Clusy 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.
At a glance
| Clusy | Deepnote | |
|---|---|---|
| What it is | Agent-native notebook: describe the outcome, the agent executes it | Collaborative cloud notebook (Jupyter-compatible) with AI assistance |
| AI | The core interaction — the agent runs workflows end to end; choose your model, or bring your own keys | Deepnote AI: completions, Generate mode, and an autonomous Auto mode that writes and runs code and SQL blocks |
| Compute | Explicit ladder: free 8 vCPU / 8 GB RAM CPU sandbox up to H100 / H200 GPUs (141 GB VRAM) | Cloud machines by plan; hardware options vary by tier |
| Collaboration | Share projects and publish notebooks to the Clusy Hub | Real-time multiplayer editing, comments, and shared workspaces |
| Experiments | Native branching with side-by-side comparison | Version history; variants live as copies |
| Data connections | Uploads, Hugging Face, Databricks and Snowflake | Broad catalog of native integrations (50+), SQL blocks against warehouses |
| Cost | Free plan; flat monthly plans from $30 with usage allowances | Free tier; paid team plans per seat |
The core difference
Deepnote is a collaborative data notebook first: a Jupyter-compatible workspace with real-time multiplayer editing, SQL and Python blocks, dozens of native data integrations, and publishable data apps. Its AI is capable, including an autonomous Auto mode that writes and executes code and SQL blocks and self-corrects. The product's soul is still team analytics collaboration, and the AI serves that.
Clusy was designed agent-native from the first commit: the primary way you work is describing an outcome, and the agent plans, writes, and executes the notebook — sourcing data, choosing compute, running training, reporting results. Experiment branching is native, and the compute ladder is explicit: a free CPU sandbox up to H100 / H200 GPUs on flat monthly plans.
If you want a shared notebook your whole team edits together — analysts, engineers, stakeholders — Deepnote's collaboration is the draw. If you want ML work done for you on real hardware while you review and steer, that's Clusy.
Choose Clusy if…
- You want the AI to do the work end to end, not assist while you drive.
- Your workloads need real GPUs — fine-tuning, training, evaluation at H100/H200 scale.
- You iterate through experiment variants and want branch-and-compare built in.
- You want to pick the agent's brain: Auto, DeepSeek, Kimi, Claude, or GPT per task.
Choose Deepnote if…
- Live multiplayer editing with your team is the feature you care about most.
- Your work is analytics on warehouse data with SQL as a first-class language.
- You publish interactive data apps to colleagues.
- You want the broadest catalog of one-click data integrations.
Frequently asked questions
- Both have AI agents — what's actually different?
- Less than the marketing on either side suggests, and the real differences are around the agent rather than in it. Both plan, write, and execute cells autonomously. Deepnote's is pointed at analytics, with warehouse SQL as a first-class output. Clusy's is pointed at ML workloads: it selects compute from a ladder that reaches H100 and H200, you choose which frontier or open model drives it (or bring your own key), and variants become branches you compare side by side rather than copies.
- Does Clusy support real-time collaboration like Deepnote?
- Not multiplayer editing. Clusy projects can be shared, and notebooks can be published to the Clusy Hub where anyone can view and fork them — but simultaneous co-editing is Deepnote's strength, not ours.
- Which is better for fine-tuning and model training?
- Clusy — that's the core use case. The agent runs LoRA fine-tunes, paper reproductions, and benchmark evaluations on managed GPUs up to H200 (141 GB VRAM), with branching to compare configurations.
Sources
Claims about Deepnote come from its own documentation, last checked : Deepnote AI analysis docs, Deepnote AI overview. 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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