Deepnote alternatives
Deepnote solved a real problem: notebooks are miserable to collaborate on,
and a hosted, real-time, versioned environment with a warehouse connection
is a genuine upgrade over passing .ipynb files around.
The question worth asking before adopting any notebook platform is who the seats are for. If half of them are stakeholders who never run a cell, you are paying platform prices for a viewing experience.
Where a notebook platform is more than the job
- Readers don't need a kernel. Most people opening a notebook link want the chart and the conclusion. Handing them a code environment shows them imports, scratch cells and a stack trace from cell 4.
- Seats for viewers. Collaborative platforms price around editors and viewers. Sharing a weekly result with twenty people gets expensive fast.
- Migration cost for a sharing problem. Adopting a platform means moving your notebooks, your secrets and your compute — a large answer to "how do I send Priya the cohort chart."
- Publishing is still to a public URL or a platform account. Neither is ideal for a client or an auditor.
The shortlist
1. Comma — keep the notebook, publish the render
Nothing about where you write the notebook changes. Export and publish the output:
jupyter nbconvert --to html --embed-images cohorts.ipynb
curl -X POST https://commareports.com/api/v1/reports \
-H "Authorization: Bearer $COMMA_API_TOKEN" \
-H "Content-Type: application/json" \
-d "$(jq -n --arg h "$(cat cohorts.html)" \
'{title:"Cohort analysis", html:$h, visibility:"team"}')"
Use --no-input when the audience should see conclusions rather than code.
Plotly, Altair, Bokeh and ipywidgets output stays interactive; the report
renders in a sandboxed iframe with scripts allowed.
What the reader gets: a URL with real per-report visibility (private, team, email domain, registered, link), anchored comment threads on a specific chart or output cell, and revisions at one id — re-publish next week and the link you already sent shows the new numbers with the old ones diffable. Viewers and commenters are free and unlimited, which is the part that usually decides this.
Not for: running the notebook. Comma stores and serves the rendered artifact; execution stays wherever it is today.
Pricing: Free — unlimited reports, viewers, commenters and revisions.
Share a Jupyter notebook as HTML →
2. Google Colab — free execution, Drive-shaped sharing
If the need is a shared runtime rather than a shared document, Colab is free and everyone already has an account. Sharing follows Drive's model, and there is no comment layer on rendered output. See Comma with Colab.
3. Hex or Mode — if it is really a data platform question
Warehouse connection, scheduled runs, an app builder and BI semantics. Buy them for that, not to host a file. See Hex alternatives and Mode alternatives.
4. nbviewer or GitHub — free, public, static
nbviewer renders any public notebook URL and GitHub renders .ipynb
inline. Both are free, neither is private, neither keeps widgets alive.
See nbviewer alternatives.
At a glance
| Option | Reader needs a seat | Private | Interactive output | Comments on output |
|---|---|---|---|---|
| Comma | No | Yes | Yes | Yes, anchored |
| Deepnote | Usually | Yes | Yes | In the notebook |
| Colab | Google account | Via Drive | Yes | On the notebook |
| Hex / Mode | Yes | Yes | Yes | Varies |
| nbviewer | No | No | Limited | No |
Checked September 2026. Verify current plans before committing.
How to choose
- A team that co-edits notebooks daily on warehouse data? A notebook platform earns its keep.
- Occasional analysis, shared results? Keep your local or Colab notebook and publish the export.
- Stakeholders who only read? Never give them a kernel. Give them a page they can comment on.