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The notebook you’ll love to use

Deepnote is a new kind of data science notebook. Jupyter-compatible with real-time collaboration and running in the cloud. Oh, and it's free.

Used by data scientists from

Collaboration

Collaboration done better

We built Deepnote because data scientists don’t work alone. Reviewing other notebooks, presenting your work to colleagues, or handing over your models to an engineering team shouldn’t be painful.

Real-time collaboration

Discuss and debug your models with your colleagues and see what everyone does in real-time.

Versioning

Track changes to your notebooks over time and always know who implemented them with blame tools.

Code review

Have your colleagues review your models and results, track their comments and notify them when you change your code.

Fully reproducible

Deepnote is a standardized environment where you can add more dependencies. Your work can be reproduced by anyone.

Looking for more? Be sure to check out our documentation ->

Main features

Making you more productive

Deepnote increases your productivity even when you are working by yourself, whether you are exploring and cleansing your data or building ML models.

Variables & better plots

See the internal state of your program at a glance with variable explorer and make discovering patterns in your data easier with interactive plots.

Code intelligence

Develop faster with intelligent autocomplete and let configurable linting tools point out bugs before they break your long training jobs.

Shortcuts & Command palette

Browse your files and access all Deepnote features without lifting your hands from the keyboard with Command Palette.

Dashboards or APIs

Coming soon

Turn your notebooks or individual cells into an interactive dashboard that’s always up to date. You can share it with anyone.

Looking for more? Be sure to check out our documentation ->

Integrations

Deepnote loves to work with other services

Deepnote integrates with your existing infrastructure and the workflows you like. Working with your datasets is easy and secure.

GitHub

Attach a public or a private repository to your project. Read data from any branch or commit and keep your notebooks in sync.

S3 and GCS buckets

Attach a public or a private bucket into your project. You will be able to read, edit and upload files to the bucket.

Spark

Connect to a Spark cluster and run jobs directly from Deepnote.

Google Drive

Add a Google Drive to your projects and directly use or edit the files.

Snowflake

Connect to a Snowflake warehouse and query the data with dedicated SQL cells.

Redshift

Connect to a Redshift warehouse and query the data with dedicated SQL cells.

PostgreSQL

Connect securely to a Postgres database without leaking credentials and run SQL queries in an intuitive interface.

Google BigQuery

Connect to your BigQuery warehouse and query the data with dedicated SQL cells.

Missing a service?

We are constantly working on integrating more services. Let us know if the one you’re looking for is missing.

Community

See how popular Python projects use Deepnote

norvig /
pytudes

15.3K

Collection of programming problems and exercises.

Open on GitHub →Launch in Deepnote →

ageron /
handson-ml

22.8K

Fundamentals of machine learning explained in Python.

Open on GitHub →Launch in Deepnote →

ageitgey /
face_recognition

39.9K

Simple facial recognition API for Python.

Open on GitHub →Launch in Deepnote →

Your repository

It’s really easy to set up
Deepnote with your repository.
Check out the docs!

You can create a Deepnote launch button for any of your Python repositories. The a look at how to launch your project in Deepnote.

Pricing

Making data scientists more productive

Whether you’re a team of one or a company, start for free. Upgrade to enable advanced machines and workflows.

Individual

For beginners, hobbyists, and data science side projects.

Free✨

Standard machine
Each month you get up to 750 compute hours for free (after completing the onboarding tasks). That means running one project non-stop, or you can split it among multiple projects.
The Standard machine has 5GB of disk space available.
Learn more
Up to 3 collaborators
Private integrations

Team

For teams and start-ups to collaborate on notebooks and experiments.

$12per editor
per month

Unlimited Standard machine
Each month you get 750 compute hours for each editor. That means running one project non-stop, or you can split it among multiple projects.
The Standard machine has 5GB of disk space available.
Learn more
Pay-per-use Pro machines
Multiple hardware options for bigger projects, billed by the minute.
Learn more
Unlimited collaborators
Team integrations

Free plans for students and teachers

Enterprise

Custom solution for multiple teams & departments with dedicated support.

All benefits of Team and:

Custom, dedicated machines
Account management
Access control and audit logs
Dedicated support
On-prem deployments
Ability to run Deepnote in your own cloud is coming soon.

Get in touch to book a demo and see how Deepnote fits into your existing data science stack.

Your data is always safe and secure. Deepnote is based on open standards so you can take your work anywhere anytime.
Have questions? Take a look into our documentation.