Clusy vs Cursor
Cursor proved what an agent inside your editor can do, and a lot of people now want that feeling for data work. The catch is structural: Cursor is built around a repository on your machine, and notebooks are neither.
At a glance
| Clusy | Cursor | |
|---|---|---|
| Built for | Notebooks and the compute they need: analysis, training, evaluation | Codebases: multi-file features, refactors, tests, shipping software |
| Notebook editing | Native. The agent creates, edits, runs, and reads the output of cells | Historically not direct in agent mode; the usual routes are an MCP server or percent-format .py files |
| Execution | The agent runs the cells and reacts to the results | You run the code; the agent writes it |
| Compute | Managed and included: free 8 vCPU / 8 GB RAM CPU sandbox up to H100 / H200 GPUs (141 GB VRAM) | Your machine, or a remote box you set up and pay for yourself |
| Codebase awareness | Project-scoped: the notebook, its data, and its outputs | Repository-wide, and very good at it |
| Experiments | Branch a notebook and compare runs side by side | Git branches |
| Cost | Free plan; flat monthly plans from $30 with usage allowances | Free tier; paid individual and team plans |
The core difference
Cursor is an AI-first editor forked from VS Code, and its agent is genuinely excellent at what it targets: reading a whole repository, planning a change, and applying it across many files while you review the diff. You choose the frontier model, everything runs on your machine, and the output is code in Git.
Notebooks sit awkwardly in that model. An .ipynb is JSON with embedded outputs rather than source, and Cursor's agent mode has historically not edited notebook cells directly. The community workarounds are real and they work, but they are workarounds: an MCP server that exposes cell operations, or abandoning .ipynb for percent-format .py files. On top of that, the editor does not provision compute, so a fine-tune still needs a GPU you found yourself.
Clusy starts from the notebook and the compute instead. The agent writes and executes cells, watches what the outputs actually say, and iterates on managed sandboxes from a free CPU tier up to H100 and H200 GPUs. What we do not have is Cursor's command of a large codebase, and we are not trying to: if your work is an application rather than an analysis, Cursor is the better tool and we would say so.
Choose Clusy if…
- The deliverable is a notebook, a model, or an evaluation rather than an application.
- The work needs a GPU you would otherwise have to find and configure.
- You want the agent to run the cells and respond to what the outputs say.
- You compare experiment variants and want branching rather than copies.
Choose Cursor if…
- You are writing software: services, libraries, apps, tests.
- You need an agent that understands a large repository end to end.
- Your code must stay on your machine.
- You already have the compute you need.
Frequently asked questions
- Can Cursor edit Jupyter notebooks?
- It opens and renders them, but agent mode has historically not edited .ipynb cells directly, which is why the community built an MCP server for cell operations and why many people convert notebooks to percent-format .py files first. Check the current release before assuming either way; this is an area Cursor has been actively working on.
- Is Clusy a Cursor for data science?
- That is the shorthand people use, and it is roughly right about the feeling and wrong about the architecture. Cursor puts an agent in your local editor; Clusy puts one in a cloud notebook with compute attached, so it can run what it writes and read the result. If you want Cursor's exact experience on your own machine, use Cursor.
- Can I use both?
- Commonly, yes. Cursor for the library, the pipeline, and the production service; Clusy for the exploratory analysis and the training runs that need a GPU. They are not competing for the same hour of your day.
- Which models can each use?
- Both let you choose. Cursor exposes frontier models for coding. Clusy offers Auto free on every plan, open models such as DeepSeek and Kimi, and Claude and GPT on higher tiers, plus bring-your-own-key.
Sources
Claims about Cursor come from its own documentation, last checked : Cursor docs, Notebook support thread, cursor-notebook-mcp. 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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