Best Jupyter Alternatives and Competitors in 2025

Jupyter Notebooks have become the standard interface for interactive Python coding, data science, and machine learning. They’re widely used by researchers, engineers, and educators to explore data, visualize results, and share reproducible code. Jupyter’s flexibility, notebook-style format, and wide kernel support have made it the go-to tool for exploratory programming in Python and R.

However, in 2025, many teams are looking for Jupyter alternatives with stronger collaboration, built-in version control, real-time teamwork, or better cloud integration. Some want full IDE features, others need scalable infrastructure, and many want to work seamlessly across R, SQL, and Python. Whether you’re a data scientist, analyst, or ML engineer — modern notebook tools offer smarter, faster, and more secure ways to work.

This article explores the top Jupyter alternatives for notebooks, dashboards, collaboration, and production-grade data workflows in 2025.

What is Jupyter?

Jupyter is an open-source project that provides a web-based interface for running interactive code in multiple languages. It’s most commonly used for Python (via IPython), but also supports R, Julia, and more. Jupyter lets users mix code, markdown, visualizations, and outputs in a single document, which is especially helpful for exploratory data analysis and sharing reproducible research. But Jupyter’s traditional UI lacks some modern features like real-time collaboration, cloud storage, and better development workflows — prompting many teams to explore alternatives.

Why Look for Jupyter Alternatives?

1. No Real-Time Collaboration: Standard Jupyter doesn’t support live editing or commenting like Google Docs or Deepnote.

2. Version Control Is Manual: Jupyter Notebooks aren’t Git-friendly by default and often require external tools to manage changes.

3. Limited Deployment Options: Out-of-the-box, Jupyter lacks easy integration with cloud-scale compute or multi-user management.

4. Not a Full IDE: It’s great for notebooks, but lacks strong debugging, testing, and editor features found in VS Code or PyCharm.

5. Better Cloud + Team Features Exist: Modern tools support collaborative notebooks, scheduling, permissions, and integrations with dbt, Git, and CI/CD pipelines.

Top Jupyter Alternatives (Comparison Table)

#	Tool	Open Source	Best For	Deployment
#1	Deepnote	No	Collaborative cloud notebooks	Cloud
#2	Google Colab	Yes	Python notebooks with GPUs	Cloud
#3	Saturn Cloud	No	Scalable notebooks + compute	Cloud
#4	VS Code + Jupyter Extension	Yes	IDE with notebook support	Desktop / Cloud
#5	Kaggle Notebooks	Yes	Quick experiments and public datasets	Cloud
#6	Databricks Notebooks	No	Unified notebooks + Spark	Cloud
#7	Polynote	Yes	Multi-language notebooks	Self-hosted
#8	Hex	No	Notebooks with SQL + app sharing	Cloud
#9	Posit Workbench	No	Enterprise R + Python notebooks	Cloud / Server
#10	Observable	No	JavaScript-based reactive notebooks	Cloud

Top 10 Alternatives to Jupyter

#1. Deepnote

Deepnote is a modern notebook platform built for collaboration. It supports Python and SQL and enables real-time teamwork, comments, and versioning — ideal for data teams replacing Jupyter in the cloud.

Features:

• Live editing and collaboration
• Supports Python, SQL, and R
• Version control and Git integration
• Notebook scheduling and sharing
• One-click cloud setup

#2. Google Colab

Google Colab is a free, hosted Jupyter notebook environment with access to GPUs. It’s a great Jupyter alternative for experimentation, teaching, or ML model prototyping.

Features:

• Free access to CPUs, GPUs, TPUs
• Python and limited R support
• Integration with Google Drive
• Markdown and code in one place
• Great for quick collaboration

#3. Saturn Cloud

Saturn Cloud offers scalable Jupyter notebooks with powerful compute (CPUs/GPUs). It’s built for data scientists running heavy workflows or cloud ML models.

Features:

• Jupyter + R notebooks
• Scalable hardware + scheduling
• Shared environments and dashboards
• MLflow, Dask, Prefect support
• Enterprise-ready features

#4. VS Code + Jupyter Extension

VS Code with the Jupyter extension allows you to run notebooks inside a full-featured code editor. It replaces Jupyter for developers who want linting, debugging, and Git workflows.

Features:

• Notebooks inside VS Code UI
• Run cells, visualize output
• Debugging, auto-complete, testing
• Python, R, and Julia support
• Works offline or in GitHub Codespaces

#5. Kaggle Notebooks

Kaggle offers hosted Python and R notebooks with free GPU support. It’s ideal for experimenting with public datasets or participating in competitions.

Features:

• Free hosted compute
• Import and export datasets easily
• Notebook sharing and versioning
• Supports R and Python
• Great for data science learners

#6. Databricks Notebooks

Databricks combines notebooks, data engineering, and ML pipelines in one cloud platform. It’s ideal for teams replacing Jupyter in Spark, Delta Lake, or ML workflows.

Features:

• Supports Python, SQL, R, and Scala
• Integrated with Delta Lake and MLflow
• Collaborative notebook editing
• Built for large-scale Spark jobs
• Used across enterprise data platforms

#7. Polynote

Polynote is an open-source, multi-language notebook tool developed by Netflix. It supports running Scala, Python, and SQL in a single notebook with a shared context.

Features:

• Polyglot execution (Scala, Python, SQL)
• Type-safe notebook interface
• Built-in plotting and Markdown
• Open-source and customizable
• Better for Spark and JVM workflows

#8. Hex

Hex is a cloud-based notebook tool with drag-and-drop blocks, SQL support, and app-like dashboard sharing. It replaces Jupyter for analysts who want no-code + SQL + Python flexibility.

Features:

• Notebook + app builder UI
• Python, SQL, Markdown blocks
• Built-in version control and sharing
• Live data outputs and input forms
• Great for internal analytics

#9. Posit Workbench

Posit Workbench (formerly RStudio Server Pro) is an enterprise-grade notebook platform that supports R, Python, and Jupyter in one interface — ideal for regulated or hybrid teams.

Features:

• Supports Jupyter, RStudio, VS Code
• User/session management and scaling
• Audit logging and access control
• Shiny, Quarto, and Markdown support
• Cloud or on-prem deployment

#10. Observable

Observable is a reactive notebook platform focused on data visualizations in JavaScript. It’s ideal for replacing Jupyter in frontend data storytelling or browser-based dashboards.

Features:

• Live notebook updates (reactive model)
• D3.js, Vega, and Plotly support
• Great for JS developers
• Interactive widgets and charts
• Embed anywhere on the web

Conclusion

Jupyter Notebooks are still central to data science workflows — but in 2025, many tools now offer better collaboration, multi-language support, and cloud scalability. Whether you’re a solo developer, part of an enterprise team, or a classroom instructor, there’s a Jupyter alternative that improves how you work with notebooks.

Choose Deepnote or Hex for real-time collaboration. Use Colab or Saturn Cloud for scalable cloud compute. Try VS Code, Polynote, or Databricks for engineering flexibility. The right tool depends on your data size, tech stack, and how you want to share and scale your analysis.

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