Every few months someone asks me: "should we stick with Jupyter, or move to Hex, or try Deepnote?" The answer is never simple because these three tools are not competing on the same axis. They share a surface-level similarity — you write code in cells, you see output below — but the philosophies underneath are completely different.
Jupyter is infrastructure. Hex is a product. Deepnote is a middle ground that bets on making Jupyter's model collaborative without throwing it away. If you understand those three sentences, you are 80% of the way to the right decision. Here is the rest.
Quick decision matrix
- Pick Jupyter if you need full control, have ops capacity to run JupyterHub, and your team is comfortable with the ecosystem. You will never get vendor-locked, and you can run any language kernel that exists.
- Pick Hex if you want notebooks that turn into stakeholder-facing apps without a separate deployment step, and you are willing to pay $38-65/seat/month for the privilege. Hex's app mode is genuinely the best "notebook to app" experience available.
- Pick Deepnote if you want the Jupyter model — .ipynb files, Python kernels, familiar cell execution — but with real-time collaboration, scheduling, and a managed environment. It is the cheapest cloud option and the most Jupyter-compatible.
- Pick none of the above if your real problem is getting notebook outputs in front of non-technical users reliably. That is a deployment and hosting problem, not a notebook problem.
The collaboration gap
This is Jupyter's biggest weakness and the entire reason Hex and Deepnote exist.
A standard Jupyter notebook is a JSON file with the .ipynb extension. It contains your code cells, markdown cells, and — critically — all the cell outputs, including rendered charts and DataFrames serialized as HTML. Try to diff this in git and you get a wall of Base64-encoded PNG data mixed with cell metadata. Two people editing the same notebook on a branch? Good luck with the merge conflict.
JupyterHub with the Real-Time Collaboration extension (based on Yjs) gives you Google Docs-style simultaneous editing, but it requires running JupyterHub — a non-trivial deployment. You are managing a hub process, spawning containers per user, configuring authentication (OAuth, LDAP, PAM), setting up persistent storage, and sizing compute. It works, but "works" and "easy" are not the same word.
Hex has multiplayer editing baked into the product. Open a notebook, see your teammate's cursor, watch their edits appear in real-time. No setup — collaboration is the default. You also get built-in version control with branching and diffing that actually makes sense, because Hex controls the entire file format. No more JSON diffs.
Deepnote takes a similar approach: real-time multiplayer editing in the browser, comments on cells, sharing via URL with granular permissions. The collaboration model feels lighter than Hex — fewer features, but also less complexity. Deepnote lets you invite unlimited viewers for free, which matters when stakeholders just need to see results without editing.
If your team is more than one person and you are using vanilla Jupyter, you are either dealing with painful git workflows or you have already built a JupyterHub deployment. Hex and Deepnote make that problem disappear, but you pay for it — either in money or in flexibility (cloud-only, no self-hosting).
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Try free →SQL as a first-class citizen
Jupyter's default assumption is that you are writing Python (or R, or Julia). If you want to query a database, you write Python code that connects to it — sqlalchemy, psycopg2, a %%sql magic extension. It works, but SQL lives inside Python strings or magic cells. The experience is not great: no schema browsing, no autocomplete on column names, no query plan visualization.
Hex treats SQL as a peer to Python. You create a SQL cell, connect to a warehouse (Snowflake, BigQuery, Databricks, Redshift, Postgres), write your query, and the result becomes a DataFrame you can reference by name in any downstream Python cell. You write the heavy lifting in SQL where the warehouse can optimize it, then switch to Python for the transformations SQL handles poorly. The handoff is seamless.
Deepnote does this too, with native SQL cells and database integrations (Snowflake, BigQuery, Databricks, Postgres, S3). The experience is similar to Hex — write SQL, get a DataFrame — though Hex's SQL editor is slightly more polished, with better autocomplete and schema navigation.
For teams where analysts think in SQL first and reach for Python second, this is often the deciding factor against vanilla Jupyter. You can install ipython-sql and set up magic commands, but it never feels native.
From notebook to app
This is where Hex separates itself from both Jupyter and Deepnote.
Hex's "app mode" lets you toggle any notebook into a published application. Code cells disappear. What remains is the output: charts, tables, markdown, and input widgets (dropdowns, sliders, text fields, date pickers). A VP who has never seen a line of Python can open the app URL, change parameters, and get updated results. The notebook and the app are the same artifact — no exporting, no deploying, no separate codebase.
This solves the classic "I built an analysis, now my manager wants a dashboard" problem without rewriting your notebook as a Streamlit or Dash app. For teams that frequently share interactive analyses with non-technical stakeholders, this alone can justify Hex's price tag.
Deepnote has presentation mode and can publish notebooks as read-only web pages, but it does not have Hex's interactive app model. A shared Deepnote notebook looks like a notebook — code cells visible, outputs inline. Good for sharing with other technical people, less good for a CFO who just wants to change the date range and see a chart.
Jupyter has Voila, which strips code cells and serves the remaining widgets and outputs as a web app. It works for simple dashboards, but the UX is noticeably worse than Hex's app mode and you are managing your own deployment. There is also nbconvert for exporting to HTML or PDF, but those are static — no interactivity.
Environment management and reproducibility
Getting a consistent Python environment across a team is one of those problems that sounds simple and is anything but.
Jupyter gives you maximum flexibility and zero opinion. You manage your own environment with conda, venv, pip, poetry — whatever you prefer. You can pin exact versions, build custom Docker images for JupyterHub spawners, and reproduce environments perfectly. But every "it works on my machine" bug is your problem to solve. JupyterHub helps by spawning pre-built containers, but someone has to build and maintain those containers.
Deepnote handles this through Docker-based custom images and automatic requirements.txt installation. Each project gets its own compute environment. Customize the Docker image if you need system-level packages, or let Deepnote auto-install your Python dependencies. Noticeably less fiddly than managing your own Jupyter environments.
Hex manages the environment for you. Data science packages come pre-installed, and you can add packages through a UI. No Docker images, no pip versions. The upside is simplicity. The downside: if you need something exotic — a specific C library, a custom-compiled package, a non-standard Python version — you are at the mercy of what Hex supports.
For reproducibility specifically: Deepnote wins on the "I want Jupyter-style control but without managing infrastructure" axis. Hex wins on "I don't want to think about environments at all." Jupyter wins on "I need to reproduce this exact environment three years from now on a completely different system" — because you own the entire stack and can freeze it however you like.
Pricing reality check
Jupyter is free and open-source (BSD license). But "free" is misleading if you need collaboration, scheduling, or managed compute. Running JupyterHub on AWS with proper auth, persistent storage, and autoscaling costs real money — I have seen teams spend $2,000-5,000/month on infrastructure for a 15-person deployment once you account for EC2 instances, EBS storage, an ALB, and the engineering time to keep it all running.
Deepnote starts free: unlimited viewers, one editor, limited compute hours. The Team plan runs about $29/editor/month and includes more compute, priority support, and dedicated compute options (where data processing happens in your VPC rather than Deepnote's infrastructure).
Hex is more expensive. The free tier is limited to one user with restricted features. Professional is roughly $38/seat/month. Team is roughly $65/seat/month and adds scheduled runs, app publishing, and SSO. For a 10-person team on Team, that is ~$7,800/year — not outrageous, but not nothing.
The hidden cost with both is lock-in. Hex uses a proprietary notebook format — your work lives in Hex, and if you leave, you are exporting, not migrating. Deepnote is better here: it uses .ipynb files natively, so your notebooks are standard Jupyter files you can take anywhere. This differentiator does not get enough attention.
Three-way comparison
| Jupyter | Hex | Deepnote | |
|---|---|---|---|
| License / model | Open-source (BSD) | Proprietary SaaS | Proprietary SaaS |
| Deployment | Local, self-hosted, or cloud (any) | Cloud only | Cloud only (dedicated compute option) |
| Real-time collaboration | Via JupyterHub + RTC extension | Built-in, Google Docs-style | Built-in, Google Docs-style |
| SQL cells | Via extensions (ipython-sql, etc.) | Native, first-class | Native, first-class |
| Python support | Full (any kernel, any package) | Managed environment, pre-installed packages | Docker-based custom images, requirements.txt |
| Language kernels | 100+ (Python, R, Julia, Scala, etc.) | Python (primary), SQL | Python (primary), SQL, R |
| Notebook format | .ipynb (JSON, open standard) | Proprietary | .ipynb compatible |
| App publishing | Voila (limited), nbconvert (static) | App mode (interactive, polished) | Presentation mode, published pages |
| Scheduling | Papermill, cron, or managed platform | Built-in scheduled runs | Built-in scheduled runs |
| Version control | Git (painful diffs on .ipynb) | Built-in branching and diffing | Git integration, comments |
| AI assistant | GitHub Copilot via extensions | Hex Magic (built-in) | Deepnote AI (built-in) |
| Data connectors | Any (via Python libraries) | Snowflake, BigQuery, Databricks, Redshift, Postgres, etc. | Snowflake, BigQuery, Databricks, Postgres, S3, dbt |
| Variable explorer | Via extensions | Yes | Yes, with data profiling |
| Self-host option | Yes (JupyterHub) | No | No |
| Free tier | Completely free | 1 user, limited features | Unlimited viewers, 1 editor |
| Paid pricing | $0 (ops cost varies) | ~$38-65/seat/month | ~$29/editor/month |
| Vendor lock-in risk | None | High (proprietary format) | Low (.ipynb compatibility) |
| GitHub ecosystem | ~70k stars across Project Jupyter repos | N/A | N/A |
| Founded | 2014 (Project Jupyter; IPython from 2001) | 2020 (~$52M raised) | 2019 (YC-backed, Czech Republic) |
| Best for | Full control, research, teaching | Analyst-to-stakeholder workflow | Collaborative data science teams |
What I would pick today
For a solo data scientist or researcher: Jupyter, no question. JupyterLab is genuinely good — tabbed editing, integrated terminal, extension ecosystem. You do not need collaboration features when there is nobody to collaborate with, and the freedom to run any kernel, install any package, and work offline is unmatched.
For a 3-8 person analytics team that presents to stakeholders: Hex. The app mode is the reason. If a significant chunk of your work involves turning analyses into interactive deliverables for people who will never look at code, Hex shortens that pipeline dramatically. The cost is justifiable if it replaces a separate app framework and deployment pipeline.
For a data science team that wants managed Jupyter: Deepnote. You keep .ipynb compatibility (this matters more than people think — it means your work is portable), you get real-time collaboration without running JupyterHub, and the price is lower than Hex. If you do not need Hex's app mode, Deepnote gives you 80% of the cloud notebook experience at a lower cost and without the format lock-in.
For teams that need notebooks AND deployment: This is the gap none of these three fully close. Jupyter is great for analysis but terrible at sharing. Hex solves sharing within its walled garden but you lose portability. Deepnote sits in between. If your real problem is getting notebook-based work deployed, scheduled, and accessible with proper auth, that is a hosting problem — and tools like Fastero are purpose-built for that layer, regardless of which notebook you use for the analysis.
The meta-advice: do not optimize for the notebook editor. Optimize for what happens after the notebook is done. The editing experience in all three tools is good enough. The real friction is collaboration, deployment, scheduling, and getting results to the people who need them — and that is where these three diverge the most.
Related: Hex Pricing: Is It Worth It for Your Data Team? | Streamlit vs Hex for Data Teams | Best Platforms to Deploy Python Data Apps
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Last updated: July 2026.