Beyond vLLM: A Practical Guide to Choosing the Right LLM Serving Engine on Kubernetes

A technical guide aimed at Kubernetes engineers explains that vLLM is not the only option for serving large language models, with alternatives like SGLang, TGI, Triton, and TensorRT-LLM capable of performing the same core role. The serving engine — not Kubernetes itself — is responsible for loading model weights, managing the KV cache, batching requests, and generating tokens. While Kubernetes handles scheduling, GPU allocation, and networking, it has no visibility into engine-level issues such as poor batching strategies or undersized caches. vLLM remains a popular default due to its OpenAI-compatible API, broad hardware support, and memory-efficient PagedAttention mechanism. However, the guide argues that choosing a serving engine should be a deliberate, workload-specific decision rather than a reflexive default.