Where data teams do their best work

Deepnote is a new kind of data notebook that’s built for collaboration — Jupyter compatible, works magically in the cloud, and sharing is as easy as sending a link.

No credit card required. Run your first notebook in 30 seconds.

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The world’s best data teams use Deepnote

Collaborative by default

We built collaboration into Deepnote by default because data teams don’t work alone.

Deepnote runs seamlessly in the cloud, making environment management a non-issue. And sharing work is as easy as sending a link (think Google Docs).

Allie Russel

Allie Russell · Senior Manager, Data Science at Webflow

“Deepnote allowed us get on the same page through collaboration, and everyone gets to use their preferred tools.”

Team of 4 collaborators with different permissions for greater data security

Made in Deepnote

Explore what data teams are building in Deepnote


Snowpark for Python

Run ML in Snowflake


Climate trends

IPCC report analysis


Weather prediction forecast

Facebook Prophet


Dynamic SQL cells

With Jinja and Python


Detecting credit fraud

TensorFlow and Keras

See more notebooks

Integrates with your data stack

Deepnote works with the tools and frameworks you’re already using and familiar with. Use Python, SQL, R, TensorFlow, PyTorch, and any of your favorite languages or frameworks. Easily connect to data sources with dozens of native integrations.

Becca Carter

Becca Carter · Analytics Lead at Gusto

”Deepnote was incredibly easy to set up and allows us to start new notebooks in seconds.”

Datastores and Metrics



Built to give you superpowers

We created Deepnote to make you more productive. 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.

Language support

Work with Python, SQL or R — all at the same time.


Visualize DataFrames as no-code configurable charts.


Go back in project history and restore past versions.


Get real-time hints when working in Python or SQL.


Collaborate with your team in real time.


Run your notebooks hourly, daily, weekly, or monthly.


Run scripts, installations, and other tasks inside an integrated terminal.

Hardware options

Choose the right machine for the job and monitor performance.

Custom Environments

Ensure everyone runs in the same shared environment.


Control who can view, comment, code, edit, and manage notebooks.


Collect feedback and iterate faster by working asynchronously.


Connect to your data warehouse and tools in seconds.

Enterprise-grade security

Secure by default by following the industry best practices like fine-grained access controls, SSO support or on-premise deployments.

  • Encrypted data and credentials
  • SOC2 and PCI compliant
  • Audit logs
  • Notebook history

Loved by 1,000s of data scientists and analysts

Showcase your projects, jump in on the conversation, and learn faster by joining our community.

Testimonial avatar Allie Russel
Allie Russel
Sr. Manager, Data Science at Webflow

Deepnote enables us to bring people into the phase of data science that’s all about experimentation, helps them understand our processes, and encourages folks to leverage data science in even more ways.

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.

@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.

Deepnote community 💙

Over 5,000 members

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

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

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

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.

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.

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

Join the world's best data teams and get started with Deepnote

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