Jupyter Notebook has long been the go-to platform for data scientists, analysts, and machine learning engineers. Its interactive interface, seamless integration with Python, and support for code, visualizations, and markdown in one place make it a versatile tool. But as the data science ecosystem grows, so does the demand for more powerful, collaborative, or specialized tools. This raises an important question: What are the best Jupyter Notebook alternatives in 2025?
In this comprehensive guide, we’ll explore the top Jupyter Notebook alternatives, highlighting their features, pros and cons, and best use cases. Whether you’re looking for better performance, version control, or team collaboration, there’s an option tailored to your needs.
Why Look for Jupyter Notebook Alternatives?
While Jupyter Notebook remains popular, it comes with limitations:
- Lack of built-in version control
- Difficult to manage large-scale projects
- Limited collaboration features for teams
- Challenges in production-level deployment
For these reasons, many developers and data teams seek more advanced environments that go beyond the traditional notebook interface.
Summary Comparison Table
| Tool | Collaboration | Cloud-Based | Language Support | Best For | Notable Features |
|---|---|---|---|---|---|
| JupyterLab | Limited | No | Python, R, Julia | Advanced Jupyter experience | Modular interface, extension support |
| Google Colab | Yes | Yes | Python | Beginners and quick prototyping | Free GPU/TPU, Google Drive integration |
| Deepnote | Real-time | Yes | Python, SQL | Teams and collaboration | Git support, database integration |
| VS Code + Jupyter | Yes (Git) | Optional | Multi-language | Developers, script integration | Full IDE features, debugging, extensions |
| Zeppelin | Yes | Yes | Python, Scala, SQL | Big data and enterprise users | Spark/Hadoop integration, visualization |
| Polynote | Limited | No | Python, Scala, SQL | Research and reproducibility | Multi-language, reproducible outputs |
| Kaggle Kernels | Limited | Yes | Python, R | Competitions and learning | Public datasets, free compute |
| Databricks Notebooks | Yes | Yes | Python, SQL, Scala | Enterprise ML & data engineering | MLflow, Spark, Delta Lake |
| Hex | Real-time | Yes | Python, SQL | Analysts and reporting | Dashboard integration, sharing |
JupyterLab
JupyterLab is the official successor to Jupyter Notebook. It retains all the functionality of Jupyter Notebook but adds a more robust user interface and extensibility.
Features:
- Multi-document interface (tabs, drag-and-drop)
- Integrated terminals, text editors, and file browsers
- Extension manager for customization
- Works with existing Jupyter Notebooks
Pros:
- Familiar to Jupyter users
- Greater flexibility and productivity
- Active open-source community
Cons:
- Still shares some limitations with classic Jupyter
- Can feel heavy on low-end systems
Best For: Users who want a better version of Jupyter with enhanced UI and modularity.
Google Colab
Google Colab is a cloud-based platform by Google that allows users to run Jupyter-style notebooks without any setup.
Features:
- Free GPU and TPU access
- Easy integration with Google Drive
- Collaboration similar to Google Docs
- Supports most Python libraries out of the box
Pros:
- No installation required
- Ideal for experimentation and teaching
- Free compute resources for prototyping
Cons:
- Limited compute quota
- Lacks advanced IDE features
- Not suitable for enterprise-grade workflows
Best For: Students, hobbyists, and prototypers who want an easy-to-use, cloud-based platform.
Deepnote
Deepnote is a modern collaborative data science notebook designed for teams and real-time collaboration.
Features:
- Live collaboration (like Google Docs for notebooks)
- Git integration and version control
- SQL support with live database connections
- Built-in team management features
Pros:
- Excellent for team workflows
- Enterprise-focused features
- Smooth onboarding and modern UI
Cons:
- Some features behind a paywall
- May not support all open-source extensions
Best For: Teams and organizations that prioritize collaboration and productivity.
VS Code with Jupyter Extension
Visual Studio Code is one of the most popular text editors, and with the Jupyter extension, it becomes a powerful alternative to Jupyter Notebook.
Features:
- Run code cells interactively in Python scripts or notebooks
- Integrated terminal, debugger, and Git support
- Rich extension ecosystem
- Multi-language support
Pros:
- Complete development environment
- Ideal for combining notebooks with scripts and projects
- Strong integration with Git and Docker
Cons:
- Steeper learning curve for beginners
- May require configuration for data science workflows
Best For: Developers who want a full-fledged IDE with notebook capabilities.
Zeppelin
Apache Zeppelin is a web-based notebook platform that supports multiple interpreters including Python, Scala, and SQL.
Features:
- Multi-language support in one notebook
- Built-in visualizations and charts
- Integration with Spark and Hadoop
- Pluggable interpreter architecture
Pros:
- Great for big data and Spark workloads
- Versatile for enterprise environments
- Real-time collaboration support
Cons:
- UI is less refined than some alternatives
- Requires setup and configuration
Best For: Enterprises working with big data technologies or multi-language workflows.
Polynote
Polynote, developed by Netflix, is an open-source notebook with strong multi-language support and reproducibility.
Features:
- Supports multiple languages (Python, Scala, SQL) in the same notebook
- Type safety and auto-completion
- Reproducible execution environment
- Rich output rendering
Pros:
- Built with reproducibility and polyglot workflows in mind
- Designed for reproducible machine learning pipelines
Cons:
- Still maturing; less community support
- Requires setup and technical knowledge
Best For: Power users and researchers focused on reproducible, multi-language workflows.
Kaggle Kernels
Kaggle Kernels is a free, cloud-based environment provided by Kaggle for running Jupyter Notebooks.
Features:
- Free CPU and GPU compute
- Access to public datasets and competitions
- Integrated code sharing and notebooks publishing
Pros:
- Ideal for experimentation and competitions
- Rich community and code base to learn from
Cons:
- Limited to Kaggle’s environment
- Not intended for production deployment
Best For: Data scientists participating in competitions or sharing notebooks publicly.
Databricks Notebooks
Databricks Notebooks are part of the Databricks platform built on Apache Spark, designed for scalable data analytics and machine learning.
Features:
- Built-in Spark integration
- Collaborative notebooks with version history
- Native MLflow and Delta Lake support
- Role-based access control
Pros:
- Highly scalable and enterprise-ready
- Ideal for large data pipelines and ML workflows
Cons:
- Requires Databricks subscription
- Steep learning curve for new users
Best For: Enterprises working on large-scale data engineering and machine learning projects.
Hex
Hex is a modern data platform that blends notebook-style development with dashboards and collaboration features.
Features:
- Notebook + dashboard hybrid interface
- SQL and Python support
- Built-in visualizations and logic blocks
- Real-time collaboration
Pros:
- Tailored for analytics and data storytelling
- Easy to share results across teams
Cons:
- Focused more on analytics than machine learning
- Premium features may require paid plan
Best For: Analysts and teams focused on interactive reporting and collaborative analysis.
How to Choose the Right Jupyter Notebook Alternative
When evaluating an alternative to Jupyter Notebook, consider the following:
- Collaboration Needs: Do you need real-time collaboration or version control?
- Compute Resources: Do you require GPUs or distributed computing?
- Programming Languages: Are you working with Python only or need multi-language support?
- Deployment Requirements: Do you need to move notebooks into production pipelines?
- Ease of Use: Is the tool beginner-friendly or suited for experienced developers?
Choosing the right tool depends on your team size, workflow needs, budget, and long-term goals.
Conclusion
Jupyter Notebook has laid the foundation for interactive data science and machine learning development. But as needs evolve, so does the ecosystem. Whether you’re a solo data scientist or part of a larger team, there are several robust Jupyter Notebook alternatives that offer enhanced features, better collaboration, and scalable infrastructure.
From cloud-based platforms like Google Colab and Kaggle Kernels to enterprise-ready tools like Databricks and Deepnote, each alternative brings something unique to the table. By choosing the right tool for your workflow, you can increase productivity, streamline collaboration, and take your data projects to the next level in 2025 and beyond.