Compare
How Clusy compares.
Honest, side-by-side looks at the tools people weigh against Clusy, including the cases where the other tool wins.
Every notebook tool, on one screen
16 notebooks and editors on the five questions that actually decide the choice. Facts come from each vendor's own documentation, last checked .
| Tool | Runs | GPU | AI | Collaboration | Licence |
|---|---|---|---|---|---|
| ClusyOurs | Cloud | Free CPU, up to H200 | Researches, then runs it | Share and fork | Proprietary |
| Jupyter Notebook | Local | Yours | Via extension | JupyterHub | BSD-3 |
| JupyterLab | Local | Yours | Via extension | Add-on / Hub | BSD-3 |
| marimo | Local or cloud | molab preview | Bring your agent | Share as app | Apache-2.0 |
| Google Colab | Cloud | Free T4, paid to A100 | Gemini | Share a link | Proprietary |
| Kaggle Notebooks | Cloud | ~30 h/week free | Minimal | Fork and comment | Proprietary |
| Deepnote | Cloud | By plan | Autonomous agent | Live multiplayer | Proprietary |
| Hex | Cloud | Not a focus | Agent stack | Live multiplayer | Proprietary |
| Databricks Notebooks | Cloud platform | You configure | Assistant | Governed co-editing | Proprietary |
| Amazon SageMaker Studio | Cloud platform | Any instance | Amazon Q | Shared spaces | Proprietary |
| VS Code + Jupyter | Local or remote | Yours | Copilot | Live Share | MIT core |
| Streamlit | Self-deployed | Your host | None | Ship an app | Apache-2.0 |
| Lightning AI Studios | Cloud | T4 to H200 | Assistance | Share a studio | Proprietary |
| JetBrains Datalore | Cloud or on-prem | Connect your own | JetBrains assist | Live multiplayer | Proprietary |
| CoCalc | Cloud or on-prem | Limited | Assistance | Live multiplayer | Source-available |
| Cursor | Local | Yours | Repo-wide agent | Git | Proprietary |
Research assistants, which do a different job
These come up in the same searches. They read and synthesize what has already been written, and they stop there. Clusy's agent also researches with citations, but it treats that as the first half and then runs the code. For a formal systematic review, or for a broad survey of an unfamiliar field, the tools below go deeper than we do.
| Tool | Runs | GPU | AI | Collaboration | Licence |
|---|---|---|---|---|---|
| ChatGPT data analysis | Cloud | None | It is the AI | Share a chat | Proprietary |
| NotebookLM | Cloud | None | Grounded Q&A | Share a notebook | Proprietary |
| Perplexity | Cloud | None | Search and synthesis | Share a thread | Proprietary |
| Elicit | Cloud | None | Literature review | Shared reviews | Proprietary |
| OpenAI deep research | Cloud | None | Agentic web research | Share a chat | Proprietary |
Head to head
Clusy vs Jupyter Notebook
Jupyter is the notebook standard: open source, local, endlessly extensible — and entirely manual. Clusy keeps the notebook you know but puts an AI agent in the driver's seat: describe the outcome, and it plans, writes, and executes cells on managed cloud compute while you stay in control of every line.
Read the comparisonClusy vs Google Colab
Colab made cloud notebooks and free GPUs accessible to everyone — and it's still where much of the world learns ML. Clusy targets the step after that: an agent that does the work end to end, on persistent projects, with compute that scales from a free sandbox to H200s.
Read the comparisonClusy vs Hex
These two are often shortlisted together but aim at different jobs. Hex is a polished analytics workspace where data teams build SQL-and-Python notebooks and publish them as apps for stakeholders. Clusy is an agent-native notebook where ML work — training, fine-tuning, evaluation — gets done end to end on real GPUs.
Read the comparisonClusy vs Deepnote
Deepnote modernized the cloud notebook: real-time collaboration, SQL blocks, a deep integration catalog, and an AI mode that writes and runs blocks on its own. Both of us put an agent in the notebook, so the honest comparison is about everything around it: what hardware it can reach, which model drives it, and what happens when you want to try five variants.
Read the comparisonClusy 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.
Read the comparisonClusy vs Kaggle Notebooks
Kaggle is the best free classroom in machine learning: real GPUs, real datasets, and hundreds of thousands of notebooks to learn from. Clusy is what the work looks like after the classroom: private projects, an agent doing the execution, and hardware sized for the run rather than the quota.
Read the comparisonClusy vs Databricks
Databricks is a platform decision: governed data, Spark at scale, and notebooks as the interface to it. Clusy is a workbench decision: an agent that runs ML experiments on managed GPUs, connected to the governed data you already have. These are complements more often than they are alternatives.
Read the comparisonClusy vs ChatGPT data analysis
Uploading a CSV to a chat assistant and getting a chart back is genuinely the fastest path to an answer: right up to the point where the answer needs to be reproducible, large, private, or trained on a GPU. Clusy keeps the plain-language interface and puts a real notebook and real compute underneath it.
Read the comparisonClusy 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.
Read the comparisonClusy vs NotebookLM
These two get compared because they share a word. NotebookLM is a reading tool: it grounds answers in documents you give it. Clusy is an execution tool: it writes and runs code against data. Almost nobody needs both for the same task.
Read the comparisonClusy vs Perplexity
Perplexity is the fastest way to find what is already known and see where it came from. Clusy is for the part after that, when the answer does not exist yet and something has to be computed to produce it.
Read the comparisonClusy vs Elicit
Both call themselves research tools and both are, for opposite halves of research. Elicit works over the published record. Clusy works over your data and your compute.
Read the comparisonClusy vs OpenAI deep research
Both are agents that work autonomously for minutes at a time and hand back something substantial. What they do in those minutes could not be more different: one reads the internet, the other runs your code.
Read the comparisonOther tools, compared
Comparisons we are not part of. If the right answer to your question is a tool that isn't ours, these pages say so.
Alternatives guides
The best Cursor alternatives in 2026
Cursor set the bar for agentic coding, and most people looking past it are not unhappy with the agent. They want a different price, a different licence, a different editor, or they have realized their work is notebooks and data rather than a codebase.
Read the guideThe best Jupyter Notebook alternatives in 2026
Jupyter is still the lingua franca of data science — and still entirely manual: your machine, your environments, your every keystroke. A new generation of notebooks adds what Jupyter leaves out: AI that does real work, managed GPUs, collaboration, and reproducibility. Here's an honest map of the options, including when to stay put.
Read the guideThe best Google Colab alternatives in 2026
Colab is the world's on-ramp to ML — free GPUs, zero setup, a share link. People go looking for alternatives when the training run outgrows the session: runtimes recycle mid-epoch, compute units drain, and projects need to persist. Here's an honest map of where to go next.
Read the guideThe best marimo alternatives in 2026
marimo is the most convincing rethink of the notebook in a decade: reactive execution, plain-Python files, open source. People still go looking for something else: usually because they need a language it does not speak, a team workflow it does not have, or hardware it does not provide. Here is where they end up.
Read the guideThe best Deepnote alternatives in 2026
Deepnote is a genuinely good collaborative notebook, and most teams that look elsewhere are not unhappy with the editing experience. They are hitting a different wall: per-seat cost as the team grows, hardware that was never meant for training, or a need for notebooks that live in Git. Here is the honest map.
Read the guideThe best Hex alternatives in 2026
Hex is one of the best analytics workspaces built: warehouse-first SQL, Python for the last mile, and a publishing layer that stakeholders genuinely use. Teams look elsewhere when the deliverable stops being a report: when it becomes a portable notebook, a trained model, or a line item somebody wants smaller.
Read the guideThe best Kaggle Notebooks alternatives in 2026
Kaggle gives away more free GPU than anyone else and surrounds it with the largest library of public datasets and forkable notebooks in the field. The reasons to leave are narrow and specific: the weekly quota, the session cap, and the fact that it is a public-first platform for work that is often private.
Read the guide