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Noteable vs Deepnote:
a side-by-side comparison for 2024

Comparing two data science notebooks.

A screenshot of Noteable
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Noteable

Website
Noteable is a collaborative notebook platform that enables teams to use and visualize data, together.
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A screenshot of Deepnote
Deepnote logo
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Deepnote

Website
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.
Get started – it’s free
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Noteable vs Deepnote

Noteable is a collaborative data science platform based on Notebooks, that is meant to service any teams performing EDA or ETL tasks.  Noteable prides itself on its collaborative features, stating that teamwork isn’t just an afterthought, it’s the heart of every project.

Deepnote is a collaboration first data platform. It provides a powerful, cloud-based workspace that allows users to easily explore, collaborate on, and share data, create interactive charts and dashboards, and build and deploy machine learning models.

Let’s break it down.

AI
Let’s start with the hottest topic, AI. Deepnote features block AI to help you write, fix, and explain your code.  As of the time of writing, Deepnote has a best-in-class AI autonomous AI agent that is able to create SQL blocks, Python Blocks, text blocks, all while running the notebook to make sure that it is getting the expected results, and fixing itself if it isn't.  Noteable’s AI comes in the form of a ChatGPT plugin, which requires you to use ChatGPT to stream the results to Noteable.

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Jupyter Compatibility
Both Deepnote and Noteable platforms are entirely Jupyter compatible.  You will not have to learn any new proprietary Notebook.  You will encounter the same Jupyter you know and love.  You can upload your Notebooks as an IPYNB and instantly begin working on your Notebook.  

Exploratory Coding
Deepnote and Noteable both have built support for Python and R.  Noteable does not feature any coding assistance or code completion, whereas Deepnote uses state of the art AI code completion, provided by Codeium.  Noteable does have a ChatGPT plugin, but it requires configuration.

Connecting to your data
Connecting to your various data sources, an essential part of data science, is handled eloquently by both platforms.  Both Deepnote and Noteable feature a dizzying array of  built-in data connectors for major cloud platforms, such as BigQuery, Snowflake, Redshift, Athena, and Clickhouse.  This is complemented by both platforms with an easy drag-n-drop for CSVs.  Deepnote has the slight edge in file based connections, where it has first class integrations with Google Drive, Google Cloud Storage, Amazon S3, Dropbox, OneDrive, and Google Sheets.  Deepnote also allows for first class integrations with version control software such as Git and GitLab.  Overall Deepnote has over double the amount of first class integrations. 

Interacting and visualizing your data
SQL is an important chunk of any notebook, and both Deepnote and Noteable have you covered with Notebook SQL blocks, which come equipped with code completion and built in data frame outputs.  Both apps come with built-in charting solutions, while still allowing users to use all python charting libraries with ease.  Additionally, both platforms feature an in-app schema explorer, so you can get a grasp of your databases visually.

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Publishing your data
If you don’t report your findings, did you find anything at all?  Noteable allows you to share your notebook, with both visualizations and code available to public users.  Deepnote ships with fully fledged reporting and dashboarding features called Deepnote Apps.  Both platforms make it easy to schedule your notebook, prepare public views, and even have interactive views.  Both platforms ship with fine grade permissioning that allows organizations to share reports publicly and within the organization.

deepnotexnoteable-publishig.png

Collaboration
It comes as no surprise, that since both platforms bill themselves as collaboration first, that both platforms share a numerous amount of features meant to increase collaboration.  Fine tuned permissions, collaborative notebooks, multi-user editing, commenting.  

Pricing
Deepnote comes equipped with a free tier, and a free two week trial on signup, with no credit card needed.  Noteable also has a free tier, and also offers a two week free trial.  Both platforms are almost identical in offerings, with slight differences in price and compute spend. With enterprise plans, both platforms want you to talk to their respective sales teams.

Conclusion
If you prioritize advanced AI capabilities and seamless coding assistance, or reporting, Deepnote may be the better choice. It offers a wider range of first-class integrations and an autonomous AI agent. On the other hand, if you prefer a platform that emphasizes collaboration and are willing to configure additional plugins, Noteable is a strong contender. Ultimately, your choice should align with your specific requirements and preferences.

Noteable

Deepnote

Setup

Is it managed?

Is it managed?

Fully managed (setup in minutes)
Fully managed (setup in minutes)

Can you self-host?

Can you self-host?

You can self-host (setup in hours)
No, you must use a managed offering

Features

Is it Jupyter compatible?

Is it Jupyter compatible?

Jupyter-compatible
Jupyter-compatible

Programming languages

Programming languages

Jupyter languages (e.g. Python, R)
SQL
Jupyter languages (e.g. Python, R)
SQL

What kind of data sources can you connect to?

What kind of data sources can you connect to?

Connect with Jupyter libraries (e.g. SQLAlchemy, psycopg2)
Connect to databases (Trino, Snowflake, CockroachDB, PostgreSQL)
Connect to data warehouses (Amazon Redshift, Google BigQuery, Databricks)
Provided file storage
Connect with Jupyter libraries (e.g. SQLAlchemy, psycopg2)
Connect to data warehouses (AWS, GCP, etc.)
Connect to databases (Postgres, MongoDB, etc.)
Provided file storage

What kind of data visualization can you do?

What kind of data visualization can you do?

Jupyter data visualization (e.g. Matplotlib, Altair, Plotly)
UI for building charts
Jupyter data visualization (e.g. Matplotlib, Altair, Plotly)
UI for building charts

Reactivity

Reactivity

No reactivity, you decide the execution order
No reactivity, you decide the execution order

Notebook scheduling

Notebook scheduling

Notebook scheduling is built in
Notebook scheduling with additional tools
Notebook scheduling is built in

Management

Reproducibility

Reproducibility

Environments are reproducible by default
Execution is reproducible by default
Run notebooks in containers
Environments are reproducible by default
Run notebooks in containers

Version history

Version history

Version history is built in
Version history is built in

Collaborative editing

Collaborative editing

Multiple editors at the same time
Multiple editors at the same time

Comments

Comments

Comment on items within a notebook
Comment on a notebook as a whole
Comment on items within a notebook

Notebook organization

Notebook organization

File-based
View notebooks in a tree, like a wiki

Licensing

License

License

Proprietary
Proprietary

Price

Price

Free tier
Pay-per-user
Pay for compute
Free tier
Pay for compute
Pay-per-user

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Alternatives to Noteable and Deepnote

Compare Noteable and Deepnote with other data science notebook tools.

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Noteable

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