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Head-to-head · Notebook Software

Deepnote vs Apache Zeppelin

  • Updated Oct 2026
  • Both researched from official sources
  • 4 checks side by side
Higher score Deepnote #6 in Notebook Software 8.8/10 Free plan · 14-day trial Free planFree trial✓ 3 of 3 features Visit Deepnote
Apache Zeppelin #12 in Notebook Software 8.1/10 Open source ✓ 3 of 3 features Try Apache Zeppelin

Deepnote leads on 0 checks, Apache Zeppelin on 0, and 4 are even. Who comes out ahead on the 4 yes/no, price and count checks where we have data for both products. The editor score weighs everything else too.

Our verdict

  • Highest scoreDeepnote · 8.8/10
  • Free planonly Deepnote
  • Free trialonly Deepnote

Deepnote scores higher on our rubric for notebook software: 8.8 against 8.1 out of 10; our editors rank them #6 and #12.

Deepnote is the better fit for collaborative data teams. Apache Zeppelin is the better fit for big-data and Spark notebook workflows.

  • Deepnote fits best

    Collaborative data teams

  • Apache Zeppelin fits best

    Big-data and Spark notebook workflows

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Side by side

Feature Deepnote 8.8/10 Visit ↗ Apache Zeppelin 8.1/10 Visit ↗
At a glance
Editor score 8.8 8.1
Ranking #6 in Notebook Software #12 in Notebook Software
Best for Collaborative data teams Big-data and Spark notebook workflows
Pricing model Free plan + paid Free
Starting price $39/seat/mo Open source
Free plan ✓ (best) Not checked yet
Free trial 14 days —
Deployment Cloud, Self-hosted Self-hosted
Platforms Web, Windows, macOS, Linux Web, macOS, Linux
Support Email, Live chat, Community, Docs Community, Docs
Integrations 20 integrations 14 integrations
Built for Small business, Mid-market, Enterprise Small business, Mid-market, Enterprise
Features Deepnote 3/3 · Apache Zeppelin 3/3
Real-time collaboration ✓ ✓
Version history ✓ ✓
GPU compute ✓ ✓
Specs
Execution model Hybrid Hybrid
Supported languages Python, SQL, R, Stata Scala, Python, SQL, R, Java, Groovy, Shell, Markdown, SPARQL
Notebook formats .deepnote, .ipynb, .qmd, .py (Percent/Marimo), PDF JSON, .zpln
Our review
Pros
  • Real-time editing, shared execution, and revision history
  • Python and SQL support with broad database integrations
  • Managed environments with Docker images, GPUs, and AI features
  • Built-in Apache Spark integration with pluggable interpreters
  • Real-time collaboration, sharing, publishing, and Git versioning
  • Charts, tables, REST APIs, and Kubernetes GPU support
Cons
  • Team pricing starts at $39 per editor monthly when billed annually
  • Free plan limits teams to 3 editors and 5 projects
  • Enterprise-only controls include SSO, audit logs, and single-tenancy
  • Self-hosted deployment requires teams to manage infrastructure
  • Its data-platform focus is narrower than general Jupyter tools
  • GPU support depends on Kubernetes interpreter pods
Our verdict

Deepnote is a collaborative notebook platform for data teams and analysts working with Python, SQL, visualizations, text, and interactive inputs. Browser-based projects support real-time editing and shared execution environments, while the…

Read the review →

Apache Zeppelin is a self-hosted, web-based notebook for interactive data analytics, visualization, and collaboration. It is aimed at data-platform teams and analysts who need notebooks that can connect to multiple languages, processing…

Read the review →
  1. DeepnoteNotebook Software 8.8Free plan · 14-day trial
  2. Apache ZeppelinNotebook Software 8.1Open source

Strengths and trade-offs

  • Deepnote — where it wins

    • Real-time editing, shared execution, and revision history
    • Python and SQL support with broad database integrations
    • Managed environments with Docker images, GPUs, and AI features

    Where it doesn't

    • Team pricing starts at $39 per editor monthly when billed annually
    • Free plan limits teams to 3 editors and 5 projects
    • Enterprise-only controls include SSO, audit logs, and single-tenancy
  • Apache Zeppelin — where it wins

    • Built-in Apache Spark integration with pluggable interpreters
    • Real-time collaboration, sharing, publishing, and Git versioning
    • Charts, tables, REST APIs, and Kubernetes GPU support

    Where it doesn't

    • Self-hosted deployment requires teams to manage infrastructure
    • Its data-platform focus is narrower than general Jupyter tools
    • GPU support depends on Kubernetes interpreter pods

More comparisons

Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026

Last updated · How we research and update