TL;DR - Deepnote
- AI-powered collaborative data science notebook supporting Python, SQL, and R.
- Transforms data explorations into interactive data apps and dashboards.
- Offers robust collaboration features, powerful compute options, and enterprise-grade security.
Pricing: Free plan available
Best for: Growing teams
4.5/5 across review platforms
Pros & Cons
Pros
- AI assistance significantly speeds up data analysis and coding tasks.
- Seamless collaboration features make teamwork efficient.
- Supports a wide range of data sources and languages.
- Ability to create interactive data apps and dashboards from notebooks.
- Strong security and compliance features suitable for enterprises.
Cons
- Limited Deepnote AI features and compute resources in the free tier.
- Advanced security and deployment options are only available in higher-tier plans.
- Reliance on cloud infrastructure may not suit all data governance requirements without custom deployments.
Ratings Across the Web
4.5(378 reviews)
Ratings aggregated from independent review platforms. Learn more
Preview
Key Features
AI-powered code generation, explanation, refactoring, and debuggingSupport for Python, SQL, and R in a single IDEInteractive visualizations and no-code configurable chartsCreation and deployment of data apps and dashboardsScheduled notebooks and API deployment for modelsReal-time collaboration, commenting, and version historyIntegration with 60+ data sources (Snowflake, BigQuery, dbt, etc.)Access to powerful GPUs and custom machine configurations
Pricing Plans
Free TrialFree
Free
- Up to 3 editors
- Up to 5 projects
- Limited Deepnote AI
- Unlimited Basic machines with 5 GB RAM, 2 vCPU
- 7 day revision history
Team
$39 per editor/month billed yearly
- Unlimited viewers & notebooks
- Unlimited Deepnote AI
- GPT-5 and Sonnet 4.5 access
- Premium integrations
- Background execution
- Scheduled notebooks
- 30 day revision history
- $280 worth of CPU every month
- $50 worth of GPU every month
- More powerful machines
- Discounted additional compute
Enterprise
Custom
- Custom contract & invoice
- Priority support
- Dedicated success manager
- Permission groups
- SSO & directory sync
- Private docker images
- Bring your own LLM
- Audit logs
- Federated Authentication
- Single-tenancy
- Volume machine discounts
What is Deepnote?
Deepnote is an AI-powered, collaborative data science notebook environment designed for data analysts and data scientists. It allows users to write, execute, and share code in Python, SQL, and R, and integrates with over 60 data sources including Snowflake and BigQuery. The platform offers an AI assistant that can generate, refactor, explain, and debug code, making data analysis more accessible and efficient for users of all skill levels.
Beyond traditional notebooks, Deepnote supports the creation of interactive data apps and dashboards, enabling users to turn quick explorations into impressive reports. It facilitates team collaboration with real-time editing, commenting, versioning, and easy sharing. Deepnote also provides powerful machine options, including GPUs, and robust security features like SOC 2 Type II compliance, HIPAA compliance, SSO, and custom deployments, ensuring data integrity and access control for organizations.
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Deepnote FAQ
What is Deepnote?
Deepnote is a collaborative, AI-powered data science notebook environment that allows data analysts and scientists to write, execute, and share code in Python, SQL, and R. It helps users analyze data, create interactive applications, and build dashboards with AI assistance and robust collaboration features.
How much does Deepnote cost?
Deepnote offers a Free plan with limited features. The Team plan costs $39 per editor/month (billed yearly). An Enterprise plan is available with custom pricing for organizations needing advanced security and machine requirements.
Is Deepnote free?
Yes, Deepnote offers a Free plan that includes up to 3 editors, 5 projects, limited Deepnote AI, unlimited basic machines, and a 7-day revision history.
Who is Deepnote for?
Deepnote is designed for data analysts and data scientists, as well as product managers and other team members who need to collaborate on data projects, perform analysis, build data applications, and share insights. It caters to individuals and teams ranging from aspiring data professionals to large enterprises.
Source: deepnote.com