The popularity of MLflow deployments really depends on the audience — individual developers vs. enterprise teams — and cloud ecosystem preferences. But based on usage trends, enterprise adoption, and community growth (as of 2025), here’s a breakdown:
🏆 Most Popular MLflow Deployment (2025 Ranking)
| Rank | Deployment Type | Popularity Reason |
|---|---|---|
| 🥇 1 | Databricks Managed MLflow | ✅ Most widely used in enterprise setups due to native support, zero setup, scalability, and built-in governance |
| 🥈 2 | MLflow Local (Laptop) | ✅ Hugely popular among students, individual practitioners, and small teams for experimentation and learning |
| 🥉 3 | Azure ML + MLflow | ✅ Preferred in Microsoft Azure ecosystems; seamless for enterprise Azure users |
| 🏅 4 | SageMaker + MLflow | ✅ Popular in AWS-heavy infrastructures; but requires more manual configuration |
| 🏁 5 | MLflow on Kubernetes | ✅ Used by advanced teams needing full control; less popular due to complexity |
📊 Popularity Summary:
- Databricks MLflow is the most popular in production and team-based environments, thanks to being fully managed, scalable, and deeply integrated with the modern data stack (Spark, Delta, Unity Catalog, etc.).
- MLflow Local (Laptop) is most popular among individual users, developers, researchers, and small startups. It’s easy to install and perfect for learning, making it widely used despite its limitations.
- Azure ML + MLflow and SageMaker + MLflow are tied closely to cloud platform preference. Both are gaining ground fast but are mostly popular within their respective ecosystems.
- MLflow on Kubernetes is powerful, but only used by tech-savvy DevOps teams or platform engineering teams due to its complexity and maintenance overhead.
✅ Final Verdict:
If you’re asking what’s most popular across all users:
- Individual Users → MLflow Local
- Enterprise/Teams → Databricks Managed MLflow