December 9th, 2025
16 Best Jupyter Notebook Alternatives for Data Teams in 2025
By Simon Avila · 31 min read
I worked through a wide range of notebook platforms to see which ones fix hidden state, unreliable runs, and messy collaboration. These 16 Jupyter Notebook alternatives proved the most dependable in 2025.
16 Best Jupyter Notebook alternatives: At a glance
Jupyter Notebook alternatives vary in how they handle collaboration, debugging, and reproducibility, so the right choice depends on what your team needs most. I compared pricing, ideal workflows, and standout strengths so you can quickly see how each option fits real project work. Here’s a clear side-by-side view of the top picks:
Alternative | Best For | Starting Price (billed annually) | Key Advantage |
|---|---|---|---|
Fast visual analysis for data teams | Natural-language queries with clean charts and notebook workflows | ||
GPU training and quick experiments | Easy access to hosted hardware and Google integrations | ||
Real-time teamwork | Strong collaboration with shared notebooks and comments | ||
Large-scale data pipelines | Tight integration with Spark and production jobs | ||
Mixed SQL and Python analysis | $36/editor/month, billed monthly | Smooth switching between notebooks and dashboards | |
Reproducible Python workflows | Free | Reactive execution with clean Python files instead of JSON | |
Clean local notebooks | Free | Simple desktop app for code, outputs, and publishing | |
Multi-language analytics | Free | Polyglot notebooks with Spark and SQL support | |
Python modeling work | Smart coding features inside a notebook-style interface | ||
Scalable Python sessions | Hosted clusters and fast hardware for heavy workloads | ||
Reproducible research | $49/month, billed monthly | Environments saved with each notebook for reliable reruns | |
Reactive data exploration | Free | Multi-language notebooks with automatic dependency tracking | |
Interactive data visuals | Reactive JavaScript notebooks for fast visual exploration | ||
Technical reports | Free | Unified notebooks, documents, and dashboards in one system | |
Fast, hosted experimentation | Free | Free GPUs and an easy browser notebook setup | |
Collaborative math and research | Hosted notebooks with LaTeX and teaching tools |
Why I looked for Jupyter Notebook alternatives
When I used Jupyter Notebook for bigger projects, the same issues kept coming up. The early checks moved fast, but things changed once the work needed clean runs, reviews, and repeated testing. Some cells returned different results depending on earlier steps, and that made debugging take longer than it should.
JupyterLab is the next-generation environment for Jupyter that combines notebooks, terminals, text editors, and file management in one place. It builds on the same Notebook foundations and keeps full .ipynb compatibility, but it adds a multi-panel layout and a richer extension system.
Even with those upgrades, JupyterLab still relies on the same execution model, so out-of-order runs can produce old values, and the JSON structure continues to create noisy diffs that slow down reviews.
Here are the main reasons people start looking for Jupyter Notebook alternatives:
Unreliable runs: Out-of-order execution creates hidden state that leads to results that change without warning. Tracking down the cause takes time.
Weak support for larger projects: The cell-based workflow makes it tough to build clean modules or add tests, which matters once work grows beyond exploration.
Difficult reviews: The notebook file format creates clutter in Git, so code reviews take more effort and slow down teamwork.
1. Julius: Best for fast visual analysis for data teams
We designed Julius to give data teams a cleaner notebook workflow that holds up as projects expand. You can write natural language prompts instead of SQL, and the Notebook records each step so your work keeps the same structure from one run to the next. That helps when you want predictable results without managing cell order or old values in memory.
Notebooks update as your data changes, and each cell shows a clear input and output. You can return to earlier steps, adjust a question, and watch the results refresh without losing context. It supports teams that need a dependable view of how an analysis evolved.
Each Notebook is fully reproducible because every input links to the exact state of your connected data at the moment you ran it. Sharing a notebook with a teammate produces the same steps and outputs on their side, which keeps reviews straightforward.
You can also bring in data from BigQuery, Sheets, Postgres, and other common sources with simple connectors. Julius keeps relationships between tables organized, so follow-up questions take less time. There is no switching between tools or rewriting queries to compare results, either.
Tip: You can learn to build a Notebook in Julius with our guide.
Why it beats Jupyter Notebook
Faster checks: Ask questions without managing cell order
Cleaner updates: Notebooks stay consistent across runs
Simple sharing: Charts and summaries are ready for review
Organized steps: Julius Notebooks keep each stage of the analysis clear
Reliable follow-ups: Natural language updates the same notebook instead of creating new files
Pros
Direct connectors for common warehouse sources
Recurring reports for ongoing updates
Clear question-driven workflow
Cons
Not built for advanced modeling
Limited control over custom visuals
Pricing
Julius starts at $16 per month for the Plus plan.
Bottom line
Julius helps you get clear answers from connected data without writing SQL, so you can move through everyday analysis faster. If you need deeper modeling or custom scripting, JupyterLab or Hex might be a better choice.
2. Google Colab: Best for GPU training and quick experiments
Google Colab is a browser notebook from Google that runs Python code on hosted CPUs and GPUs without any local setup. It opens standard .ipynb files and connects directly to Drive for easy access to data.
I tested Colab on a few small models to see how quickly I could reach a working run. The setup stayed simple, and I could start training with the default runtime. I liked how easy it was to pull in data from Drive or Sheets and keep everything in one place.
GPU jobs on the Pro tier ran without slowdown, and switching runtimes was fast. Sharing notebooks was easy because everything lived online. Colab worked well for quick tests and small experiments.
Why it beats Jupyter Notebook
Faster setup: Notebooks run without local installs
Easy hardware changes: Switch between CPU or GPU with a menu
Simple sharing: Everything lives in the cloud for easy access
Pros
Hosted environment that starts quickly
Good GPU support on paid plans
Smooth integration with Google data sources
Cons
Session limits can interrupt longer runs
Fewer options for complex project structure
Pricing
Bottom line
3. Deepnote: Best for real-time teamwork
Deepnote is a cloud notebook built for teamwork, and that focus was clear once I started testing it. It handled live edits well, updates from teammates appeared quickly, and cell-level comments kept discussions organized. The layout supported collaboration even when several people worked at once.
I ran SQL and Python cells back to back, and each one responded on time. Table previews opened right away, and charts updated in time to confirm early steps. Version history stayed clear enough to track earlier attempts during the session.
I used it for a few shared tasks and saw how much easier it was to keep everyone aligned. The workspace reduced the back-and-forth that usually slows teams during exploratory work.
Why it beats Jupyter Notebook
Live collaboration: Edits update for everyone
Commenting tools: Feedback appears directly in the cells that need attention
Consistent outputs: Shared runs match across users
Pros
Clear real-time editing
Easy SQL and Python flow
Helpful review tools
Cons
Limited control over visuals
Not ideal for heavy pipelines
Pricing
Deepnote starts at $39 per editor per month.
Bottom line
Deepnote supports active teamwork during early exploration, which helps when many people work inside the same notebook. If you prefer natural language prompts for fast checks, Julius is easier.
4. Databricks Notebooks: Best for large-scale data pipelines
Databricks Notebooks are part of the Databricks platform and give you a workspace for running SQL, Python, and Scala on managed Spark clusters. I ran some test projects in this environment and saw how well it handled bigger data jobs. Setup took little time, and long processes ran without interruptions.
Databricks made it simple to look through tables and confirm updates at each step. Jobs finished reliably, and changing clusters helped me compare performance without adjusting the notebook. Scheduling tasks inside the interface kept the routine stable.
Databricks proved helpful when the work required scale and steady performance. It supported ongoing data operations and kept the workflow clear for teams handling larger workloads.
Why it beats Jupyter Notebook
Cluster support: Runs scale with minimal setup
Supports extended tasks: Longer workloads run fully without breaking
Multi-language cells: Use Python, SQL, and Scala in one place
Pros
Strong Spark integration
Good scheduling options
Reliable performance
Cons
Pricing can add up
Steeper learning curve
Pricing
Databricks Notebooks uses a pay-as-you-go model.
Bottom line
Databricks helps teams run heavy workloads and keep pipelines structured, which matters for data engineering tasks. If reproducibility is the priority, marimo handles that work with a cleaner setup.
5. Hex: Best for mixed SQL and Python analysis
Hex is a notebook-style workspace that lets you mix SQL and Python in the same project. It returned outputs quickly, and the small charts helped me confirm early results before moving ahead.
During testing, the version history became the feature I used the most. It kept older attempts organized and made it easy to compare changes. Shared spaces also helped when I needed feedback from teammates without moving files around.
I used Hex for workflows that needed structure but not heavy modeling. It supported mixed analysis, stayed clear to navigate, and made it easy to revisit earlier ideas.
Why it beats Jupyter Notebook
Mixed workflow: Move between SQL and Python without extra steps
Version history: Earlier changes remain easy to review
Team spaces: Everyone accesses the same files without extra setup
Pros
Smooth SQL and Python flow
Helpful version tracking
Good for lightweight dashboards
Cons
Not ideal for large models
Some features require higher tiers
Pricing
Hex starts at $36 per editor per month, billed monthly.
Bottom line
Hex supports hybrid analysis with SQL and Python, which helps when projects need fast iteration. For reliable daily checks with natural language, Julius may be the better match.
6. marimo: Best for reproducible Python workflows
marimo is a notebook tool that stores each project as a plain Python file. It surprised me once I started testing small analysis scripts that needed clean execution. The reactive model kept outputs tied to the exact code in each cell, and runs were predictable across repeated tests.
During testing, the plain Python file format helped the most. Diffs were simple to review, and the workflow felt close to working in a normal codebase. Running the same notebook as a script required no extra effort, which made checks faster.
marimo works well for teams that want clear, reproducible steps without hidden values in memory. The environment keeps each cell connected to the code that produced it.
Why it beats Jupyter Notebook
Plain files: Stored as Python instead of JSON
Reactive runs: Outputs match the exact inputs
Readable diffs: Reviews are easier to manage
Pros
Strong reproducibility
Clear input and output pairing
Easy version tracking
Cons
Python only
Smaller ecosystem
Pricing
marimo is open source and free to use.
Bottom line
marimo supports predictable analysis by tying each step to clean Python files, which helps when reproducibility matters. If your team needs stronger collaboration, Deepnote is a good option.
7. nteract: Best for clean local notebooks
nteract is a lightweight desktop app for running Jupyter notebooks locally. It loaded quickly when I opened it for the first time, and the desktop app kept the interface simple. Running cells worked well for small checks, and outputs were clear enough to follow during early exploration. The straightforward layout made it easy to focus on the code.
During testing, I opened a few existing notebooks to confirm how rendering behaved. nteract put out clean charts and tables, and the execution order was easy to follow. The app avoided clutter that often slows other tools.
nteract is a good fit when you want a lightweight option for quick tests. It keeps everything local and opens and closes without extra steps.
Why it beats Jupyter Notebook
Simple layout: No extra panels to worry about
Quick startup: Opens notebooks without setup
Clean outputs: Results load in a clear, easy-to-scan format
Pros
Lightweight interface
Easy to manage existing notebooks
Good for small tasks
Cons
Limited features
Few integrations
Pricing
nteract is open source and free to use.
Bottom line
nteract helps with quick local checks for small experiments, which matters when you want a simple environment. For multi-language analytics, Apache Zeppelin handles broader workflows.
8. Apache Zeppelin: Best for multi-language analytics
Apache Zeppelin is a notebook tool for working across SQL, Python, and Spark in the same workspace. It stood out when I tested Spark workflows that needed several languages in one notebook. Switching between interpreters was smooth, and smaller jobs ran without slowdown. The interface kept SQL, Python, and Spark cells organized.
During exploration, browsing Spark tables helped verify structures before running larger steps. Visualizations appeared fast enough to support early checks, and shared notebooks remained easy to read during reviews.
Zeppelin fits data engineering tasks that use multiple languages. The structure is flexible and works well for distributed data work.
Why it beats Jupyter Notebook
Multi-language support: One notebook handles several interpreters
Spark workflow: Strong tools for distributed data
Simple sharing: Results appear the same for everyone who opens the notebook
Pros
Good Spark integration
Flexible interpreter system
Clear outputs
Cons
Setup may take effort
Interface looks dated
Pricing
Apache Zeppelin is open source and is free to use.
Bottom line
Apache Zeppelin fits analytics work that spans SQL, Spark, and Python, which helps when you need one environment for several tasks. For larger data engineering pipelines, Databricks offers more structure.
9. JetBrains Datalore: Best for Python modeling work
JetBrains Datalore is a cloud notebook environment built for Python work. It offered a structured environment when I tested it on a few modeling tasks. I thought code completion worked well, and the editor caught small errors before I ran cells. Datalore produced clean outputs, and the layout kept the step-by-step work easy to follow.
One detail that stood out was the workspace integration. I could open datasets, adjust functions, and rerun cells without losing context. The environment made it easy to compare earlier attempts and keep notebooks aligned with the project.
Datalore proved helpful for Python-heavy work that benefits from smart coding tools. It supported analysis, small models, and quick checks while keeping code readable.
Why it beats Jupyter Notebook
Smart editor: Highlights issues as you write, so fixes take less time
Organized workspace: Keeps notebooks, datasets, and scripts arranged in a clear layout
Clean execution: Runs code in a consistent order so results match what the notebook shows
Pros
Helpful autocomplete
Good project structure
Strong Python tools
Cons
Limited support for other languages
Some features require paid plans
Pricing
JetBrains Datalore starts at $29.17 per user per month.
Bottom line
Datalore supports Python-focused modeling with editor features that speed up early testing, which helps when accuracy matters. If you want a light local notebook setup, nteract is easier to manage.
10. Saturn Cloud: Best for scalable Python sessions
Saturn Cloud is a cloud platform for running Python workloads on scalable hardware. I ran a few larger Python tasks to see how well it handled heavier workloads and found the setup straightforward. I also liked that I could launch sessions with more memory or GPUs without adjusting much.
During exploration, I switched between different hardware options to compare performance. Jobs finished without interruptions, and the workspace made monitoring progress simple. Pulling in data from cloud storage took little time, and longer scripts ran without performance issues.
Saturn Cloud helped when I needed more power than a local environment could offer. It supported bigger tasks without forcing me to manage the underlying infrastructure.
Why it beats Jupyter Notebook
Scalable sessions: Hardware can be upgraded as needed
Hosted resources: No local setup for heavy jobs
Reliable long runs: Larger tasks complete without runtime interruptions
Pros
Flexible hardware choices
Good support for bigger Python jobs
Easy cloud storage access
Cons
Costs rise with usage
Fewer collaboration tools than team platforms
Pricing
Saturn Cloud uses a pay-as-you-go model.
Bottom line
Saturn Cloud supports heavier Python workloads with flexible hardware, which helps when projects outgrow local resources. If your team needs stronger collaboration features, Deepnote is a better fit.
Special mentions
A few tools didn’t land in the top ten, though they’re still strong options for the right tasks. Here are more tools to consider:
Nextjournal: Nextjournal is a notebook platform built for reproducible research, and that focus was clear once I tested it on a small project. Each rerun used the exact environment I saved, which kept outputs consistent.
Polynote: Polynote is a multi-language notebook tool that supports Python, Scala, and SQL in the same workflow. Testing it showed how cleanly it handled mixed languages without dependency issues, and each step produced clear outputs.
Observable: Observable is a browser notebook for interactive data visuals, and it updated charts as soon as I changed an input. I could test small adjustments without restarting anything, which made quick checks easier.
Quarto: Quarto is a publishing system for notebooks and technical documents. It handled reports that combined code and narrative text in one file. I liked that the build system produced consistent outputs each time I rendered a project. It’s useful when the final deliverable needs to look polished.
Kaggle Notebooks: Kaggle Notebooks is a free hosted environment for running Python code with built-in datasets and optional GPUs. It opened fast during testing, and each session handled small experiments without extra setup.
CoCalc: CoCalc is a cloud workspace for math-heavy notebooks, and the built-in LaTeX tools were helpful during testing. Shared editing made group work easier, and everyone could see updates in real time. It suits academic teams working with symbolic math.
How I tested these Jupyter Notebook alternatives
To compare these tools fairly, I worked through a wide range of analyses that covered both quick checks and longer workflows. One of my goals was to see how fast each platform turned a question into a chart or metric. Another was to check how well the workflow held together as more steps were added.
Team scenarios were part of the process, too. I shared notebooks to see how each tool handled edits from multiple people, then ran full workflows from start to finish to confirm whether the results matched across runs.
To keep the testing consistent, I ran the same set of checks across all platforms:
Reproducibility: I reran the same notebook several times to see if outputs matched and whether any tool held on to old values in the background.
Execution order: I moved through cells in different sequences to check how each notebook handled dependency issues or partial runs.
Collaboration: I shared notebooks with teammates to test comments, edit tracking, and how well results carried over across accounts.
Data sources: I connected to BigQuery, Sheets, Postgres, and cloud files to see how quickly each tool pulled in data and refreshed results.
Performance: I ran heavier tasks to check if tools slowed down, lost progress, or needed manual resets.
Structure: I reviewed how each notebook organized steps, files, and outputs during a longer workflow.
How to choose the right Jupyter Notebook alternative
The right Jupyter Notebook alternative depends on how you work, how much structure you need, and whether your projects rely more on collaboration, reproducibility, or scale. Choose:
Julius if you want natural language analysis, quick charts, and notebooks that keep a clear structure across runs.
Google Colab if you need quick GPU access and want to run models without setting up local hardware.
Deepnote if real-time collaboration matters and you want a workspace built for team editing.
Databricks Notebooks if your work relies on Spark, scheduling, or larger engineering pipelines.
Hex if your projects mix SQL and Python and you want fast outputs for early analysis.
marimo if you want reproducible Python workflows stored as plain files.
nteract if you prefer a lightweight desktop app for simple notebook checks.
Apache Zeppelin if you want multi-language notebooks for SQL, Spark, and Python together.
JetBrains Datalore if you prefer smart coding help for Python modeling tasks.
Saturn Cloud if you need larger compute resources for long or heavy Python runs.
Nextjournal if environment snapshots and strict reproducibility are important.
Polynote if you want a reactive notebook that supports multiple languages at once.
Observable if your workflow depends on interactive data visuals.
Quarto if you need polished technical reports that combine code and narrative.
Kaggle Notebooks if you want fast, hosted experimentation with free GPU access.
CoCalc if your work includes teaching, LaTeX, or math-heavy notebooks.
My final verdict
My testing showed me that each tool handled only part of the notebook workflow. Deepnote covered shared editing, Databricks managed larger pipelines, and Hex or marimo supported structured or reproducible code, but all of them added steps that slowed down everyday analysis.
Julius cuts through that by keeping notebooks stable and letting you ask questions in natural language, giving you a faster path from prompt to result. I’ve found the structure reliable during repeated runs and quick reviews. You get an environment built for dependable daily analysis, and that consistency matters when teams need answers without extra steps.
Try Julius if you want an AI alternative to Jupyter Notebook
If you’re sorting through Jupyter Notebook alternatives because your current setup slows you down, Julius gives you a clearer way to work. You ask questions in natural language, and the notebook returns structured results without managing cell order or writing SQL. It connects to your data and keeps each step organized so the workflow stays predictable.
Here’s how Julius helps:
Quick single-metric checks: Ask for an average, spread, or distribution, and Julius shows you the numbers with an easy-to-read chart.
Built-in visualization: Get histograms, box plots, and bar charts on the spot instead of jumping into another tool to build them.
Catch outliers early: Julius highlights values that throw off your results, so decisions rest on clean data.
Recurring summaries: Schedule analyses like weekly revenue or delivery time at the 95th percentile and receive them automatically by email or Slack.
Smarter over time: With each query, Julius gets better at understanding how your connected data is organized. It learns where to find the right tables and relationships, so it can return answers more quickly and with better accuracy.
One-click sharing: Turn a thread of analysis into a PDF report you can pass along without extra formatting.
Direct connections: Link your databases and files so results come from live data, not stale spreadsheets.
Ready to see how Julius can help your team make better decisions? Try Julius for free today.
Frequently asked questions
How do Julius and Jupyter Notebook compare?
Julius provides structured, repeatable notebooks that use natural language, while Jupyter Notebook relies on cell execution that can produce different results depending on run order. You can learn more about their differences in our Julius vs Jupyter comparison.
Are there Jupyter Notebook alternatives that improve reproducibility?
Yes, tools like marimo and Nextjournal improve reproducibility by keeping environments and execution steps consistent across runs. marimo uses plain Python files that remove hidden execution issues, and Nextjournal saves the full environment so reruns match the original setup.
Which is a good Jupyter Notebook alternative for beginners?
Julius is a good Jupyter Notebook alternative for beginners because you can ask natural language questions instead of writing SQL or managing cell order. Julius keeps each step organized in a notebook format, so new users can follow the workflow without learning complex tooling. Google Colab is also beginner-friendly if you prefer a browser-based setup.
