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Clusy vs Deepnote

Deepnote modernized the cloud notebook: real-time collaboration, SQL blocks, integrations, and — more recently — an AI agent in beta. Clusy starts from the other end: the agent is the product, and the notebook is where you supervise it, with managed compute up to H200 GPUs included.

At a glance

ClusyDeepnote
What it isAgent-native notebook: describe the outcome, the agent executes itCollaborative cloud notebook (Jupyter-compatible) with AI assistance
AIThe core interaction — the agent runs workflows end to end; choose your model, or bring your own keysAI completions plus a notebook agent (beta, on paid plans as of mid-2026)
ComputeExplicit 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
CollaborationShare projects and publish notebooks to the Clusy HubReal-time multiplayer editing, comments, and shared workspaces
ExperimentsNative branching with side-by-side comparisonVersion history; variants live as copies
Data connectionsUploads, Hugging Face, Databricks and SnowflakeBroad catalog of native integrations (50+), SQL blocks against warehouses
CostFree plan; flat monthly plans from $30 with usage allowancesFree 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 story has grown from completions into a notebook agent (in beta on paid plans, as of mid-2026) that can plan and edit blocks on request. The product's soul is team analytics collaboration.

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?
Depth of the loop. Deepnote's agent (beta as of mid-2026) plans and edits blocks when asked, inside a notebook you drive. Clusy's agent is the driver: it plans multi-step workflows, executes them on compute it selects — including GPUs — evaluates results, and reports back, while you inspect and steer. Clusy also lets you choose which frontier or open model powers the agent.
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.

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

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