Best software · Developer & data
10 Best Data Science Notebooks of 2026
This list is for data scientists and analysts choosing where to write and share code, queries and charts. It covers hosted team notebooks (Hex, Deepnote, Count), free and open source notebooks (Jupyter, marimo, Google Colab), notebooks inside data and ML platforms (Databricks, Amazon SageMaker Studio), and hosted environments for R and teaching (Posit Cloud) plus a BI tool with notebooks (Mode). We compared documented features and published prices for 10 tools. Scores weigh features at 40%, ease of use at 30% and value at 30%.
By the Mojolin research desk · Updated November 10, 2026 · 10 tools compared · Prices checked on vendor pages, latest November 10, 2026
Questions or corrections: editorial@mojolin.com
Mojolin is self-funded How we score
Hex is our top pick for most data teams: SQL, Python and chart cells share one project, work publishes as a data app, every plan allows unlimited users, and Professional costs $36 per editor per month. Deepnote is the runner-up for teams coming from Jupyter, with real-time editing.ipynb import and Team seats at $39 per editor per month billed yearly that include monthly CPU and GPU credit. Google Colab is the easiest free start for individual work with GPUs. Teams that want open formats should run Jupyter or marimo, teams on a lakehouse should use Databricks Notebooks, and ML teams on AWS should look at SageMaker Studio.
Top picks
Best overall
Hex
8.8 / 10
Data teams that write SQL and Python together and publish the results as apps for the rest of the company.
Read the review →Runner-up
Deepnote
8.7 / 10
Data science teams that want a Jupyter-compatible notebook with live collaboration and included compute.
Read the review →Also great
Google Colab
8.6 / 10
Students, researchers and individuals who want a free hosted notebook with GPU access and Drive sharing.
Read the review →Comparison table
| # | Tool | Pricing | Score | |
|---|---|---|---|---|
| 1 | Hex | Data teams that write SQL and Python together and publish the results as apps for the rest of the company. | Free Community plan; Professional $36 per editor per month, Team $75 per editor per month, Enterprise custom | 8.8 |
| 2 | Deepnote | Data science teams that want a Jupyter-compatible notebook with live collaboration and included compute. | Free plan for up to 3 editors; Team $39 per editor per month billed yearly; Enterprise custom | 8.7 |
| 3 | Google Colab | Students, researchers and individuals who want a free hosted notebook with GPU access and Drive sharing. | See vendor pricing (USD check pending) | 8.6 |
| 4 |  Jupyter | Data scientists and organizations that want the open standard notebook and are willing to run it themselves. | Free and open source; hosting and compute are your own cost | 8.5 |
| 5 |  marimo | Python developers and data scientists who want reproducible, Git-friendly notebooks instead of .ipynb files. | Free and open source; the molab cloud notebooks are free to try | 8.4 |
| 6 |  Databricks Notebooks | Data teams whose data already lives in a Databricks lakehouse and Unity Catalog. | See vendor pricing (USD check pending) | 8.3 |
| 7 |  Count | Analytics teams that present work to stakeholders on a shared canvas and want AI answers they can audit. | Free plan for up to 5 users; Pro $49 per editor per month with free viewers; Enterprise custom | 8.1 |
| 8 |  Posit Cloud | R and Python users, instructors and students who want a ready environment without installing anything. | Free tier; Student $5, Instructor $15, Basic $25 and Standard $75 per month, all month-to-month | 8.0 |
| 9 |  Amazon SageMaker Studio | Machine learning teams on AWS that need notebooks connected to training, deployment and MLOps. | See vendor pricing (USD check pending) | 7.9 |
| 10 |  Mode | Analysts who write SQL first and hand reports and dashboards to business teams. | See vendor pricing (USD check pending) | 7.7 |
Scoring weights
Every tool gets three editorial scores out of 10, from vendor documentation, product pages and the published price. The overall score is the weighted average below, the same method on every list. Full method
The full ranking
Rank 1 · Best overall
Hex
A collaborative analytics workspace where SQL, Python and chart cells live in one notebook that can be published as a data app, with the Hex agent built in.
hex.tech8.8
out of 10
Best for: Data teams that write SQL and Python together and publish the results as apps for the rest of the company.
Hex is our top pick because it covers the whole path from a query to something a business user can open. A project mixes SQL, Python and chart cells, and the same work can be published as a data app with a few clicks. The Hex agent can answer questions and build notebooks, and AI use draws on monthly credit grants included with each paid seat. Pricing starts with a free Community plan, Professional costs $36 per editor per month with up to 5 published apps and 30 day version history, and Team at $75 per editor per month adds unlimited apps, scheduled runs and alerts, shared components and unlimited history. Every plan allows unlimited users, so viewers do not add cost. Medium compute is included on paid plans, while larger machines and GPUs are billed per minute, from $0.32 an hour for the Large profile. Students and educators at universities and bootcamps can get Professional for free.
Standout feature
Notebook to published data app in one tool, with unlimited viewers on every plan.
Use cases
Analytics teams
Turn a SQL and Python analysis into a published app for sales leaders
Outcome: One link that stays current instead of exported screenshots
Data scientists
Run a heavier model on a Large or GPU compute profile
Outcome: More memory only for the minutes it is used
Business users
Ask the Hex agent a question against modeled data
Outcome: An answer built on the team's semantic model
Key features
- SQL, Python and chart cells in one project
- Hex agent for asking questions and building notebooks with AI
- Published data apps, unlimited on Team
- Scheduled runs and alerts on Team
- Semantic models, Git project export and dbt, Airflow and Dagster integrations
Pros
- Unlimited users on every plan, editors are the billable seat
- Moves from ad hoc notebook to published app without another tool
- Team plan has a 14 day free trial with no payment card
Cons
- Community and Professional cap published apps at 5
- Large and GPU compute is billed per minute on top of the plan
- Scheduled runs and shared collections need the $75 Team plan
Rank 2 · Runner-up
Deepnote
A cloud notebook for Python, SQL and R with real-time collaboration, an AI agent, scheduled notebooks and apps built from notebook blocks.
deepnote.com8.7
out of 10
Best for: Data science teams that want a Jupyter-compatible notebook with live collaboration and included compute.
Deepnote is the closest match to a shared Jupyter notebook that a whole team can edit at once. A notebook combines code, SQL, text and chart blocks, people can comment and edit in real time, and work can be imported and exported as .ipynb files so existing notebooks move over. The free plan covers up to 3 editors and 5 projects on Basic machines with 5 GB of RAM and 2 vCPUs, which suits a small group trying it out. Team costs $39 per editor per month billed yearly and adds unlimited viewers and notebooks, scheduled notebooks, background execution, git sync and 30 day revision history. It also includes $280 worth of CPU, $50 worth of GPU and $39 worth of AI credits every month, which changes the value math for compute heavy teams. Warehouse connectors such as Snowflake, BigQuery and Databricks sit on Team, and SSO and HIPAA need Enterprise.
Standout feature
Team seats include $280 of CPU and $50 of GPU every month.
Use cases
Data science teams
Move existing .ipynb notebooks into a shared workspace
Outcome: Live co-editing and comments on the same notebook
Analysts
Schedule a notebook that refreshes a weekly metrics report
Outcome: Results update without anyone rerunning cells
ML engineers
Train on a GPU machine using included monthly credits
Outcome: Experiments without a separate cloud bill
Key features
- Code, SQL, text and chart blocks in one notebook
- Deepnote Agent for generating, editing and explaining code
- Real-time collaboration, comments and git sync
- Scheduled notebooks and background execution on Team
- Import and export as .ipynb
Pros
- Team plan includes $280 of CPU, $50 of GPU and $39 of AI credits each month
- Unlimited viewers and notebooks on Team
- Free plan allows up to 3 editors and 5 projects
Cons
- Free plan AI is limited to 5 agent calls per month
- SSO, audit logs and HIPAA are Enterprise only
- Many warehouse integrations such as Snowflake and BigQuery need Team
Rank 3 · Also great
Google Colab
Google's hosted Jupyter Notebook service with free access to GPUs and TPUs, Gemini built in, and notebooks stored in Google Drive.
colab.google8.6
out of 10
Best for: Students, researchers and individuals who want a free hosted notebook with GPU access and Drive sharing.
Google Colab is the easiest way to start a notebook: it is a hosted Jupyter Notebook service that needs no setup and is free of charge to use, including access to GPUs and TPUs. Notebooks are stored in Google Drive or loaded from GitHub and shared like Google Docs, which makes it a natural fit for classes and research groups. Gemini is built into the interface for generating, explaining and debugging code, and it can run code to answer questions about data. The free version has real limits. Google says expensive resources such as GPUs are heavily restricted, resources are not guaranteed, and notebooks run for at most 12 hours. Colab Pro, Pro+ and Pay As You Go add compute units, faster accelerators and more memory, with Pro+ allowing background execution up to 24 hours. Companies that need IAM controls can use Colab Enterprise in Vertex AI. We could not confirm USD plan prices from this location.
Standout feature
Free GPU and TPU access in a notebook that shares like a Google Doc.
Use cases
Students
Run a course notebook in the browser without installing Python
Outcome: Everyone starts with the same working environment
ML researchers
Prototype a model on a free GPU runtime
Outcome: Quick experiments before paying for compute
Analysts
Share a notebook with a colleague through Google Drive
Outcome: Review and comments without exporting files
Key features
- Hosted Jupyter notebooks with no setup
- Free access to GPUs and TPUs, with limits
- Gemini for generating, explaining and debugging code
- Sharing through Google Drive like Docs and Sheets
- Pro, Pro+ and Pay As You Go for more compute units
Pros
- Free of charge to use with no installation
- Sharing works the same way as Google Docs
- Colab Enterprise is available inside Vertex AI and Google Cloud
Cons
- Free tier resources are not guaranteed and GPU access is restricted
- Notebooks run for at most 12 hours
- Fewer team features such as scheduling and app publishing than Hex or Deepnote
Rank 4
Jupyter
Project Jupyter's free, open source JupyterLab and Jupyter Notebook apps, plus JupyterHub for multi-user deployments and Voilà for sharing results.
jupyter.org8.5
out of 10
Best for: Data scientists and organizations that want the open standard notebook and are willing to run it themselves.
Jupyter is the open standard behind most of this list. Project Jupyter publishes JupyterLab, the classic Jupyter Notebook, JupyterHub and Voilà as free software, and the .ipynb format it defined is what Colab, Deepnote and SageMaker Studio open and export. JupyterLab is a web-based environment for notebooks, code and data with a modular design that extensions build on, and Jupyter supports over 40 programming languages. For teams, JupyterHub deploys notebooks to many users on central infrastructure with pluggable authentication through PAM or OAuth and Docker or Kubernetes for scaling. Voilà turns a notebook into a stand-alone web application for sharing results. The trade-off is that you supply the servers, security and collaboration habits yourself. Sharing a notebook on its own means email, Dropbox, GitHub or the Jupyter Notebook Viewer, which is fine for code but less polished than a hosted workspace for business readers.
Standout feature
The open notebook format almost every other tool here reads and writes.
Use cases
Research labs
Deploy JupyterHub on the lab's own servers
Outcome: Every member gets notebooks next to the data
Data scientists
Work locally in JupyterLab with Python and R kernels
Outcome: No subscription and no vendor lock-in
Analysts
Publish a notebook as a Voilà app
Outcome: Interactive results without exposing code
Key features
- JupyterLab interface with a modular extension system
- Over 40 programming languages including Python, R, Julia and Scala
- Open .ipynb notebook format based on JSON
- JupyterHub for deploying notebooks to many users
- Voilà for turning notebooks into stand-alone web apps
Pros
- Free software with an open document format
- Runs anywhere, from a laptop to centralized infrastructure
- The format most other notebooks here import and export
Cons
- No built-in real-time team workspace like Hex or Deepnote
- You run and secure servers yourself for team use
- Sharing relies on email, GitHub, Dropbox or nbviewer rather than a hosted app
Rank 5
marimo
An open source reactive Python notebook stored as plain .py files that can run as scripts, be served as apps or exported to WebAssembly HTML.
marimo.io8.4
out of 10
Best for: Python developers and data scientists who want reproducible, Git-friendly notebooks instead of .ipynb files.
marimo fixes the hidden state problem that makes many Jupyter notebooks hard to rerun. It is reactive: run one cell and marimo runs the cells that depend on it, and deleting a cell removes its variables, so a notebook always matches its code. Notebooks are saved as pure Python files, which means they diff cleanly in Git, can be imported as modules, tested with pytest and run as scripts in pipelines. Built-in SQL cells query dataframes and databases including Polars, Pandas, DuckDB, SQLite, Postgres and MySQL. For sharing, notebooks can be served as web apps with the marimo CLI or exported to WebAssembly-powered HTML that runs in the browser, and molab runs notebooks in the cloud. The editor includes AI assistance with any hosted or local model, and marimo pair connects coding agents to live sessions. The limits are a Python-only focus and fewer managed team features than a hosted workspace.
Standout feature
Reactive notebooks saved as plain Python files.
Use cases
Data engineers
Keep notebooks in the main repository and review them in pull requests
Outcome: Readable diffs instead of JSON noise
Analysts
Query a DuckDB file with SQL cells and plot the result
Outcome: An interactive notebook that reruns correctly
Educators
Publish an interactive lesson as WebAssembly HTML
Outcome: Students run it in the browser with no install
Key features
- Reactive execution that reruns dependent cells automatically
- Notebooks stored as pure Python files for Git
- Built-in SQL cells for dataframes and databases such as DuckDB and Postgres
- Run as scripts or serve as web apps from the CLI
- AI assistance and marimo pair for connecting coding agents
Pros
- No hidden state, so results are reproducible
- Diffs and code review work like any Python file
- Exports to WebAssembly HTML for sharing without a server
Cons
- Python only, no R or Julia kernels
- Team workspace features depend on how you host it
- Moving from Jupyter means adjusting to reactive rules such as no redefined variables
Rank 6
Databricks Notebooks
The notebook environment built into the Databricks Data + AI Platform, with Python, SQL, Scala and R, Databricks Assistant and dashboards from notebook results.
databricks.com8.3
out of 10
Best for: Data teams whose data already lives in a Databricks lakehouse and Unity Catalog.
Databricks Notebooks are the default for teams already on the Databricks platform, because the notebook sits next to the data, the compute and the governance. Notebooks support Python, SQL, Scala and R with markdown, real-time co-editing and comments, and results can become dashboards without leaving the notebook. Databricks Assistant is context aware, drawing on lineage, related tables and popularity to suggest code, and the Data Science Agent in Genie Code can build a whole notebook for tasks like exploratory analysis, forecasting and machine learning from a single prompt. Usage logs and forks on reports show how an analysis is being consumed. Pricing is pay as you go with no up-front cost and per second billing, based on compute usage, while your cloud provider bills its own resources. That makes cost harder to predict than a per-seat tool, and we could not confirm a USD starting figure for notebooks. Databricks Free Edition gives learners a no-cost workspace.
Standout feature
Notebooks that run on the same governed lakehouse as the data.
Use cases
Data engineers
Explore a Unity Catalog table with SQL and Python in one notebook
Outcome: Analysis on governed data without copying it
Data scientists
Ask the Data Science Agent to draft a forecasting notebook
Outcome: A working first version to refine
Analytics leads
Turn notebook results into a shared dashboard
Outcome: Stakeholders see the output without reading code
Key features
- Python, SQL, Scala and R in the same notebook
- Databricks Assistant and the Data Science Agent in Genie Code
- Real-time co-editing and comments
- Dashboards built directly from notebook results
- Native access to Unity Catalog data and platform compute
Pros
- No separate setup for data access, compute and visualization
- Suggestions use table lineage and popularity
- Free Edition for learning and personal projects
Cons
- Only makes sense if your data platform is Databricks
- Usage-based pricing is harder to forecast than seats
- Cloud provider compute is billed separately from Databricks
Rank 7
Count
An AI analytics canvas where teams query databases, run Python, build reports and check the AI's reasoning together in real time.
count.co8.1
out of 10
Best for: Analytics teams that present work to stakeholders on a shared canvas and want AI answers they can audit.
Count replaces the linear notebook with a canvas, which suits analysts who explain findings to other people. Queries, Python, charts, notes and reports sit side by side on an infinite canvas, teammates comment and edit in real time, and work can be published as reports and dashboards or sent on a schedule by email and Slack. Its AI agent is built around trust: Count says every fact comes with an explanation of how it was made, and the canvas flags uncited claims, assumptions and model inferences. The free plan covers up to 5 users with one project and three canvases. Pro costs $49 per editor per month, viewers are free, AI credits are unlimited under fair use, and it adds unlimited canvases, three database or MCP connections, Slack integration and a 2 GB DuckDB compute layer and Python VM. The governed semantic layer, SSO, row-level security and embedded analytics require Enterprise.
Standout feature
AI answers that flag their own uncited claims and assumptions.
Use cases
Analytics teams
Lay out a market sizing analysis on a canvas with the board deck and survey
Outcome: One shared view of data, reasoning and conclusions
Finance analysts
Schedule a weekly report to Slack
Outcome: Leaders get numbers without logging in
Data leads
Review which AI claims lack a source before a decision
Outcome: Fewer unchecked numbers in final reports
Key features
- Infinite canvases for exploring and presenting data
- AI agent with reasoning chains that flag uncited claims and assumptions
- Database and MCP connections to BigQuery, Snowflake and Postgres
- Scheduled reports by email and Slack
- Python cells on a DuckDB compute layer
Pros
- Viewers and report viewers are free
- Unlimited AI credits on Pro under fair use
- Real-time collaboration and comments on the canvas
Cons
- Pro allows only 3 database connections
- Semantic layer, SSO and row-level security are Enterprise only
- Canvas layout differs from a linear notebook, which takes adjustment
Rank 8
Posit Cloud
Posit's hosted RStudio and Jupyter Notebooks in the browser, with shared spaces, project templates and month-to-month plans.
posit.co8.0
out of 10
Best for: R and Python users, instructors and students who want a ready environment without installing anything.
Posit Cloud, from the company behind RStudio, is the most practical hosted option for teams and classes that work in R as well as Python. It runs RStudio and Jupyter Notebooks in the browser, each project in an isolated container with its own compute, storage and packages, and packages install from pre-built binaries. Shared spaces add role-based access, project templates and shared data connections, and instructors can mark a project as an assignment so every student gets their own copy with the same environment. Compute scales from 1 GB of RAM and 1 CPU up to 32 GB and 8 CPUs. The pricing is simple and month to month: a free tier that does not expire, Student at $5, Instructor at $15, Basic at $25 and Standard at $75 per month. It has fewer AI features and stakeholder-facing outputs than the analytics workspaces higher on this list, and publishing Shiny apps and reports goes through the separate Connect Cloud.
Standout feature
Hosted RStudio and Jupyter with a free tier that does not expire.
Use cases
Instructors
Hand out a project template as an assignment
Outcome: Every student starts with identical packages
R analysts
Run RStudio in the browser on a shared space
Outcome: No local install or dependency issues
Small teams
Share a ready-to-go project with installed packages
Outcome: Colleagues open and rerun it immediately
Key features
- RStudio and Jupyter Notebooks in the browser
- Projects in isolated containers with their own packages
- Shared spaces with role-based access and templates
- Assignments that give each student a copy of a project
- Compute from 1 GB RAM and 1 CPU up to 32 GB and 8 CPUs
Pros
- Free tier with no expiration date
- All paid plans are month-to-month
- Strong support for R as well as Python
Cons
- Fewer AI and BI-style sharing features than Hex or Count
- Publishing apps uses a separate product, Connect Cloud
- Compute tops out at 32 GB RAM and 8 CPUs
Rank 9
Amazon SageMaker Studio
AWS's web-based interface for end-to-end model development, with managed JupyterLab, Code Editor and RStudio on scalable compute.
aws.amazon.com7.9
out of 10
Best for: Machine learning teams on AWS that need notebooks connected to training, deployment and MLOps.
SageMaker Studio is a notebook environment for machine learning engineers more than for analysts. It is a single web interface for model development where you launch fully managed JupyterLab, Code Editor or RStudio and scale compute as needed. Kiro is available directly in Studio JupyterLab, and teams can bring other ACP-compatible coding agents, including Claude Code. From the same place you can upload data, train and tune models, compare experiments and deploy to production, and SageMaker JumpStart offers hundreds of publicly available models plus over 15 prebuilt solutions. Studio can also create and connect to Amazon EMR clusters and run AWS Glue interactive sessions for data preparation. Pricing follows AWS usage: the Free Tier gives 250 hours a month of an ml.t3.medium instance for Studio notebooks for the first two months, then you pay for instances. That model is flexible but harder to budget, and the interface is more complex than the analytics tools above.
Standout feature
Managed JupyterLab connected to the full AWS model training and deployment stack.
Use cases
ML engineers
Fine-tune a JumpStart model from a JupyterLab notebook
Outcome: Training and deployment without switching tools
Data engineers
Debug a Spark job on Amazon EMR from Studio
Outcome: Spark UI access next to the notebook
Platform teams
Standardize ML workflows with SageMaker MLOps tools
Outcome: Repeatable pipelines with audit trails
Key features
- Managed JupyterLab, Code Editor and RStudio
- Kiro and any ACP-compatible coding agent in JupyterLab
- SageMaker JumpStart with hundreds of public models
- Connections to Amazon EMR and AWS Glue interactive sessions
- MLOps tools for training, deployment and monitoring
Pros
- Covers data prep through deployment in one interface
- Compute scales up for training without leaving the notebook
- AWS Free Tier includes 250 hours a month of ml.t3.medium for 2 months
Cons
- Steeper learning curve than hosted analytics notebooks
- Usage-based instance billing is hard to forecast
- Sharing results with business readers is not the focus
Rank 10
Mode
A collaborative BI platform from ThoughtSpot that connects a SQL editor, Python and R notebooks and visual reports in one place.
mode.com7.7
out of 10
Best for: Analysts who write SQL first and hand reports and dashboards to business teams.
Mode, now part of ThoughtSpot, was built for SQL-first analysts and still combines a SQL editor, Python and R notebooks and visual reporting in one product. A query result can feed a notebook and then a chart or dashboard without exporting, which is why analysts use it for ad hoc work that later becomes self-serve reporting. The free Studio plan is for individual analysts with SQL, Python and R, private database connections, up to 3 users and 10 MB per query. Pro adds 250 GB per month with 5 GB per query, schedules, permissioning, email and Slack sharing and API access, and requires at least 3 users; Enterprise starts at 50 users with custom data compute, SAML SSO and premium support. Both paid plans are sold through a demo, so there is no published price to compare. Mode is more of a BI tool with notebooks than a pure notebook, which suits analysts who report but limits heavier data science work.
Standout feature
SQL editor, notebooks and dashboards connected in one report.
Use cases
Analysts
Write a SQL query and extend it in a Python notebook
Outcome: One report from query to chart
Data teams
Publish reusable datasets for self-serve reporting
Outcome: Business users explore governed data
Ops teams
Schedule a report to Slack each morning
Outcome: Daily numbers delivered automatically
Key features
- SQL editor connected to Python and R notebooks
- Visual Explorer and custom HTML dashboards
- Reusable datasets and scheduled reports
- Email and Slack sharing on Pro
- GitHub sync, REST APIs and dbt freshness sync
Pros
- Free Studio plan for individual analysts, up to 3 users
- Query results flow straight into notebooks and charts
- Mode says teams can be running in 30 minutes or less
Cons
- Pro and Enterprise prices are quote-only
- Pro requires a minimum of 3 users and Enterprise 50
- Studio limits queries to 10 MB
Frequently asked questions
+What is the best data science notebook in 2026?
Hex is our top pick for data teams because SQL, Python and charts share one project, results publish as data apps, and every plan allows unlimited users. Deepnote is the best alternative for teams that want a collaborative take on Jupyter.
+What is the best free data science notebook?
Jupyter and marimo are free and open source if you run them yourself. Google Colab is free of charge with limited GPU and TPU access, and Hex, Deepnote, Count, Mode and Posit Cloud all have free plans with usage limits.
+Which notebook is best for team collaboration?
Deepnote and Hex both support real-time editing and comments with free or unlimited viewers, and Count adds a shared canvas for presenting work. Jupyter needs JupyterHub and your own servers for multi-user setups.
+Is marimo a replacement for Jupyter?
For Python work, often yes: marimo is reactive, so cells rerun when their inputs change, and notebooks are saved as plain Python files for Git. Jupyter still supports over 40 languages, including R and Julia, which marimo does not.
+Which notebook is best for machine learning?
Google Colab is the easiest way to get free GPU and TPU time for experiments. Teams training and deploying models at scale should use Amazon SageMaker Studio on AWS or Databricks Notebooks on a Databricks lakehouse.
About this list
Updated November 10, 2026 by the Mojolin research desk. Mojolin is self-funded
Questions or corrections: editorial@mojolin.com
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