Best Jupyter alternatives compared

Jupyter Notebook changed how data scientists work by making code, visualizations, and narrative text work together in one interactive environment. It's been the standard for exploratory analysis, model prototyping, and research for over a decade, and for good reason — the cell-based interface makes it easy to experiment, iterate, and see results immediately.

But as data work becomes more collaborative and workflows extend beyond individual exploration, teams can start to find they need capabilities Jupyter wasn't originally designed for.

This guide examines modern notebook platforms that build on Jupyter's foundation while adding collaboration, governance, and publishing features that team-based data work requires.

When teams need collaboration and publishing

Jupyter was designed for individual data scientists working locally. As team workflows evolved, certain patterns started creating friction:

Collaboration requires workarounds

Jupyter stores notebooks as JSON files that bundle code, outputs, and metadata together. When you change a cell, execution counts, timestamps, and output data all change too. Git treats everything as content, so collaborative work often produces merge conflicts that require manual metadata editing rather than focusing on actual code changes. Teams work around this by emailing notebooks, using screen shares, or converting to static formats.

Execution state lives only in memory

When you run cells out of order during exploration (which is part of what makes notebooks powerful), the execution sequence exists only in kernel memory, not in the saved file. Someone else running the same notebook top-to-bottom might get different results or errors. Research on notebook reproducibility found that of 2,684 Jupyter notebooks tested, only 245 reproduced their original results when re-run.

Sharing requires rebuilding

Jupyter focuses on the analysis itself. When you need to share results with stakeholders who want interactive exploration, you typically export static HTML, rebuild visualizations in BI tools, or schedule recurring meetings. The analysis artifact and the stakeholder deliverable become separate things that drift out of sync.

These aren't design flaws — Jupyter wasn't built for these use cases. Modern platforms address them while preserving what makes notebooks valuable for exploratory work.

Top Jupyter alternatives

Different platforms solve different problems. Here's how the leading Jupyter alternatives compare:

Best overall: Hex

Hex provides the most advanced collaborative notebook environment available, combining real-time multiplayer editing with AI-assisted development powered by Claude Sonnet models. It's an AI-powered platform where data teams and business users work side-by-side.

Hex's notebook addresses all three areas where local Jupyter creates friction. Real-time collaboration eliminates file-based version conflicts, semantic layer integration ensures everyone queries consistent metric definitions, and one-click publishing turns exploratory notebooks into stakeholder-ready apps without rebuilding.

Key capabilities

• Real-time collaboration. Multiple people work in the same notebook simultaneously with Google Docs-style editing, commenting, and automatic conflict resolution. No more JSON merge conflicts or emailed notebook files.

• AI-powered analysis. The notebook agent understands your warehouse schema and semantic models, helping you build complete analyses through conversation. All generated code is inspectable and editable.

• Unified environment. SQL and Python work together out of the box, no configuration needed. SQL-only analysts can use notebooks without Python expertise, unlike Jupyter (Python required) or Databricks/Colab (SQL requires setup). Query results flow directly between SQL and Python cells.

• Semantic layer integration. Sync with dbt MetricFlow so everyone queries the same metric definitions. The AI understands these definitions, so the generated code uses your governed metrics automatically.

• Interactive publishing. Turn your exploratory notebook into an interactive app with dropdowns and filters in one click. Stakeholders explore results themselves without waiting for new analysis.

• Production-ready infrastructure. Scheduled execution, parameterization, API endpoints, and embedding capabilities mean the same notebook handles both exploration and production delivery.

Who it's for

Hex works well for data teams juggling multiple tools for their workflow. If you're currently using Jupyter for exploration, a separate BI tool for stakeholder dashboards, and Google Docs for documentation, Hex consolidates these into one environment. It handles the full workflow from exploration to stakeholder delivery, with real-time collaboration that eliminates merge conflicts and granular permissions that let business users interact with results without accessing code.

Hex is also particularly valuable for teams with SQL-fluent analysts who've been locked out of notebook-based workflows. In traditional environments, these analysts either can't use Jupyter (Python barrier) or face configuration hurdles in Databricks or Colab. Hex makes notebooks accessible to anyone who knows SQL, expanding the notebook interface beyond just Python experts.

Best for free compute: Google Colab

Google Colab is a free, cloud-based Jupyter Notebook environment with zero-setup access to GPUs and TPUs and Google Docs-style real-time collaboration.

As a Jupyter alternative, Colab solves Jupyter’s infrastructure and collaboration barriers: instant access to specialized hardware without local setup, and multiple people can edit the same notebook simultaneously, with changes syncing automatically.

Key capabilities

• Google Docs-style collaboration. Multiple people edit simultaneously, with comments and automatic saves to Google Drive.

• Instant GPU/TPU access. Click a dropdown to run on specialized hardware — no infrastructure setup required.

• Google ecosystem integration. Mount Drive for datasets, authenticate with Google Cloud, and export to Sheets or Docs seamlessly.

Note that while Colab excels at Python execution with free compute, SQL requires additional setup and configuration, which limits notebook adoption to primarily Python-fluent users.

Who it's for

Colab is most suitable for students, researchers, and data scientists who need free access to GPUs and TPUs for learning and prototyping. The free tier has session limits and timeouts that make it unsuitable for production workloads, but for exploration and model validation, it provides hardware that would cost thousands to access locally.

Best for reactive execution: marimo

marimo is an open-source reactive notebook for Python that stores notebooks as pure .py files and automatically re-runs dependent cells when code changes.

marimo turns to its architecture to solve Jupyter’s hidden state and version control problems. Reactive execution eliminates out-of-order execution bugs, and pure Python storage means Git diffs show actual code changes instead of JSON metadata conflicts.

Key capabilities

• Pure Python storage. Notebooks are .py files, so Git diffs show actual code changes and merge conflicts resolve like normal Python.

• Reactive execution. Change one cell and marimo automatically re-runs everything downstream — no manual re-running or execution order confusion.

• Enforced consistency. Variables must be defined in one place, cyclic references aren't allowed, and correct usage is the default.

Who it's for

marimo is a good option for teams that absolutely need reproducibility, like scientific research, regulated industries, or anywhere "it worked on my machine" isn't acceptable. The trade-off is a less mature ecosystem and a learning curve for the reactive model.

Best for enterprise ML: Databricks

Databricks Notebooks are collaborative notebooks integrated into the Databricks Lakehouse Platform, with native Spark execution, MLflow experiment tracking, and unified data and AI workflows.

As a Jupyter alternative for enterprise ML teams, Databricks eliminates the friction of moving between notebook and production: query Delta tables directly, train models on distributed compute, and deploy through MLflow without leaving the platform. 

Key capabilities

• End-to-end ML workflow. Develop in notebooks, log experiments to MLflow automatically, and promote models to production from the same interface.

• Scalable compute. Start on interactive clusters, then schedule the same notebook as production jobs on larger infrastructure without rewriting code.

• Native lakehouse integration. Query terabyte-scale Delta tables interactively and deploy through governed workflows with lineage tracking.

Who it's for

Databricks is a no-brainer for enterprise ML teams that have already invested in the Databricks Lakehouse. The integration depth with Delta tables, Spark, and MLflow outweighs standalone notebook alternatives when your data and pipelines already live in the platform.

Move beyond Jupyter with Hex

Jupyter established notebooks as the standard interface for data science work. The interactive cell-based approach and ability to mix code with narrative remain fundamental to how data scientists work.

Hex builds on that foundation with capabilities designed for team collaboration. SQL and Python work together out of the box — no configuration needed — which makes notebooks accessible to SQL-fluent analysts who've been locked out of traditional notebook environments.

Real-time multiplayer editing eliminates JSON merge conflicts by letting you work together in the same document with synchronization handled automatically. Semantic layer integration means everyone queries the same metric definitions. And one-click publishing turns your exploratory notebook into a stakeholder-ready app without rebuilding, using the same artifact with a different interface.

The notebook agent, powered by the latest Claude Sonnet models, helps data scientists build complete analyses through conversation while keeping all code inspectable and editable. Because the AI understands your semantic models and endorsed tables, it generates code using your governed definitions automatically.

The time you spend managing collaboration and rebuilding for stakeholders could go toward the analysis itself. Get started with Hex for free, or schedule a demo to see how it works with your specific data stack.