Hex vs Deepnote
Both are hosted, multiplayer, SQL-plus-Python notebooks that publish to stakeholders. Hex leans further toward the analytics org: warehouse-first, a strong app-publishing layer, and AI aimed at query and analysis drafting. Deepnote leans further toward the notebook: Jupyter compatibility, a broad integration catalog, and an environment that ML-adjacent Python people find familiar. Pick Hex if the deliverable is a polished data app; pick Deepnote if it is a notebook others can run.
At a glance
| Hex | Deepnote | |
|---|---|---|
| What it is | A warehouse-first analytics workspace where SQL and Python cells compose into interactive apps for stakeholders | A collaborative cloud notebook: Jupyter-compatible, multiplayer, with SQL blocks and a deep integration catalog |
| AI | A deep agent stack: the Notebook Agent generates and edits logic with warehouse-schema context, plus Hex Magic inline, Threads for stakeholder questions, and agents for generative apps and semantic modeling | Deepnote AI, including an autonomous Auto mode that writes and executes code and SQL blocks and self-corrects, alongside Generate mode, completions, and error fixing |
| Compute | Managed compute sized for analytics rather than model training | Managed cloud machines by plan; hardware options vary by tier |
| File format | Hex projects with a DAG-style execution graph; exportable, but not .ipynb-native | Jupyter-compatible notebooks in a hosted workspace |
| Collaboration | Built for it: multiplayer editing, review, and published app endpoints | Real-time co-editing, comments, shared workspaces, and publishable data apps |
| Cost | Free tier for individuals; paid team and enterprise plans | Free tier; paid team plans per seat |
| License | Proprietary | Proprietary |
The core difference
Hex is built around the modern data stack. The unit of work is a project whose cells form an explicit graph, SQL against Snowflake or BigQuery is first-class, and the publishing layer is genuinely good: an analysis becomes an interactive app with inputs and controls that non-technical colleagues actually use. Its agent stack is deep: the Notebook Agent writes and edits analysis with warehouse-schema context, and further agents build apps and semantic models.
Deepnote starts from Jupyter compatibility and adds the cloud. Notebooks import and export, the integration catalog is broad, real-time co-editing works the way people expect from Google Docs, and Deepnote AI's Auto mode writes and runs code and SQL blocks on request. Work that started on someone's laptop lands here with less friction.
In practice the choice tracks your org chart. An analytics team publishing to business stakeholders gets more from Hex. A mixed team of analysts and Python engineers who still care about .ipynb portability gets more from Deepnote.
Pick Hex if…
- SQL against a warehouse is the centre of the work.
- The output is an interactive app for people who will never open a notebook.
- You want AI that understands your warehouse schema.
- Analytics governance and review workflows matter.
Pick Deepnote if…
- Notebook portability matters: things arrive and leave as .ipynb.
- Your team is more Python engineering than SQL analytics.
- You want the widest catalog of one-click data connections.
- You want an in-product notebook agent on a familiar Jupyter surface.
Where Clusy fits
That's usBoth optimize for analytics collaboration, and neither is built for model training. If the work is fine-tuning, evaluation, or reproducing an experiment on H100-class hardware, that is a different product category: ours included. Some teams run both: an analytics workspace for reporting, an agent-native notebook for modeling.
Try Clusy freeFrequently asked questions
- Is Hex a notebook or a BI tool?
- Both, deliberately. You develop like a notebook (SQL and Python cells, iterating) and ship like BI, publishing an interactive app with controls. That duality is the product.
- Which is better for a team new to cloud notebooks?
- Deepnote, usually, because it feels like the Jupyter people already know and imports existing work. Hex asks for a bit more adjustment and pays it back in the publishing layer.
- Do either of them run GPU workloads?
- Not as a strength. Both size compute for analytics. For training and fine-tuning, use a platform whose compute ladder is the point.
Sources
Every claim about Hex and Deepnote on this page comes from their own documentation, last checked . Quotas, hardware tiers, and pricing move; check the vendor before relying on a number. Spotted something out of date? Tell us and we will fix it.