Ray Serve
by Anyscale · open-source · Last verified 2026-03-17
Scalable model serving library built on the Ray distributed computing framework. Provides model composition, autoscaling, and request batching for deploying complex ML inference pipelines at scale.
https://docs.ray.io/en/latest/serve/ ↗C+
C+—Average
Adoption: BQuality: AFreshness: ACitations: C+Engagement: F
Specifications
- License
- Apache-2.0
- Pricing
- open-source
- Capabilities
- model-composition, autoscaling, request-batching, distributed-serving, multi-model
- Integrations
- hugging-face, langchain
- Use Cases
- scalable-serving, model-pipelines, distributed-inference, production-ml
- API Available
- Yes
- SDK Languages
- python
- Deployment
- self-hosted, anyscale-cloud, kubernetes
- Rate Limits
- N/A (self-hosted)
- Data Privacy
- Self-hosted, user-managed
- Tags
- model-serving, ray, distributed, scalable
- Added
- 2026-03-17
- Completeness
- 100%
Index Score
55.5Adoption
60
Quality
85
Freshness
82
Citations
58
Engagement
0