Count score factors
Editorial quality78
Practical utility82
Trust & transparency85
Freshness88
Engagement quality0
Momentum50
Decision guide · Reviewed Sep 8, 2026 · AI Data & Analytics, AI Data Notebooks
Count
73.5
Deepnote
77.2Choose Count when you need data teams and stakeholders collaborating on ad-hoc queries, exploratory data analysis, and visual presentations on top of cloud warehouses.. Choose Deepnote when you need data teams seeking a multiplayer cloud notebook that unifies SQL, Python, interactive data apps, and AI assistance..
At a glance
The practical differences
| Decision factor | Count | Deepnote |
|---|---|---|
| Best for | data teams and stakeholders collaborating on ad-hoc queries, exploratory data analysis, and visual presentations on top of cloud warehouses. | data teams seeking a multiplayer cloud notebook that unifies SQL, Python, interactive data apps, and AI assistance. |
| Not ideal for | organizations looking only for conventional static pixel-perfect reporting or those lacking structured database or spreadsheet connections. | developers wanting an offline-only local notebook or organizations strictly avoiding cloud-hosted compute infrastructure. |
| Pricing | At the review date (September 2026), Count offers a Free tier with 3 editor seats, paid tiers at $49 and $69 per editor per month, and custom Enterprise agreements. Viewer and collaborator seats are free across all tiers. Verify current details on the official pricing page. | As of September 2026, Deepnote provides a free entry tier alongside paid Pro, Team, and Enterprise plans, as well as an Education plan. Official pages indicate Deepnote AI features are restricted to Pro, Team, and Enterprise workspaces. Readers should check the official pricing page for current seat rates and compute limits. |
| Key difference | Unlike traditional BI dashboards or standalone notebooks, Count organizes analysis on an infinite multiplayer canvas with integrated DuckDB compute and free viewer seats. | Unlike standard local Jupyter environments, Deepnote combines native multiplayer collaboration and first-class SQL blocks with reactive data apps and headless agent integration via MCP, allowing AI coding tools to run and publish notebooks programmatically. |
| Overall rank | #273 | #97 |
| Review verdict | Count is well suited for modern data and product teams looking to eliminate back-and-forth communication through visual, interactive SQL and Python canvases. Teams seeking a rigid, traditional dashboarding tool or lacking warehouse data infrastructure may find the freeform canvas model excessive. | Deepnote is well suited for modern analytics teams that need seamless collaboration between SQL analysts, Python data scientists, and non-technical stakeholders viewing interactive data apps. It is less suitable for teams requiring purely local, air-gapped development without cloud dependencies or those seeking standalone business intelligence semantic modeling without notebook workflows. |
Deepnote score factors
Editorial quality82
Practical utility90
Trust & transparency85
Freshness92
Engagement quality0
Momentum50
Count strengths
- Collaborator and viewer seats are included across all pricing plans without additional cost.
- Combines code-based (SQL/Python) and no-code approaches on an unconstrained whiteboard canvas.
- Employs DuckDB locally in the compute layer to minimize recurring warehouse query fees.
- Certified for SOC 2 and compliant with GDPR, with HIPAA compliance available on Enterprise plans.
Deepnote strengths
- Native support for mixing SQL and Python in the same notebook without boilerplate connection code.
- Fast conversion of computational notebooks into polished, reactive data apps with hidden code.
- Broad connectivity with over 100 integrations covering cloud warehouses, databases, and file stores.
- Multi-provider AI model selection spanning Anthropic and OpenAI models.
- Real-time Google Docs-style collaboration and comment threads on individual blocks.
Frequently asked
Count vs Deepnote
Should I choose Count or Deepnote?+
Choose Count when you need data teams and stakeholders collaborating on ad-hoc queries, exploratory data analysis, and visual presentations on top of cloud warehouses.. Choose Deepnote when you need data teams seeking a multiplayer cloud notebook that unifies SQL, Python, interactive data apps, and AI assistance.. ToolsRank scores Count 73.5 and Deepnote 77.2; the gap reflects editorial quality, utility, trust, and freshness, not popularity or payment.
Is Count cheaper than Deepnote?+
Count: At the review date (September 2026), Count offers a Free tier with 3 editor seats, paid tiers at $49 and $69 per editor per month, and custom Enterprise agreements. Viewer and collaborator seats are free across all tiers. Verify current details on the official pricing page. Deepnote: As of September 2026, Deepnote provides a free entry tier alongside paid Pro, Team, and Enterprise plans, as well as an Education plan. Official pages indicate Deepnote AI features are restricted to Pro, Team, and Enterprise workspaces. Readers should check the official pricing page for current seat rates and compute limits. Compare the plan you would actually use and verify current prices on each vendor's pricing page before purchasing.
Which is better for ai data & analytics?+
Deepnote currently scores higher for ai data & analytics work. Count remains the stronger pick when your priority is data teams and stakeholders collaborating on ad-hoc queries, exploratory data analysis, and visual presentations on top of cloud warehouses.. Avoid Deepnote if you are developers wanting an offline-only local notebook or organizations strictly avoiding cloud-hosted compute infrastructure..
Can I use Count and Deepnote together?+
Yes. Count stands out for unlike traditional BI dashboards or standalone notebooks, Count organizes analysis on an infinite multiplayer canvas with integrated DuckDB compute and free viewer seats. Deepnote stands out for unlike standard local Jupyter environments, Deepnote combines native multiplayer collaboration and first-class SQL blocks with reactive data apps and headless agent integration via MCP, allowing AI coding tools to run and publish notebooks programmatically. Pairing them makes sense when one workflow needs both strengths; otherwise pick the tool that matches your primary job.