# MLflow vs Ray Serve — Fit & Features

Canonical: https://trutool.co/compare/mlflow-vs-ray-serve
Author: Snehil (https://trutool.co/authors/snehil)

Compare MLflow and Ray Serve by use case, features, limitations, and evaluation questions.

## Compare fit, features, and limitations

### MLflow
Track experiments and manage the lifecycle of machine learning and generative AI applications.
Best fit: Machine learning teams building, serving, and operating model workloads.
- Experiment tracking
- Model registry
- AI evaluation
Before you choose: Benchmark your workload, verify model licenses and hardware requirements, and compare inference, hosting, and storage costs.
Pricing: https://mlflow.org
- [Read MLflow profile](https://trutool.co/tools/mlflow): Read MLflow profile

### Ray Serve
Build scalable model-serving applications with Ray.
Best fit: Machine learning teams building, serving, and operating model workloads.
- Model serving
- Python deployment
- Scalable inference
Before you choose: Benchmark your workload, verify model licenses and hardware requirements, and compare inference, hosting, and storage costs.
Pricing: https://docs.ray.io/en/latest/serve/index.html
- [Read Ray Serve profile](https://trutool.co/tools/ray-serve): Read Ray Serve profile