Clusy 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.
At a glance
| Clusy | Kaggle Notebooks | |
|---|---|---|
| What it is | Agent-native private notebook workspace on managed compute | Free public notebooks attached to datasets, competitions, and a large community |
| AI | An agent that plans and executes the workflow; choose Auto, open models, Claude, or GPT | Light assistance; the leverage is thousands of public notebooks to fork |
| Compute | free 8 vCPU / 8 GB RAM CPU sandbox on the free plan, up to H100 / H200 GPUs (141 GB VRAM) on paid plans | Free weekly GPU/TPU quota (about 30 hours as of mid-2026) with ~12-hour sessions |
| Persistence | Projects persist; state, files, and history survive between sessions | Sessions end at the cap; outputs persist as notebook versions |
| Privacy | Private by default; publish to the Clusy Hub only if you choose to | Public-by-default culture; private notebooks supported but not the norm |
| Data | Uploads, Hugging Face, Databricks, and Snowflake connections | The largest public dataset catalog in the field, attached in one click |
| Cost | Free plan; flat monthly plans from $30 with usage allowances | Free |
The core difference
Kaggle's value is the ecosystem around the notebook. Datasets attach without uploading, competitions give the work a scoreboard, and the public notebook library means someone has usually already solved a nearby problem in code you can read and fork. The compute is a real free GPU allowance (roughly 30 hours a week as of mid-2026, with sessions capped near 12 hours) which is remarkable for free and finite by design.
Clusy's value is that the work happens without you. Describe the outcome and the agent plans, writes, and runs the cells on a managed sandbox, from a free CPU tier up to H100 and H200 GPUs, in a project that persists between sessions. Experiments branch so variants can be compared side by side, and data connections cover uploads, Hugging Face, Databricks, and Snowflake.
The honest split is confidentiality and duration. Kaggle is built for public, bounded work and is unbeatable at it. Private data, multi-hour runs, and results that need to still be there next month are the point at which a hosted private workspace stops being a luxury.
Choose Clusy if…
- The data or the result is confidential.
- Runs are long enough that a session cap would end them early.
- You want the experiment executed for you, not just hosted.
- You need more GPU than a weekly quota provides.
Choose Kaggle Notebooks if…
- You are learning, and reading other people's solutions is the point.
- You are competing: Kaggle is the home field.
- The datasets you need are already in Kaggle's catalog.
- Free matters more than persistence or privacy.
Frequently asked questions
- How much free GPU does Kaggle actually give you?
- Around 30 hours a week as of mid-2026, on a floating quota that can be more when capacity allows, with individual sessions capped near 12 hours and a weekly reset. It is the most generous no-strings free GPU offer in the field.
- Can I move a Kaggle notebook to Clusy?
- Yes. it is a standard notebook, so the code carries over. What changes is the data path: Kaggle's attached-dataset directories become uploads or a Hugging Face / warehouse connection.
- Does Clusy have a free tier that competes with Kaggle's?
- Not on GPU. Clusy's free plan includes the Auto model and an 8 vCPU / 8 GB CPU sandbox; GPUs start on paid plans. For free GPU hours specifically, Kaggle wins and we would rather say so.
Sources
Claims about Kaggle Notebooks come from its own documentation, last checked : Floating GPU quota announcement, Efficient GPU usage docs. Quotas, hardware tiers, and pricing move; check the vendor before relying on a number, and tell us if we have something wrong.
See the agent do the work.
Free plan, no credit card. Describe an ML task and watch it get planned, executed, and reported in a notebook you control.
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