Databricks vs. Kubeflow
A side-by-side comparison based on G2 review data.
| Metric | Databricks | Kubeflow |
|---|---|---|
| Overall rating | 4.6 | 4.5 |
| Review count | 1,388 | 21 |
| Ease of use | 8.9 | 8.0 |
| Ease of setup | 8.7 | 7.0 |
| Quality of support | 8.9 | 7.7 |
| Meets requirements | 9.1 | 8.8 |
| NPS | 77.851361 | 57.142857 |
| Small Business | 15.1% | 42.9% |
| Mid-Market | 37.6% | 14.3% |
| Enterprise | 47.2% | 42.9% |
| Total page views | 16,340 | 82 |
| Total LLM citations | 14,031 | 133 |
Databricks
Databricks is the Data and AI company. More than 20,000 organizations worldwide — including adidas, AT&T, Bayer, Block, Mastercard, Rivian, Unilever, and 70% of the Fortune 500 — rely on the Databricks Data + AI Platform to build and scale data and AI apps, analytics, and agents. Headquartered in San Francisco with 30+ offices around the globe, Databricks offers a unified platform that includes Genie, Lakebase, Agent Bricks, Lakeflow, Lakehouse, and Unity Catalog.
- Features
- Ease of Use
- Integrations
- Collaboration
- Analytics
- Easy Integrations
Kubeflow
Kubeflow is an open-source platform designed to facilitate the deployment, orchestration, and management of machine learning (ML) workflows on Kubernetes. It provides a comprehensive suite of tools that cover the entire ML lifecycle, enabling data scientists and engineers to develop, train, and deploy models efficiently in scalable and portable environments. Key Features and Functionality: - Kubeflow Notebooks: Offers web-based development environments, such as Jupyter Notebooks, running inside Kubernetes pods, allowing for interactive model development.
- Model Variety
- Problem Solving
- Efficiency
- Flexibility
- Scalability