Clusy vs marimo
Both start from the same complaint (the notebook, as inherited, is a bad research artifact) and fix opposite halves of it. marimo rebuilds the format so notebooks are reproducible and diffable. Clusy rebuilds the workflow so an agent writes and runs the notebook on compute you did not have to provision.
At a glance
| Clusy | marimo | |
|---|---|---|
| What it is | Agent-native cloud notebook: describe the outcome, the agent plans and executes it | Open-source reactive Python notebook you run yourself |
| AI | Built in and in the driver's seat: pick Auto (free), open models like DeepSeek and Kimi, or Claude / GPT | In-editor assistant, plus `marimo pair` to attach an external agent; you bring the keys |
| Compute | Managed and included: free 8 vCPU / 8 GB RAM CPU sandbox up to H100 / H200 GPUs (141 GB VRAM) | Local by default; molab preview offers ~4 vCPU / 32 GB sessions with opt-in GPUs, free while capacity allows |
| Execution model | You or the agent run cells; results stream into a persistent project | Reactive; changing a cell re-runs everything that depends on it |
| File format | Notebook in a hosted project, shareable and forkable via the Clusy Hub | Plain .py; diffable in Git, importable, testable with pytest, runnable as a script |
| Experiments | Native branching with side-by-side comparison | Git branches, like any Python file |
| License | Proprietary; free plan, paid plans from $30/mo | Open source (Apache-2.0), free |
The core difference
marimo's insight is structural. It parses the dependency graph between cells, so changing one cell re-runs everything downstream and there is no stale hidden state to explain. Notebooks are saved as ordinary Python, which means a readable `git diff`, importable modules, pytest coverage, and `python notebook.py` as a script. It is Apache-2.0 licensed, runs locally, and its hosted preview (molab) offers roughly 4 vCPU / 32 GB sessions with opt-in GPUs, free while capacity allows as of mid-2026. Its AI story is bring-your-own: an in-editor assistant, plus `marimo pair` to attach an external coding agent to a live notebook.
Clusy's insight is about who is doing the work. You describe the outcome ("fine-tune this model on my dataset and report evaluation metrics") and the agent plans the approach, sources the data, writes and executes the cells, picks the compute, and reports back. The compute ladder is part of the product: a free 8 vCPU / 8 GB RAM CPU sandbox up to H100 and H200 GPUs (141 GB VRAM), with no drivers to install. Experiments branch natively, so five variants are five branches to compare rather than five copies of a file.
Read honestly, marimo wins on openness, cost, and reproducibility guarantees; we do not have reactive execution and we are not open source. Clusy wins on managed hardware and on doing the work rather than hosting it. If you want both properties, the practical answer today is to use marimo for artifacts that must be reviewed as code, and an agent-native notebook for the experiments that need GPUs.
Choose Clusy if…
- You want the ML work done, not just a better place to type it.
- The job needs real GPUs and you would rather not provision any.
- You iterate over experiment variants and want branch-and-compare built in.
- You want to choose the agent's model per task, or bring your own key.
Choose marimo if…
- Reproducibility is the requirement: the notebook is a research artifact.
- Your notebooks must live in Git as reviewable, testable Python.
- You want open source you can run locally, forever, for free.
- You want interactive UI elements without a separate app framework.
Frequently asked questions
- Is Clusy open source like marimo?
- No. marimo is Apache-2.0 and runs entirely on your machine; Clusy is a hosted product with a free plan. If self-hosting or auditing the source is a requirement, marimo is the honest recommendation.
- Does Clusy have reactive execution?
- Not in marimo's sense. Clusy tracks cell dependencies to flag stale results and the agent re-runs what its plan requires, but it does not enforce a reactive dependency graph over the whole notebook the way marimo does by design.
- marimo's molab has free GPUs now; why pay for Clusy?
- For the work, not the hardware. molab's GPU preview is a genuinely good free offer, and if you want to write the notebook yourself it may be all you need. Clusy is for the case where you want an agent to plan, run, and evaluate the experiment for you, on hardware up to H100/H200 with capacity you are not sharing with a preview queue.
- Can I use both?
- Yes, and some people should. Use marimo where the notebook is the deliverable and has to survive review; use Clusy where the result is the deliverable and the path there involves a lot of GPU hours.
Sources
Claims about marimo come from its own documentation, last checked : molab, now with GPUs, molab guide, Export formats. Quotas, hardware tiers, and pricing move; check the vendor before relying on a number, and tell us if we have something wrong.
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