marimo vs Deepnote
They solve different problems that both get filed under "better notebook". marimo fixes the notebook itself: reactive execution, plain-Python files, open source, local-first. Deepnote fixes the workflow around it: real-time co-editing, SQL against your warehouse, integrations, and publishable apps, all hosted. Solo or open-science work leans marimo; a data team that lives in shared documents leans Deepnote.
At a glance
| marimo | Deepnote | |
|---|---|---|
| What it is | An open-source reactive Python notebook: change a cell and everything downstream re-runs, so there is no stale hidden state | A collaborative cloud notebook: Jupyter-compatible, multiplayer, with SQL blocks and a deep integration catalog |
| AI | An in-editor assistant, plus `marimo pair` to attach an external coding agent such as Claude Code or Codex to a running notebook | Deepnote AI, including an autonomous Auto mode that writes and executes code and SQL blocks and self-corrects, alongside Generate mode, completions, and error fixing |
| Compute | Local by default. molab, its hosted workspace, is in public preview on CoreWeave with roughly 4 CPUs and 32 GB RAM, opt-in GPUs, and 12-hour sessions, free while capacity is not oversubscribed (checked August 2026) | Managed cloud machines by plan; hardware options vary by tier |
| File format | Pure .py files that diff, import, and test with pytest; `marimo convert` reads .ipynb and `marimo export ipynb` writes it | Jupyter-compatible notebooks in a hosted workspace |
| Collaboration | Share as an app, as slides, or as a public molab link; no live multiplayer editing | Real-time co-editing, comments, shared workspaces, and publishable data apps |
| Cost | Free; molab free during preview | Free tier; paid team plans per seat |
| License | Open source (Apache-2.0) | Proprietary |
The core difference
marimo's premise is that the notebook format is the bug. Cells form a dependency graph and re-run automatically, notebooks are plain .py files that diff and test like code, and the whole thing is Apache-2.0 licensed and runs on your machine. Its hosted workspace, molab, is in public preview and free while capacity allows: a convenience layer, not the product.
Deepnote's premise is that the workflow around the notebook is the bug. It is a hosted Jupyter-compatible workspace built for teams: several people editing the same notebook live, SQL blocks against a warehouse, a large catalog of native data connections, comments and review, and a publish step that turns an analysis into an app a stakeholder can use. Its AI is not a bolt-on either: Deepnote AI has an autonomous Auto mode that writes and executes code and SQL blocks and self-corrects.
The deciding question is usually who else touches the work. If the answer is a reviewer reading a diff, marimo's format is the advantage. If the answer is three analysts and a stakeholder in the same document, Deepnote's collaboration is worth more than reactivity.
Pick marimo if…
- You want your notebooks in Git as readable, testable Python.
- Reproducibility is the requirement: research, publication, or handover.
- You prefer open source and local-first, with no vendor in the loop.
- You want interactive UI elements without a separate app framework.
Pick Deepnote if…
- Several people edit the same analysis at the same time.
- SQL against a warehouse is a first-class part of the work.
- You need one-click connections to a long list of data sources.
- The deliverable is an app or dashboard for someone non-technical.
Where Clusy fits
That's usNeither is aimed at heavy model work: marimo leaves compute to you, and Deepnote's machines are sized for analytics. If the job is fine-tuning, training, or evaluation on real GPUs (and you would rather supervise an agent than type every cell) that is the gap Clusy fills (our product, so weigh accordingly).
Try Clusy freeFrequently asked questions
- Is marimo free and Deepnote paid?
- marimo is free and open source, and its molab cloud preview is free while capacity allows. Deepnote has a free tier and charges per seat for team plans. Long term the real cost difference is operational: with marimo you supply the compute and the setup.
- Can Deepnote do reactive execution like marimo?
- Not in the same way. Deepnote is Jupyter-compatible, so cells run when you run them and hidden state is possible. marimo's reactivity is structural: it is why the format is different in the first place.
- Which one works better with AI agents?
- For external coding agents, marimo's plain-Python files are easier to read and edit, and `marimo pair` is built for exactly that. Deepnote takes the other route and ships its own: Auto mode writes and runs code and SQL blocks without you driving it. Two shapes of the same idea: bring your own agent, or use the one included.
Sources
Every claim about marimo and Deepnote on this page comes from their own documentation, last checked . Quotas, hardware tiers, and pricing move; check the vendor before relying on a number. Spotted something out of date? Tell us and we will fix it.