product
Data quality: Editorially ReviewedProcess automation
Free
AI infrastructure & hosting
Workflow orchestration
Self-hosted / on-premises
Agent runtime
Private cloud / VPC
Model Serving
SaaS (vendor-hosted)
GPU & Compute
On-device / edge
Model inference
Cross-industry / Horizontal
Enterprise / custom quote
Ray Serve
AI infrastructure & hosting
Overview
Ray Serve is an open-source model-serving library within the Ray ecosystem. It lets developers deploy model ensembles, APIs, and scalable inference pipelines across distributed CPU and GPU clusters.
- Pricing model
- Free
- Deployment
- SaaS (vendor-hosted), Self-hosted / on-premises, Private cloud / VPC, On-device / edge
- Regions served
- Global
- Categories
- Process automation, Free, AI infrastructure & hosting, Workflow orchestration, Self-hosted / on-premises, Agent runtime, Private cloud / VPC, Model Serving, SaaS (vendor-hosted), GPU & Compute, On-device / edge, Model inference, Cross-industry / Horizontal, Enterprise / custom quote
- Last updated
- 6 October 2026
- Published
- 6 October 2026
Use cases
Process automation
Workflow orchestration
Deployment options
SaaS (vendor-hosted)
Self-hosted / on-premises
Private cloud / VPC
On-device / edge
Data sources & provenance
Where this information came from, how it was checked and when it was last looked at. “Last updated” records a change to the record — it does not mean the record was verified.
- Data quality
- Data quality: Editorially Reviewed
- Primary source
- docs.ray.io
- Sources cited
- 1
- Last reviewed
- 5 October 2026
- Last updated
- 6 October 2026
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