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Data as of Oct 15, 2026Based on 41 AI responses
Reviewed by Dimitry Apollonsky
AI summary
Project Jupyter holds a clear lead, frequently highlighted for flexible local installations and modular interfaces. Databricks shares the spotlight on key platform differentiators for data scientists, matching it in discussions around managed compute and big data scale.
Rank and Mentioned in · since Jul 8
| # | Brand | Recommended for | Mentioned in |
|---|---|---|---|
| 1 | modular local workflows, tabbed interfaces, and software engineering control | modular local workflows, tabbed interfaces, and software engineering control | 98% |
| 2 | zero-setup cloud access, immediate GPU availability, and Vertex AI integration | zero-setup cloud access, immediate GPU availability, and Vertex AI integration | 88% |
| 3 | collaborative model training and transition to production deployment | collaborative model training and transition to production deployment | 68% |
| 4 | real-time multi-user editing, inline commenting, and live session sharing | real-time multi-user editing, inline commenting, and live session sharing | 59% |
| 5 | Apache Spark integration, automatic versioning, and real-time co-editing | Apache Spark integration, automatic versioning, and real-time co-editing | 56% |
| 6 | hands-on data science experimentation and shared community projects | hands-on data science experimentation and shared community projects | 32% |
| 7 | enterprise data governance and large datasets exceeding local memory | enterprise data governance and large datasets exceeding local memory | 29% |
| 8 | shared compute clusters, data governance, and production machine learning | shared compute clusters, data governance, and production machine learning | 27% |
| 9 | end-to-end machine learning lifecycles, model registries, and deployment | end-to-end machine learning lifecycles, model registries, and deployment | 27% |
| 10 | collaborative cloud notebooks operating with managed GPU support | collaborative cloud notebooks operating with managed GPU support | 12% |
| 11 | 10% | ||
| 12 | 10% | ||
| 13 | 10% | ||
| 14 | 10% | ||
| 15 | 10% |
What’s changing · since July
Since July, Google Colabrising has become one of the three most named options when data scientists compare key platform differentiators.
Weeks of Aug 24 – Sep 27, 2026 · Since Jul 5
Widest split: Deepnote, 29% on ChatGPT Search and 90% on Google AI Mode.
| Brand | ChatGPT Search | Google AI Mode | Comparison |
|---|---|---|---|
| Project Jupyter | 100% | 95% | |
| Google Colab | 86% | 90% | |
| Visual Studio Code | 71% | 65% | |
| Deepnote | 29% | 90% | |
| Databricks Collaborative Notebooks | 48% | 65% | |
| Kaggle | 10% | 55% | |
| Databricks Data Intelligence Platform | 24% | 35% | |
| Databricks | 38% | 15% | |
| Amazon SageMaker | 38% | 15% | |
| Nextjournal | Outside ChatGPT Search's top 25 | 25% |
33% of citations point at the ranked brands’ own sites. datasciencenotebook.org, medium.com, youtube.com are the most-cited sites outside the ranked brands’ own domains.
What are the key differentiators between notebook environments for a data scientist?
