ease of use and real-time collaboration without requiring a separate compute platform. Deepnote's pricing starts at $39 per editor per month (billed yearly, with a 20% discount) for the Team plan, while Enterprise custom pricing includes SSO, audit logs, and single-tenancy. The free tier is limited to 3 editors, 5 projects, and basic machines (5 GB RAM, 2 vCPU).
The honest trade-offs with Deepnote are clear: it is a fully managed service with no self-hosting option, which may concern organizations with strict data residency or compliance requirements. Some advanced features—such as scheduled runs, folders, and the Notebook API—are locked behind the Team or Enterprise plans. The free tier's AI capabilities are heavily throttled (10 completions per month), and compute resources are capped at basic machines unless you pay for more. Additionally, while Deepnote supports Python and R, it does not natively support SQL-only workflows as seamlessly as dedicated SQL editors, though SQL blocks are available. For teams that need maximum control or offline access, Deepnote's cloud-only model is a limitation.
How it works
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Real-time collaborative editing
Multiple users can edit the same notebook simultaneously with changes reflected instantly, similar to Google Docs.
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AI code completion via Codeium
Powered by Codeium, offers up to 10 completions per month on free plan, unlimited on paid plans.
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Jupyter compatibility
Supports .ipynb import/export, Python and R, and standard Jupyter libraries for data visualization.
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Built-in data connectors
Connects to major cloud platforms like Google BigQuery, Snowflake, and AWS via file-based integrations.
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Interactive apps and dashboards
Create shareable, interactive data apps and dashboards directly from notebooks without additional tools.
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Scheduled runs
Automate notebook execution on a schedule, available on Team and Enterprise plans.
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Fine-grained permissioning
Control access at the project and notebook level, with permission groups on Enterprise plan.
Strengths and trade-offs
Strengths
- Real-time collaboration with multi-user editing and commenting, enabling seamless teamwork on data projects.
- Advanced AI code completion powered by Codeium, providing up to 10 completions per month on free plan.
- Full Jupyter compatibility with support for Python and R, plus .ipynb import/export for easy migration.
- Rich set of integrations including built-in connectors for Google BigQuery, Snowflake, and AWS.
Trade-offs
- No self-hosting option—users must use the managed cloud offering, limiting control over infrastructure.
- Free tier is restricted to 3 editors and 5 projects, with heavily throttled AI capabilities (10 completions/month).
- Some essential features like scheduled runs, folders, and Notebook API require paid Team or Enterprise plans.
- Compute resources on free tier are capped at basic machines (5 GB RAM, 2 vCPU), with more powerful machines only on paid plans.
Pricing context
Free (up to 3 editors, 5 projects, basic machines), Team at $39 per editor/month billed yearly (with 20% discount, 14-day trial), Enterprise custom pricing (includes SSO, audit logs, single-tenancy).
Getting started with Deepnote Notebooks
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Sign up for Deepnote
Go to deepnote.com and click "Get started for free". Create an account using your email or a Google/GitHub login. The free plan supports up to 3 editors and 5 projects, which is sufficient for initial exploration.
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Connect a data source
In a new project, click "Add data" and select your cloud platform (e.g., BigQuery, Snowflake, or AWS). Follow the prompts to authenticate and configure the connection. This allows you to query data directly from your notebook without manual file uploads.
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Create and run a notebook
Click "New notebook" and choose Python or R as the kernel. Write a simple query or data analysis cell, then press Shift+Enter to execute. The results display inline, and you can add markdown cells for documentation.
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Collaborate in real time
Share the project with teammates by clicking "Share" and entering their email addresses. Multiple editors can work simultaneously, with changes visible instantly. Use comments to discuss specific cells or results.
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Schedule a notebook run
Upgrade to a Team or Enterprise plan to enable scheduling. Open the notebook, click the clock icon, and set a cron expression or frequency. This automates execution, with results saved and notifications sent to collaborators.
Frequently Asked Questions
What is Deepnote and how does it work for data teams?
Deepnote is a cloud-based data platform that enables real-time collaborative editing on notebooks, similar to Google Docs. It supports Python and R, integrates with major cloud services, and is fully compatible with Jupyter notebooks for seamless teamwork.
What are the main features of Deepnote Notebooks?
Deepnote offers real-time multi-user editing, AI code completion via Codeium, built-in data connectors for BigQuery and Snowflake, interactive dashboards, scheduled runs, fine-grained permissions, and revision history. It also supports Jupyter .ipynb import/export and git integration.
How does Deepnote's free tier compare to its paid plans?
The free tier allows up to 3 editors and 5 projects with basic machines (5 GB RAM, 2 vCPU) and only 10 AI completions per month. Paid Team plans at $39/editor/month unlock unlimited AI, scheduled runs, and more compute resources.
Is Deepnote better than Hex or Databricks Notebooks?
Deepnote offers a more generous free tier and AI code completion via Codeium compared to Hex. Unlike Databricks Notebooks, Deepnote focuses on ease of use and real-time collaboration without self-hosting, but lacks deep Databricks ecosystem integration.
What are the limitations of using Deepnote for data projects?
Deepnote is fully managed with no self-hosting option, which may raise data residency concerns. The free tier has restricted editors, projects, and compute. Advanced features like scheduled runs and Notebook API require paid plans.
Can I use Deepnote with SQL or only Python and R?
Deepnote primarily supports Python and R, but includes SQL blocks for querying databases. However, it does not offer a dedicated SQL-only workflow like specialized SQL editors, so teams needing extensive SQL may find it less seamless.
Alternatives
How Deepnote Notebooks compares
Direct head-to-head against 3 competitors. Picked by 7wData.
Deepnote Notebooks
- Pricing
- Free (up to 3 editors, 5 projects, basic machines), Team at $39 per editor/month billed yearly (with 20% discount, 14-day trial), Enterprise custom pricing (includes SSO, audit logs, single-tenancy).
- Target
- Deepnote is a cloud-based, collaboration-first data platform designed for data teams that need to work together on notebooks in real time, similar to Google Docs.
- Strength
- Real-time collaboration with multi-user editing and commenting, enabling seamless teamwork on data projects.
- Watch for
- No self-hosting option—users must use the managed cloud offering, limiting control over infrastructure.
Hex
- Pricing
- $36/editor/month (monthly); $30/editor/month (annual)
- Target
- Data teams needing SQL + Python notebooks with no-code visualization blocks
- Deployment
- Managed cloud
- Strength
- Combines notebooks with no-code visualization blocks for mixed-skill teams
- Watch for
- Per-editor pricing escalates quickly; fewer native integrations than Deepnote
Jupyter
- Pricing
- Free (open source); JupyterHub requires self-managed infrastructure
- Target
- Individual analysts and teams wanting full local control and offline work
- Deployment
- Self-hosted or cloud
- Strength
- Mature open-source ecosystem with full offline capability and no vendor lock-in
- Watch for
- No built-in collaboration; requires manual setup for sharing and permissions
Databricks
- Pricing
- Pay-as-you-go (DBUs); custom enterprise contracts
- Target
- Enterprises needing massive distributed computing and data engineering workflows
- Deployment
- Managed cloud
- Strength
- Built for massive distributed computing with strong data engineering tools
- Watch for
- Complex pricing based on DBUs; can become expensive at scale
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