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Deepnote

Freemium

Collaborative cloud notebook for data science with built-in AI copilot for code, SQL, and visualizations.

india friendly pricingapi available

Our take

A collaborative cloud notebook for data science with a built-in AI copilot - a strong free-tier option for technical analysts.

Best for

Data scientists and analysts who want a collaborative, cloud-based Jupyter-style notebook with AI code assistance.

Pros

  • Generous free tier for individuals and small teams
  • AI copilot helps write and explain SQL/Python code
  • Real-time collaboration similar to Google Docs for notebooks

Cons

  • Requires comfort with code/notebooks, unlike Julius AI or Polymer
  • Less suited to non-technical business users needing simple dashboards

How it compares

Similar collaborative notebook concept to Hex, but with a more generous free tier aimed at individuals and smaller teams.

Full review

Deepnote is a collaborative data science notebook platform — similar in spirit to Jupyter notebooks but built for teams, with real-time collaboration, built-in data connectors, and AI features that can generate and explain code, SQL, and visualizations from natural-language prompts.

Its collaborative, cloud-based notebook environment (multiple people editing and running code together, like Google Docs for data analysis) combined with AI code generation is aimed at data teams who want the flexibility of code-based analysis (Python, SQL) with easier collaboration and AI assistance than a traditional local Jupyter setup provides.

Deepnote has a free tier for individuals and small teams, with paid plans for larger teams and more compute. For data science and analytics teams that work in code (Python/SQL) and want a collaborative, AI-assisted notebook environment, Deepnote is comparable to Hex — the choice between them often comes down to specific integration needs and team workflow preferences around notebooks versus app-building.

Cloudkart Rubric

3.6/5 avg
  • Actual Utility
    4/5
  • Ease of Use
    4/5
  • Pricing Fairness
    4/5
  • Reliability
    3/5
  • Differentiation
    3/5