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.
Last reviewed · 7 options compared
Why people look beyond Deepnote
Per-seat cost: collaborative pricing scales with headcount, which stings once stakeholders want accounts too.
GPU work: the platform is sized for analytics; fine-tuning and training belong somewhere else.
Reproducibility: hosted Jupyter-compatible notebooks still carry hidden-state and diff problems.
Data residency: some teams cannot put notebooks in a vendor's cloud at all.
Model choice: you get Deepnote's AI, not a choice of which frontier or open model runs your work, and no bring-your-own-key.
The alternatives
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Hex
Best like-for-like: collaborative notebooks with stronger publishing
The most direct competitor. Same core idea (hosted, multiplayer, SQL and Python in one document) with a stronger app-publishing layer and a broader agent stack aimed at warehouse analytics. Teams whose deliverable is a polished interactive report for stakeholders usually prefer it; teams who value .ipynb portability usually do not.
Warehouse-first SQL with Python for the last mile
Publishing layer that non-technical colleagues actually use
Best when the work is ML on GPUs rather than analytics
Deepnote's compute is sized for analysis; if you are fine-tuning, training, or benchmarking, that is a different product category. Clusy is an agent-native notebook: describe the outcome and an agent plans, writes, and runs the cells on managed compute from a free 8 vCPU / 8 GB sandbox up to H100 and H200 GPUs, with native branching to compare experiments. Honest caveat: we do not do real-time multiplayer editing, which may be the single feature you came to Deepnote for. Disclosure: our product.
Agent executes end to end: data, training, evaluation, reporting
GPU ladder to H100 / H200 with nothing to provision
Branch experiments and compare runs side by side
Databricks, Snowflake, and Hugging Face connections
The reason many teams go back: it costs nothing, runs on hardware you already have, and JupyterHub gives a team shared access with per-user environments. What you take on is operations: someone has to own images, upgrades, and the support requests when a kernel dies.
No per-seat cost, ever
JupyterHub for shared, governed team deployments
Total control over environments and data residency
If the recurring pain is notebooks that only run in one order and diffs nobody can review, marimo fixes it at the format level: reactive execution and plain-Python files. Open source and free, with a hosted preview (molab) offering opt-in GPUs while capacity allows as of mid-2026. It trades away multiplayer editing entirely.
Reactive execution — no stale hidden state
Notebooks as plain .py: reviewable, testable, runnable
Hosted Jupyter with a free GPU, Gemini assistance, and Drive-based sharing. Collaboration is file-level rather than live co-editing, and runtimes are ephemeral, but for individuals and small teams whose budget just disappeared, it covers a surprising amount of ground.
Best when governance and data scale are the requirement
If the reason to leave is that data has outgrown a hosted notebook (or that compliance wants catalog-level lineage and access control) the lakehouse is the destination. Notebooks sit on top of governed tables with Spark, jobs, and MLflow around them. It is a platform decision, with the procurement to match.
Collaborative cloud notebooks with JetBrains code intelligence, SQL cells, and reactive execution options, plus an on-premises deployment, which is the deciding factor for organizations that cannot put notebooks in a vendor's cloud.
Real-time collaboration and report publishing
On-premises deployment available
Strong Python assistance from the JetBrains toolchain
Entries are grouped by the reason teams leave (cost, compute, reproducibility, governance) not ranked overall.
Clusy is our product, disclosed with a chip, and it is a poor substitute for Deepnote's collaboration. The entry says so.
No competitor prices are quoted; per-seat plans and beta gating move faster than this page.
Competitor facts are taken from each vendor's own documentation and last checked ; the comparison matrix lists the source link behind every one. Found something out of date? Tell us.
When to stay with Deepnote
Honestly: switching tools has a cost, and sometimes the right answer is the one you already use.
Live multiplayer editing is the workflow, not a nice-to-have.
Your integration catalog needs are broad and already wired up.
The team is productive and the seat cost is smaller than the migration cost, which it usually is.
You publish data apps from notebooks and people rely on them.
Frequently asked questions
What is the closest alternative to Deepnote?
Hex, by some distance: the same collaborative SQL-and-Python notebook shape with a stronger publishing layer. The main trade is notebook portability: Deepnote stays close to .ipynb, Hex does not.
Is there a free alternative to Deepnote?
Several. Jupyter and JupyterHub are free and self-hosted; marimo is free and open source; Google Colab and Kaggle are free hosted notebooks with GPU access. All of them trade away some of Deepnote's collaboration polish.
Which Deepnote alternative is best for GPU work?
None of the collaboration-first tools, honestly. For free GPU hours, Kaggle and Colab. For persistent GPU workspaces, Lightning AI or your own box. For an agent running training on managed H100/H200 hardware, that is what we built Clusy for.