Deepnote
by Deepnote · Collaborative Python and SQL notebooks with dashboards, apps and scheduling
BenchRank score
Screenshots of Deepnote
Homepage
Overview
Deepnote is a cloud notebook where teams query data with Python and SQL, chart the results, and publish them as dashboards, data apps or scheduled jobs. Notebooks connect to warehouses and databases through built-in integrations, run on hosted CPU or GPU machines, and can be deployed as APIs. Editing is real-time, with commenting and revision history.
- Best for
- Data teams wanting collaborative Python and SQL notebooks that publish as dashboards and apps
- Pricing
- Free tier, then Team at $39 per editor/month billed yearly, with Enterprise on custom pricing.
- Runs on
- Web
Strengths and trade-offs
Strengths
- SQL, Python and chart blocks in one collaborative notebook
- Publish notebooks as apps, dashboards, scheduled jobs or APIs
- Built-in integrations for major warehouses and databases
- Real-time collaboration with commenting and revision history
Trade-offs
- Free plan caps you at 3 editors, 5 projects and 10 AI completions a month
- Team includes fixed AI, CPU and GPU credit allowances; heavier use costs extra
- SSO, audit logs and single-tenancy need Enterprise, priced on request
- Only the annual rate is shown; the month-to-month price is not listed
How Deepnote markets itself
A structured read of the promise, proof and page design on Deepnote’s captured homepage.
Homepage capture
“The open data workspace built for humans agents”
- Angle: AI-native
- Hero: Product screenshot
Pricing
Published plans and prices from Deepnote’s own pricing page.
How this score is made up
Each dimension is scored out of 100 and combined into the headline score using fixed weights.
MCP support
Whether an agent can drive the product through the Model Context Protocol, and how much setup that takes.
API quality
Public API surface: machine-readable spec, official SDKs, documented auth, errors, rate limits and versioning.
Documentation
Publicly reachable docs — coverage, freshness, code samples and machine readability.
Agent friendliness
How readable the site is to an automated client: llms.txt, structured data, server-rendered content, crawler access.
Pricing transparency
Whether real prices are published, self-serve signup exists, and usage costs are knowable without a sales call.
Changelog
A public, dated record of what shipped and when — the clearest signal that a product is still alive.
Marketing site structure
Whether the site answers a buyer's questions: clear positioning, the pages that matter, and accessibility.
Page speed
How fast the site loads for real visitors: Chrome UX Report 75th-percentile LCP, INP and CLS, with a Lighthouse mobile run standing in where a site has too little traffic for field data.
Operational trust
Status page and incident history, security disclosure, compliance and data-processing documentation.
Measured, but not part of the score
Useful to know, but not a mark for or against the product — so these do not affect the ranking.
Openness
Source availability, self-hosting, data export and open standards. Scored and shown, but not part of the composite — paid SaaS is not worse for being paid SaaS.
Maintenance
Release cadence and repository activity. Scored and shown, but not part of the composite — it is only measurable for open repositories.
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Where this comes from
The Deepnote pages BenchRank reads when it scores the product — its documentation, release notes, status and security pages, and its repository where there is one.
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