Deepnote
Collaborative AI-native data notebook for data scientists and analysts with real-time multiplayer editing and AI code assistance.
Deepnote is a cloud-hosted data science notebook that combines Jupyter notebook compatibility with real-time multiplayer collaboration, integrated AI assistance, and a polished analytics workspace. Its AI Code Completion, AI Chat, and Fix with AI features help data scientists write Python, SQL, and R faster, debug errors, and understand complex pipelines. Founded in 2019 by Jakub Jurovych and Milan Plzik, Deepnote has raised $31.5 million in funding and is used by over 350,000 users at companies including Shopify, Toyota, and Harvard. In 2024, Deepnote launched natural language-to-SQL and automatic chart generation from query results. It integrates with Snowflake, BigQuery, PostgreSQL, and major cloud storage providers, with a free plan supporting up to 5 projects.
Key Features
- Real-time multiplayer collaboration - multiple users edit and run the same notebook simultaneously
- AI code completion and chat for Python, SQL, and R within the notebook environment
- Natural language to SQL for querying connected databases without writing SQL manually
- Fix with AI - highlights error cells and suggests one-click fixes for runtime exceptions
- Native integrations with Snowflake, BigQuery, Redshift, PostgreSQL, and S3
- Scheduled notebook runs for automating recurring data pipelines and reports
- Jupyter-compatible - import and export standard .ipynb files without reformatting
Use Cases
- Data teams sharing exploratory analysis with non-technical stakeholders using clean notebook presentations
- Analysts querying Snowflake or BigQuery using natural language without needing SQL expertise
- ML engineers collaborating on feature engineering notebooks without version conflict issues
- Students learning data science using AI code suggestions without leaving the notebook environment
Pros
- Real-time collaboration is genuinely seamless - the Google Docs experience applied to data notebooks
- AI-assisted SQL from natural language is practical and accurate on standard analytical queries
- Jupyter compatibility means zero migration friction - import existing notebooks in one click
Cons
- Performance can be sluggish on the free tier with large datasets due to limited compute allocation
- AI features are less capable than standalone coding assistants like GitHub Copilot or Cursor
- Not suitable for production model serving or real-time pipelines - focused on exploratory analysis
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Julius AI
AI data analyst that reads your spreadsheets, databases, and files - answering questions, building charts, and running analyses in plain English.
Hex
AI analytics platform combining collaborative SQL/Python notebooks, data apps, and natural-language queries for data teams; $19.8M ARR.
Weights & Biases
ML experiment tracking, model monitoring, and dataset versioning platform - used by OpenAI, Toyota, and 1,000+ organizations to ship better models faster.
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