vLLM vs SGLang: picking an inference engine

vLLM and SGLang are the two engines we reach for most. Both are fast, both are open source, and both will happily serve any HuggingFace model. The difference is what they are tuned for — and the right choice depends almost entirely on the shape of your traffic.

vLLM — the throughput workhorse

vLLM popularized paged attention, which treats the KV cache like virtual memory and packs many concurrent requests into a GPU without fragmentation. If your workload is a firehose of independent chat requests, vLLM's continuous batching is hard to beat on tokens-per-dollar.

• Best for: high-concurrency, independent requests.
• Strength: raw throughput and broad model coverage.
• Reach for it when: you are serving a chat product with lots of unrelated users.

SGLang — structured and shared-prefix work

SGLang adds RadixAttention, which shares KV cache across requests that have a common prefix. If your prompts share a big system prompt, a long few-shot preamble, or a tree of structured calls, SGLang reuses that work instead of recomputing it.

• Best for: shared prefixes, agents, structured generation, JSON-constrained output.
• Strength: prefix caching and fast structured decoding.
• Reach for it when: every request starts with the same 2k-token system prompt.

Rule of thumb
-------------
Many unrelated chats ............ vLLM
Shared system prompt / few-shot . SGLang
Agent / tool-calling trees ...... SGLang
Pure max-throughput batch ....... vLLM

In practice the gap is smaller than the internet implies, and both keep leapfrogging each other release to release. We default to the one that fits your traffic shape and re-benchmark when your workload changes — you should not have to track engine release notes to keep your endpoint fast.

Not sure which way your traffic leans? Tell us the model and a sentence about how it is used, and we will pick — and show our work.