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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
Visitar Sitio Web → Ver Documentación GitHub

Precios

Model
free
Plan Gratuito
Apache 2.0 licensed, fully open source

Características

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

Ventajas

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

Desventajas

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

Mejor Para

enterprise startup

Alternativas

bentoml mlflow
ml-serving inference llm scalable ray