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Project comparison
MLflow vs Ray
Compare adoption, momentum, maintenance health, and project basics before choosing which tool to evaluate deeper.
MLflow
The open source AI engineering platform for agents, LLMs, and ML models. MLflow enables teams of all sizes to debug, evaluate, monitor, and optimize production-quality AI applications while controlling costs and managing access to models and data.
25.9K
Stars
+129
Weekly
88/100
Health
Ray
Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
42.5K
Stars
+72
Weekly
79/100
Health
Quick Take
Adoption
Ray has the larger GitHub footprint with 42.5K stars.
Momentum
MLflow is currently growing faster at +129 stars this week.
Maintenance
MLflow has the stronger health score at 88/100.
Side-by-side Metrics
| Signal | MLflow | Ray |
|---|---|---|
| GitHub stars | 25.9K | 42.5K |
| Weekly growth | +129 | +72 |
| Health score | 88 | 79 |
| Contributors | 1.0K | 1.6K |
| Commits per week | 112.2 | 87.6 |
| Open issues | 2.1K | 3.5K |
| Language | Python | Python |
| License | Apache-2.0 | Apache-2.0 |
| Last commit | 15h ago | 15h ago |
| Last release | v3.12.0 | ray-2.55.1 |
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