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Where data teams do their best work

Deepnote is a new kind of data science notebook. Jupyter compatible with real-time collaboration and runs in the cloud.

Deepnote is completely free. Get started in 10 seconds.

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The world’s best data teams use Deepnote

Move faster with collaboration

We built Deepnote because analysts and data scientists don’t work alone.

Click on a link to jump into any project and see in real-time what everyone is up to.

No more out-of-sync files.

“Working in Deepnote is like code-review and rapid prototyping at the same time, saving valuable time in the iteration cycles.”

Luca Naef

Luca Naef

CTO at VantAI VantAI
Read VantAI case study →
Team of 4 collaborators with different permissions for greater data security

Share with your team, your clients, or the world

No need to email files or take screenshots of charts. In Deepnote, you can share projects by sending a link to anyone. By setting workspace permissions, you decide who can edit code and who can view it.

Team of 2 collaborators using Deepnote comments in order to communicate and work together directly within a data science notebook

Review work in the right context

Discuss and debug in real-time by commenting on code and visualizations. No more email or Slack messages to get feedback on your work.

Deepnote showing the history of changes made by data scientists in a team to a notebook

See history, track changes

Are you looking at the latest version of your file? See all changes as they happen when you are working with colleagues or clients.

Making you more productive

Explore all our features →

We created Deepnote to help you save time. Whether you work in a team or by yourself, Deepnote helps you clean your data, write complex queries, build predictive models, and ship beautiful apps.

Integrated Snowflake, BigQuery, Redshift and PostgreSQL connections showing automatic Deepnote analysis of dataframes

From Python to SQL and back

Don’t jump between multiple apps. Query data from wherever it’s stored, including Snowflake, BigQuery, Redshift and PostgreSQL. Switch between SQL and Python in order to transform, clean, and export your data.

Deepnote visualisation showing an automated analysis of a dataframe as a line chart

Visualize data in seconds

Rapidly generate interactive charts to discover patterns in your data. No need to fuss with visualization libraries. Simply pick the data you want to plot, and Deepnote will do the rest.

Three separated Deepnote projects with different environments which are completely separate for spotless reproducibility

Reproducible results

No more broken pip installs or mismatched requirements. Ensure reproducibility across your workspace by re-using the same environments throughout your projects. Create custom environments within Deepnote or import existing ones from Dockerhub, GCR, and ECR.

Deepnote showing different variables in a variable explorer for instant analysis of data and autocomplete for a full development environment experience

Write faster with autocomplete

Develop faster with intelligent autocomplete and linting tools. Deepnote points out bugs before they break long training jobs, just like you are used to from a local IDE such as VSCode, PyCharm or Sublime Text. The table names and columns are auto-suggested when writing SQL.

Explore all our features →

Integrates with everything

Deepnote integrates flawlessly with all your existing infrastructure and processes.

Use Python, R, Julia, TensorFlow, PyTorch, or any of your favorite languages or frameworks.

We’ve also pre-built 100s of native integrations that simplify the process of connecting your data sources.

All integrations are encrypted and can be easily shared with your workspace. No exposed passwords in your notebooks.

Show all integrations →


Attach a repository to your project to read or commit to any branch.

Google Drive

Mount Google Drive directories in your projects to read and edit its files.


Connect to your BigQuery warehouse and query the data using Deepnote SQL blocks.


Connect to a Snowflake warehouse and query the data using Deepnote SQL blocks.

Amazon S3

Attach a bucket to your project and read, edit or upload files to the bucket.


Connect to a Postgres instance and use SQL directly from a notebook interface.

Deepnote has a growing list of integrations. Show all integrations →

Enterprise-grade security

Secure by default, Deepnote has SOC 2 and follows industry best practices, including fine-grained access controls, SSO support and on-premise deployments.

SOC 2 Type 2
Encrypted data and secrets
Access logs
Snowflake, MongoDB, PostgreSQL and an Amazon S3 bucket connected to a Deepnote project as integrations


Deepnote is the favorite tool for thousands of data scientists all over the world.

Showcase your projects, join the discussion on best data science practices, or simply learn faster thanks to our community.

Join the community →

Thousands of members

Our community forums are the best place to get started and get answers to your Deepnote and data science questions.

The best place to learn

Whether it’s machine learning, visualizations, working with pandas or posting memes, we've got you covered.

Build Deepnote with us

Your feedback is what ultimately shapes Deepnote. Our community is the best place to give us feedback on new features you would like to see.

Explore the community
norvig / pytudes

Collection of programming problems and exercises.

Launch in Deepnote →

Simple facial recognition API for Python.

Launch in Deepnote →

Fundamentals of machine learning explained in Python.

Launch in Deepnote →
Launch your repo in Deepnote

Loved by data scientists and analysts

Awesome service for working on Jupyter Notebooks collaboratively. My students are currently using it for their machine learning class projects; and it's also useful for me to check & comment. Really cool is the data management. No mounting required, it's just drag and drop.

Testimonial avatar Sebastian Raschka
Sebastian Raschka
Assistant Prof. of Statistics at @UWMadison

@DeepnoteHQ Amazing tool. Have been using for just couple of days. Feels great to just start working rather than cracking my head on getting packages installed.

Testimonial avatar Darshan Ramesh
Darshan Ramesh

Delightful user experience reminds me of Superhuman with the command palette and constant reminders of how to use hotkeys to work more efficiently.

Testimonial avatar Mike Xie
Mike Xie
Data Scientist, Team Lead at Lambda School

Very impressed with the new @DeepnoteHQ! Managing SQL in a notebook's a nightmare! So the fact that you can now combine SQL cells with Python code is just SO neat! + connect direclty to Postgres, S3 or BigQuery.

Testimonial avatar Charly Wargnier
Charly Wargnier
Py Dev | BI & SEO Consultant

Got early access to @DeepnoteHQ.This stuff is insane! Super intuitive and amazing UX. It's like a collaborative VSCode + Jupyter + Colab.

Testimonial avatar Ishaan Malhi
Ishaan Malhi
Machine Learning Engineer at Apple

@DeepnoteHQ is amazing. You name the feature, it has all covered. Great UI, real-time collaboration, variable explorer, keyboard shortcuts, and many more make it a perfect Jupyter environment for the data scientist.

Testimonial avatar Shubham Agarwal
Shubham Agarwal

Machine learning is a very empirical discipline so iteration speed is everything - working in Deepnote is like code-review and rapid prototyping at the same time, saving valuable time in the iteration cycles.

Testimonial avatar Luca Naef
Luca Naef
CTO at VantAI

Working on @ca_covid and trying out @DeepnoteHQ 1.5 years after a demo that was impressive even then and... wow. A bit blown away tbh.

Testimonial avatar Alec Wilson
Alec Wilson
Data Scientist at Vaccinate CA

@DeepnoteHQ sealed the deal with real-time collaboration and fast boot times. I rarely find myself editing notebooks on my local machine anymore.

Testimonial avatar Adithya Balaji
Adithya Balaji
Carnegie Mellon CS Masters Student

Get started with Deepnote today

Deepnote is a new kind of data science notebook. Jupyter-compatible with real-time collaboration and running in the cloud.

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