# Ray Serve vs Replicate — Fit & Features

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

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

## Compare fit, features, and limitations

### 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

### Replicate
Run and deploy machine-learning models through an API.
Best fit: Developers evaluating model APIs or inference infrastructure.
- Run and deploy machine-learning models through an API
Before you choose: Compare latency, usage billing, rate limits, model licenses, and data handling for your workload.
Pricing: https://replicate.com/
- [Read Replicate profile](https://trutool.co/tools/replicate): Read Replicate profile