Deepnote is, in one phrase, Jupyter as a Google Doc. It takes the Jupyter notebook format that data scientists already know and live in, and rebuilds it as a cloud-hosted, real-time-collaborative web app where multiple people can edit the same notebook at the same time, comment in the margins, and never have to worry about Python environments or pip install again.

That description is small and that description is the entire pitch. Deepnote did not try to reinvent the notebook. It took the format the data science world had standardized on — the .ipynb file — and made it pleasant to use as a team. For data science groups that live in pandas and scikit-learn, this is exactly the right product.

What It Actually Is

Deepnote is a browser-based notebook environment that runs Python (and SQL, and R) in cloud-hosted compute environments. Each project has a managed Python environment (you specify packages in a requirements file or via the UI), persistent storage, and a notebook UI that looks and behaves like Jupyter — because under the hood it largely is Jupyter, with a custom collaborative front end and a managed runtime.

The features that make it more than "hosted Jupyter":

• Real-time multiplayer editing. Multiple analysts can open the same notebook and see each other's cursors, exactly like Google Docs. Comments and threads attach to specific cells.
• Native SQL blocks. Connect to Snowflake, BigQuery, Postgres, or any major warehouse and write SQL in dedicated SQL cells. Results land as pandas DataFrames automatically, ready for Python.
• Managed environments. No more "works on my machine." Environments are defined per project and persist across sessions and collaborators.
• Scheduled runs and notebook-as-pipeline. Deepnote notebooks can be scheduled like cron jobs, which is an underrated way to run lightweight production data work without a full orchestration tool.
• AI assistance (Deepnote AI). Generates code, explains cells, and answers questions about a dataset.

The Origin Story

Deepnote was founded in 2019 by Jakub Jurových, Filip Stollár, and Allan Campopiano, originally in Bratislava, Slovakia. The founders were Jupyter power users who got tired of the painful machinery around it: managing Python environments, sharing notebooks via email or Git, keeping environments in sync across a team. The hypothesis was that the content of a Jupyter notebook was great but the operational experience of using one in a team was unacceptable in 2019, and that fixing the operational experience without changing the content format was a multi-billion-dollar opportunity.

Deepnote went through Y Combinator (S19), raised from Index Ventures and Accel, and built a strong following inside data science teams at research labs, universities, and ML-heavy startups. It's particularly visible in academic and bioinformatics communities, where Jupyter is the lingua franca.

Where It Sits Relative to Hex and Mode

This is the question every data leader asks. The honest answer:

Hex is for a mixed data team — analysts and data scientists together — where SQL workflows and Python workflows are both first-class and where the output is often a polished interactive app for stakeholders. Hex's reactive execution model and app builder are the differentiators.

Mode is for SQL-first analytics teams that want a polished reporting tool with some Python capability on the side. It's the legacy default for many established data teams.

Deepnote is for Python-first data science teams that want their existing Jupyter workflow to be collaborative without learning a new mental model. If your team thinks in DataFrames first and SQL second, Deepnote is the most natural fit. If your team thinks in SQL first, Hex or Mode will feel better.

The simple way to remember it: Hex won the analytics workspace war; Deepnote is winning a sub-war for Python-native data science teams. Both can be the right answer.

The Opinionated Take

Deepnote made a deliberate choice not to reinvent the notebook. That choice has costs and benefits. The cost is that Deepnote inherits Jupyter's structural limitations: linear cell execution, no built-in reactivity, the perennial "did I run that cell yet?" problem. The benefit is that any data scientist who has ever used Jupyter — which is essentially all of them — can be productive in Deepnote within five minutes, and existing Jupyter notebooks import cleanly.

For ML and research teams, this tradeoff is correct. For analytics teams that prioritize trustworthy, reproducible analyses, Hex's reactive model is the better foundation. The fact that both products have grown is evidence that "data workspace" is actually two adjacent markets pretending to be one.

There's also a subtle strategic question: as AI assistants get better at writing notebooks, the value of "perfect Jupyter compatibility" matters more, not less, because every model on Earth was trained on Jupyter notebooks scraped from GitHub. Deepnote's bet on the format may age better than people thought in 2022.

Where Deepnote Fits in the Stack

Deepnote sits in the analyst/data-scientist authoring layer, between the warehouse and the human. It connects natively to Snowflake, BigQuery, Redshift, Postgres, MySQL, and S3, and it can run scheduled notebooks for lightweight production work. It is not a BI tool and not an orchestration tool, though it overlaps with both at the edges.