Clusy 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.
At a glance
| Clusy | Google Colab | |
|---|---|---|
| What it is | Agent-native notebook: the agent plans, codes, and runs your ML work end to end | Hosted Jupyter notebook with free (limited) compute and Gemini code assistance |
| AI | Agent executes whole workflows; choose Auto (free), open models, or Claude / GPT | Gemini-based code completion and assistance inside cells |
| Compute | Included managed sandboxes: free 8 vCPU / 8 GB RAM CPU sandbox up to H100 / H200 (141 GB VRAM) | Free limited GPUs; paid tiers draw compute units for T4 up to A100-class hardware |
| Sessions | Persistent projects — state and outputs survive between visits | Ephemeral runtimes that idle out and recycle; state must be saved to Drive |
| Experiments | Branch notebooks and compare experiments side by side | One notebook per file in Drive; manual copies for variants |
| Data connections | Uploads, Hugging Face, Databricks and Snowflake connectors | Google Drive, BigQuery, and anything you script |
| Pricing model | Free plan; flat monthly plans ($30–$200) with usage allowances | Free tier; paid subscriptions plus pay-as-you-go compute units |
The core difference
Google Colab is a hosted Jupyter environment: you write the code, and Google lends you compute — free with usage limits, or via paid plans that draw down compute units for T4-to-A100 class GPUs. Gemini-powered assistance can complete code and, more recently, generate analysis for you, but the working model is still you driving a single notebook tied to a runtime that can recycle out from under you.
Clusy is built around the agent instead of the runtime. You describe the goal; the agent plans the work, writes and executes the cells, and reports results into a persistent project — no runtime roulette, no re-mounting Drive, no losing state when a session recycles. When an experiment forks, you branch the notebook and compare runs side by side instead of juggling copies.
On compute, Colab's free tier is unbeatable for learning. Clusy's free tier is a CPU sandbox; its paid plans are flat monthly prices with usage allowances that reach dedicated L40S, A100, H100, and H200 GPUs — sized for training and fine-tuning runs rather than bursty sessions.
Choose Clusy if…
- You want an agent to run the whole workflow — data prep, training, evaluation — not just autocomplete.
- Your runs are long: training and fine-tuning jobs that shouldn't die with a recycled runtime.
- You need bigger hardware than Colab offers — H100 / H200 with 141 GB VRAM.
- You want flat, predictable monthly pricing instead of watching compute units drain.
Choose Google Colab if…
- You're learning, teaching, or following tutorials — Colab's free GPUs and shareable notebooks are perfect for it.
- Your work lives in the Google ecosystem (Drive, BigQuery, Sheets).
- You need a quick throwaway GPU session, not a persistent project.
- Zero budget is a hard constraint and the free-tier limits don't hurt you.
Frequently asked questions
- Is Clusy free like Google Colab?
- Clusy has a real free plan — the Auto model plus an 8 vCPU / 8 GB RAM cloud sandbox, no credit card — but free GPUs are Colab's territory. Clusy's GPUs (T4 through H200) come with paid plans starting at $30/month.
- Do Clusy sessions disconnect like Colab runtimes?
- Projects in Clusy are persistent: notebooks, outputs, and files stay put between visits, and long runs are executed by the agent on managed sandboxes rather than depending on your browser tab staying open.
- Which GPUs does Clusy offer?
- Entry GPUs (T4, L4, A10) on Plus, L40S and A100 on Pro, and H100 / H200 with up to 141 GB VRAM on Max — all as managed sandboxes the agent can select for the job.
- Can I bring notebooks from Colab to Clusy?
- The fastest path is to start a Clusy project, attach your data, and let the agent rebuild the workflow — describe what the notebook does and paste the key code. The agent reproduces and typically cleans up the pipeline as it goes.
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.
Try Clusy free