vLLM vs BentoML

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.

BentoML is the packaging and serving framework around the engine: you define a service in Python, it builds an OCI image with the model, dependencies and API baked in, and that image runs on your Kubernetes cluster, a VM, or a managed platform without change. It works with vLLM as a backend, so you get vLLM's throughput plus a deployment story. Of everything here it is closest in spirit to what Modal does — decorated Python that becomes a running endpoint — with the difference that the artefact is a standard container you own rather than a platform you rent.