# Petals vs Ray Serve — Fit & Features

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

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

## Compare fit, features, and limitations

### Petals
🌸 Run LLMs at home, BitTorrent-style. Fine-tuning and inference up to 10x faster than offloading.
Best fit: People and teams evaluating ai models & inference for a specific workflow.
- Core product workflow
Before you choose: Test a representative task, inspect outputs, and confirm integrations, permissions, data handling, licensing, and current costs.
Pricing: https://github.com/pricing
- [Read Petals profile](https://trutool.co/tools/petals): Read Petals 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