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Head-to-head · Notebook Software

Datalore vs Apache Zeppelin

  • Updated Oct 2026
  • Both researched from official sources
  • 4 checks side by side
Higher score Datalore #5 in Notebook Software 8.9/10 Free plan · 14-day trial Free planFree trial✓ 3 of 3 features Visit Datalore
Apache Zeppelin #12 in Notebook Software 8.1/10 Open source ✓ 3 of 3 features Try Apache Zeppelin

Datalore 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 scoreDatalore · 8.9/10
  • Free planonly Datalore
  • Free trialonly Datalore

Datalore scores higher on our rubric for notebook software: 8.9 against 8.1 out of 10; our editors rank them #5 and #12.

Datalore is the better fit for polyglot teams needing deployment options. Apache Zeppelin is the better fit for big-data and Spark notebook workflows.

  • Datalore fits best

    Polyglot teams needing deployment options

  • Apache Zeppelin fits best

    Big-data and Spark notebook workflows

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Side by side

Feature Datalore 8.9/10 Visit ↗ Apache Zeppelin 8.1/10 Visit ↗
At a glance
Editor score 8.9 8.1
Ranking #5 in Notebook Software #12 in Notebook Software
Best for Polyglot teams needing deployment options Big-data and Spark notebook workflows
Pricing model Free plan + paid Free
Starting price — Open source
Free plan ✓ (best) Not checked yet
Free trial 14 days —
Deployment Cloud, Self-hosted Self-hosted
Platforms Web Web, macOS, Linux
Support Tickets, Community, Docs, Email Community, Docs
Built for Solo, Small business, Mid-market, Enterprise Small business, Mid-market, Enterprise
Features Datalore 3/3 · Apache Zeppelin 3/3
Real-time collaboration ✓ ✓
Version history ✓ ✓
GPU compute ✓ ✓
Specs
Execution model Hybrid Hybrid
Supported languages Python, SQL, R, Scala, Kotlin Scala, Python, SQL, R, Java, Groovy, Shell, Markdown, SPARQL
Notebook formats .ipynb, .datalore, .zip, .py, .kts, .scala, .r, .html JSON, .zpln
Our review
Pros
  • Supports Python, SQL, R, Scala, and Kotlin notebooks
  • Combines real-time collaboration, version history, and shared workspaces
  • Offers cloud, API, and self-hosted deployment options
  • 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
  • Cloud Free excludes notebook version history
  • GPU access is limited to paid Cloud computation and configurable On-Premises deployments
  • The Cloud plan is listed without a separate plan price or distinct published entry
  • 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

Datalore is a collaborative data science notebook platform from JetBrains for teams working across analysis, reporting, and shared notebook workflows. It supports Python, SQL, R, Scala, and Kotlin, with database connections, SQL cells, CPU…

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. DataloreNotebook Software 8.9Free plan · 14-day trial
  2. Apache ZeppelinNotebook Software 8.1Open source

Strengths and trade-offs

  • Datalore — where it wins

    • Supports Python, SQL, R, Scala, and Kotlin notebooks
    • Combines real-time collaboration, version history, and shared workspaces
    • Offers cloud, API, and self-hosted deployment options

    Where it doesn't

    • Cloud Free excludes notebook version history
    • GPU access is limited to paid Cloud computation and configurable On-Premises deployments
    • The Cloud plan is listed without a separate plan price or distinct published entry

  • 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

All notebook software comparisons → · Full ranking →

Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026

Last updated · How we research and update