vLLM vs Ray Serve

vLLM is the high-throughput LLM inference engine that effectively set the category standard, and its PagedAttention memory management is why it serves far more concurrent requests per GPU than a naive implementation. It exposes an OpenAI-compatible API, so an application already talking to OpenAI can be pointed at a vLLM endpoint by changing a base URL. It is Apache-2.0 and now sits under the PyTorch Foundation rather than a single company. The important thing to understand about the whole category: a large share of the managed platforms you might pay for are running vLLM underneath, so choosing it directly is not a downgrade from the commercial option — it is the commercial option without the margin.

Ray Serve is the serving layer of Ray, the distributed computing framework, and it is the answer when the hard part is not throughput on one card but coordinating many models across many machines. It has first-class vLLM support, does autoscaling and back-pressure properly, and composes pipelines where a request touches several models in sequence. It is heavier than the others and that weight is the point — it is aimed at the case where you are building serving infrastructure rather than deploying an endpoint. If you only need one model behind one URL, this is more machinery than the job requires.