R

Ray Serve

Scalable model serving library

A scalable model serving library for building online inference APIs. Framework-agnostic, designed for deploying machine learning models alongside business logic.

33K

GitHub Stars

none

TypeScript

steep

Learning Curve

4.0

DX Score

Visiter le Site Web → Voir la Documentation GitHub

Tarification

Model

free

Offre Gratuite

Apache 2.0 licensed, fully open source

Fonctionnalités

✓ Framework-agnostic
✓ Model composition
✓ Dynamic scaling
✓ Request batching
✓ FastAPI integration
✓ LLM optimizations
✓ Response streaming
✓ Multi-model serving
✓ GPU support

Avantages

+ Works with any ML framework
+ Excellent for LLM serving
+ Scales automatically
+ Great Python integration
+ Active development

Inconvénients

- Complex for simple cases
- Ray cluster overhead
- Learning curve
- Resource intensive

Idéal Pour

enterprise startup

Alternatives

ml-serving inference llm scalable ray