Ray Serve
Ray Serve is a scalable model serving library built on the Ray distributed computing framework.
FreeWebAPICLIOpen sourceMultimodal
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Ray Serve is a scalable model serving library built on the Ray distributed computing framework. It is framework-agnostic, supporting PyTorch, TensorFlow, and Scikit-learn, and enables complex model pipeline composition with dynamic autoscaling. Its Python-first approach allows data scientists to implement complex inference logic and deploy to production seamlessly using familiar tools.
Key features
- Framework Agnostic
- Python-first Configuration
- Dynamic Autoscaling
- Complex Pipeline Composition
- Distributed Resource Management
- HTTP & gRPC Support
- Zero-downtime Updates
Pricing
Use cases
- Large-scale LLM Serving
- Real-time Recommendation Systems
- Multi-model Ensemble Deployment
- Online Inference Pipelines
Who it is for
ML EngineersData scientistsMLOps Specialists
Integrations
KubernetesPyTorchTensorFlowHugging FacePrometheus
Tags
MLOpsRay
How we verified this
Company, pricing, and feature details come from the primary sources below and our latest verification pass. When sources disagree, the official source and the most recent check win.
Last verified 08/30/2026Verified sources: 1
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