Ray Serve
Scale model serving across a cluster
Inference serverServing frameworkApache-2.0Runs locallyPythonAdvanced
🐳 Docker pulls
📦 PyPI downloads / month
📦 npm downloads / month
🚀 Latest release ·
What is Ray Serve?
Ray Serve is a scalable model-serving library that composes multiple models and Python business logic into one deployment, scaling across a Ray cluster.
Why people choose Ray Serve
- Composes several models in one pipeline
- Autoscaling across a cluster
- Framework-agnostic
Ray Serve at a glance
| Category | Inference server |
| Type | Serving framework |
| License | Apache-2.0 |
| Runs locally | Yes |
| Built with | Python |
| Skill level | Advanced |
| Best for | multi-model production pipelines at scale |
Latest releases
Open-source alternatives to Ray Serve
Other open-source inference server tools worth comparing:
vLLMHigh-throughput serving for production
TGIHugging Face's production text server
SGLangFast serving with structured outputs
LMDeployToolkit for compressing and serving LLMs
Aphrodite EngineHigh-throughput LLM serving
TensorRT-LLMPeak throughput on NVIDIA GPUs
OpenLLMServe any open model as an OpenAI API in one command
KTransformersRun huge MoE models on one consumer GPUBentoMLPackage any model into a production APIRay Serve head-to-head
Ray Serve vs vLLMRay Serve vs TGIRay Serve vs SGLangRay Serve vs LMDeployRay Serve vs Aphrodite EngineRay Serve vs TensorRT-LLMRay Serve vs OpenLLMRay Serve vs KTransformersRay Serve vs BentoML
FAQ
Is Ray Serve free?
Ray Serve is free and open-source (Apache-2.0 license), so you can use, self-host and modify it at no cost.
Can I run Ray Serve locally?
Yes. Ray Serve is designed to run on your own machine or server, keeping your data private.
What is the best alternative to Ray Serve?
Popular open-source alternatives include vLLM, TGI, SGLang. See the comparisons above to choose.
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