Clusy vs Hex
These two are often shortlisted together but aim at different jobs. Hex is a polished analytics workspace where data teams build SQL-and-Python notebooks and publish them as apps for stakeholders. Clusy is an agent-native notebook where ML work — training, fine-tuning, evaluation — gets done end to end on real GPUs.
At a glance
| Clusy | Hex | |
|---|---|---|
| Built for | ML researchers and data scientists running experiments | Data and analytics teams answering business questions |
| Center of gravity | The experiment: train, fine-tune, evaluate, branch, compare | The warehouse: SQL + Python analyses published as apps |
| AI | Agent executes whole workflows end to end; you pick the model | Notebook Agent and Magic AI assist with queries, code, and analyses |
| Compute | Managed GPU sandboxes included, free 8 vCPU / 8 GB RAM CPU sandbox up to H100 / H200 | Warehouse pushdown plus hosted Python — GPUs aren't the focus |
| Experiments | Native notebook branching with side-by-side comparison | Version history and review flows oriented at collaboration |
| Output | Notebooks, trained models, evaluations, shareable results | Interactive data apps, dashboards, and threads for stakeholders |
| Cost | Free plan; flat monthly plans from $30 with usage allowances | Free tier for individuals; team and enterprise plans priced per seat |
The core difference
Hex is warehouse-first. Its center of gravity is the modern data stack: SQL cells against Snowflake or BigQuery, Python for the last mile, a deep agent stack (the Notebook Agent writes and edits analysis with warehouse-schema context, Magic works inline, Threads answers stakeholder questions), and a publishing layer that turns notebooks into interactive apps and dashboards for non-technical stakeholders. It's built for analytics teams shipping answers to the business.
Clusy is experiment-first. The agent takes a goal — reproduce a paper, fine-tune a model, benchmark five approaches — plans the work, writes and runs the cells, and hands you a notebook you can branch to compare variants. Compute is part of the product: managed sandboxes from a free CPU tier up to H100 / H200 GPUs, which is the class of hardware model training actually needs.
The honest split: if your output is a dashboard or an app answering a business question, Hex is the stronger tool. If your output is a trained model, an evaluation table, or a research result, that's the work Clusy is built for.
Choose Clusy if…
- Your work is model training, fine-tuning, or research — you need GPUs, not dashboards.
- You want an agent that runs the full workflow, not an assistant that drafts cells.
- You compare many experiment variants and want branching built into the notebook.
- You're an individual or small team — a $0 start beats a per-seat platform.
Choose Hex if…
- Your team lives in a data warehouse and ships analyses to business stakeholders.
- You need polished, interactive data apps and dashboards as the deliverable.
- SQL is the primary language of your work, with Python second.
- You need mature enterprise collaboration: reviews, permissions, endorsed datasets.
Frequently asked questions
- Is Clusy a BI or analytics tool like Hex?
- No. Clusy can absolutely do exploratory analysis, but it doesn't publish dashboards or apps for stakeholders. Its job is ML and data science work — training, fine-tuning, evaluation, experimentation — done by an agent in a notebook you control.
- Can Clusy connect to my data warehouse?
- Yes — Clusy has direct Databricks and Snowflake connections, alongside file uploads and public sources like Hugging Face.
- Do Hex and Clusy overlap at all?
- At the edges. Both are cloud notebooks with AI in them. But Hex optimizes for analytics collaboration and publishing, while Clusy optimizes for agent-executed ML work on serious GPUs. Some teams use both: Hex for reporting, Clusy for modeling.
Sources
Claims about Hex come from its own documentation, last checked : Notebook Agent docs, AI in Hex. Quotas, hardware tiers, and pricing move; check the vendor before relying on a number, and tell us if we have something wrong.
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.
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