Running notebooks in the cloud means no local setup, easy sharing, and consistent environments. But the landscape has grown crowded, and different platforms optimize for different things. Choosing the right one depends on what problem you're actually trying to solve.
Here's a practical comparison of the major options, based on real tradeoffs rather than feature checkbox marketing.
JupyterHub is the open-source standard that everything else is measured against. You deploy it on your own infrastructure, you control everything, and you get the full Jupyter experience without vendor lock-in.
The appeal is obvious for organizations with specific requirements. If you need to keep data on-premises for compliance reasons, JupyterHub lets you do that. If you want to customize the environment down to the kernel level, nothing stops you. If you're philosophically committed to open source, this is the most open option available.
The downside is equally obvious: you have to manage the infrastructure. Someone needs to set it up, someone needs to keep it running, someone needs to handle scaling when your team grows. That's a real cost, even if you're not paying a vendor directly. Most teams underestimate how much DevOps work is involved until they're already committed.
JupyterHub also doesn't solve the communication problem at all. You get notebooks, but getting those notebooks in front of stakeholders is still your problem. You'll end up exporting, screenshotting, and copy-pasting just like you would with local Jupyter.
Best for: Teams with DevOps resources and specific security or compliance requirements that preclude using hosted services.
Colab is free, it's easy, and it gives you access to GPUs without any setup. For learning Python or machine learning, it's hard to beat. You click a link and you're writing code within seconds.
The friction-free experience is genuine. Google handles the infrastructure, the environment comes pre-configured with popular libraries, and you can share notebooks just by sharing a link. For quick experiments and educational work, Colab removes almost all barriers.
The problems emerge when you try to do professional work. Sessions disconnect unpredictably—sometimes after an hour, sometimes after fifteen minutes. Your state is gone. The variables you computed, the data you loaded, the model you trained—all vanished. This is maddening for anything longer than a quick experiment.
The Google Drive integration sounds convenient until you have hundreds of notebooks and need to find something specific. There's no real project organization, no meaningful search, and no way to connect notebook outputs to anything else you're doing.
For sharing with stakeholders, Colab is particularly weak. You can share the notebook itself, but that means sharing code, which most stakeholders don't want to see. There's no native way to create reports or presentations from your work.
Best for: Learning, prototyping, and ML experiments where you need free GPU access and don't mind occasional session resets.
Hex positions itself as the collaborative data platform for teams. It combines notebooks with dashboards, apps, and scheduling in a way that feels cohesive. If you need to build internal data tools, Hex has thought about that problem more than most.
The SQL integration is genuinely good. You can write SQL and Python in the same notebook, with outputs flowing between them naturally. For teams that live in SQL and only occasionally drop into Python, this is a compelling workflow.
The app publishing feature lets you turn analyses into interactive tools that non-technical users can operate. Set up some parameters, add some dropdowns, and suddenly your ad-hoc analysis becomes a reusable application. That's powerful for teams that repeatedly answer similar questions with different inputs.
The learning curve is real, though. Hex isn't Jupyter—it's Hex. The reactive execution model takes getting used to. The cell dependencies create a different mental model than running cells sequentially. This isn't bad, but it's not what people expect coming from traditional notebooks.
Pricing scales quickly too. The free tier exists but is limited. Once you're paying, you're paying enterprise prices. For a startup or small team, the cost can be hard to justify unless you're getting serious value from the collaboration and app features.
Best for: Teams building internal data apps and dashboards, especially if SQL is central to the workflow and budget isn't the primary constraint.
Observable takes a completely different approach. It's JavaScript-first, reactive by default, and optimized for beautiful interactive visualizations. If you're publishing data journalism or building explorable explanations, Observable is in a class of its own.
The D3.js integration is seamless because Observable was built by Mike Bostock, who created D3. The visualizations you can build are stunning—far beyond what matplotlib or even Plotly can achieve without heroic effort.
The reactive programming model means cells automatically re-run when their dependencies change. This creates a different relationship with your analysis where you're building a live, explorable document rather than a linear script.
The limitation is also the strength: it's JavaScript, not Python. If you're a Python analyst, Observable requires learning a new language and a new ecosystem. Your pandas skills don't transfer. Your sklearn models don't run here. For heavy data processing, you're working against the grain.
Best for: Data journalists, interactive visualization specialists, and anyone publishing public-facing explorable content who's comfortable working in JavaScript.
Deepnote nails real-time collaboration better than anyone else. If you want Google Docs-style multiplayer editing for notebooks, Deepnote delivers. You can see your colleague's cursor, watch them write code, and work on the same analysis simultaneously.
The Jupyter compatibility is genuine. Unlike platforms that reinvent the notebook format, Deepnote feels like Jupyter. Your muscle memory transfers. Your existing notebooks import cleanly. There's no steep learning curve.
The free tier is generous enough for individuals and small teams to get real work done. The onboarding is smooth. Overall, Deepnote feels like someone took Jupyter and asked "how do we make this actually work for teams?" then executed competently on the answer.
What's less developed is the output story. Deepnote is great for collaborative exploration but weaker for communicating results to stakeholders. You can share notebook links, but that means sharing code. There's no native brief or report format that abstracts away the implementation details.
Best for: Teams that want real-time collaborative editing on notebooks and prefer something that feels like Jupyter rather than a reinvented paradigm.
Margin focuses on the gap between analysis and communication. The thesis is that most analysts' pain isn't in the notebook itself—it's in what happens after the notebook is done. The report. The stakeholder presentation. The "can you send me that chart?" requests.
Briefs are the core differentiator. You write your analysis in a notebook, then create a brief that's connected to that notebook. Charts, tables, and outputs embed directly without copy-paste. The connection stays intact—anyone can trace a claim in the brief back through the code to the dataset, so data lineage is built in rather than bolted on.
The sharing model is deliberately simple. You get a link. The link shows your polished brief. Stakeholders see the narrative and the outputs without seeing the code. If they want to verify something, they can click through to the source.
The limitations are real too. There's no real-time collaboration yet. Team features are coming but not here. If you need multiplayer editing or complex app building, Margin isn't the right tool today.
Best for: Individual analysts or small teams whose main pain is the communication layer—getting results out of notebooks and into formats stakeholders trust.
The right choice depends on what problem is actually causing you pain.
If your problem is infrastructure management and you want to stop running servers, any of the hosted options solve that. Pick based on secondary factors.
If your problem is collaboration and your team needs to work on the same notebook simultaneously, Deepnote is purpose-built for that.
If your problem is building internal tools and dashboards, Hex has invested more in that use case than anyone else.
If your problem is communication—getting your analysis to stakeholders in a format they trust and can verify—that's where Margin focuses. The notebook is just the beginning. The brief is the deliverable.
There's no universally correct answer. There's just the answer that fits your situation, your team, and the specific gaps in your current workflow.
Want to try connected reports? Sign up for Margin and see how briefs work.