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

Comparing two data science notebooks.

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Hex

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The Data Workspace for Teams. Work with data in collaborative SQL and Python notebooks. Share as interactive data apps that anyone can use.
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Deepnote

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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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Hex vs Deepnote

Hex vs Deepnote: a side-by-side comparison for 2023

Hex is a modern Data Workspace. It makes it easy to connect to data, analyze it in collaborative SQL and Python-powered notebooks, and share work as interactive data apps and stories.

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.

AI
Both platforms offer AI assistance for coding. Both Hex and Deepnote boast an advanced AI agent capable of creating SQL, Python, and text blocks, ensuring expected results and self-correction.  However, Deepnote additionally offers a best-in-class advanced AI code completion powered by Codeium.

Jupyter Compatibility
Both Deepnote and Hex 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 Hex both have built in support for Python and R.  Hex also features basic code completion, whereas, as mentioned previously, Deepnote uses AI code completion, provided by Codeium.  This delivers a significantly better experience when doing exploratory data science, as Deepnote can deliver context aware code straight to your SQL blocks.

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 Hex 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 Hex 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.

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

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.  Hex 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. Hex additionally has one extra tier, which unlocks  With enterprise plans, both platforms want you to talk to their respective sales teams.

Conclusion
In summary, when comparing Hex and Deepnote in 2023, we find two powerful data workspace solutions catering to data professionals and collaboration.

Both platforms offer advanced AI assistance, Jupyter compatibility, and robust data connection capabilities. Deepnote excels with superior AI code completion and a wider range of integrations.

They both provide strong data interaction and visualization tools, as well as data publishing features with fine-grained permissions.

Collaboration is a key focus for both platforms, featuring collaborative features and fine-tuned permissions.

Ultimately, your choice may depend on your specific needs and preferences, but both Hex and Deepnote offer powerful solutions for collaborative data science, analysis, and machine learning tasks.

Hex

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?

No, you must use a managed offering
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 data warehouses (Google BigQuery, ClickHouse, etc.)
Connect to databases (MariaDB, SQL Server, etc.)
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

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

Notebook scheduling

Notebook scheduling

Notebook scheduling is built in
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 items within a notebook

Notebook organization

Notebook organization

View notebooks in a list
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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