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

Website besuchen → Dokumentation anzeigen GitHub

Preise

Model

free

Kostenlose Stufe

Apache 2.0 licensed, fully open source

Funktionen

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

Vorteile

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

Nachteile

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

Am besten für

enterprise startup

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