vLLM or SGLang? Here’s the actual decision guide.

Through this blog series, we have covered two deep dives on both the inference engines, one on vLLM, one on SGLang, and one long comparison blog on why their results differ in the first place. If you’ve read all three, you already know enough to debate with me on the subject (I highly welcome it).

But if you’re here looking for a short answer to the question “Which one do I actually pick for my project?”. It’s your lucky day, today we’ll discuss a quick guide to choose the best inference engine.

First, answer this honestly

Do most of your requests share context with each other?

That’s it. This question will leave you inclined towards an inference engine.

If Yes → keep reading, SGLang is probably your answer.

If No → keep reading, vLLM is probably your answer.

Before we proceed, let’s break down what “shared context” actually means in practice.

Pick SGLang if you’re building any of these

RAG systems — Your user base mostly asks questions about the same document, same knowledge base, same 5000-token context. SGLang’s prefix caching (RadixAttention, if you want the technical name) is a master of this domain. Barely anything gets recomputed.

Agentic Loops — Every reasoning step resends almost the entire conversation history plus current tool’s output. If you pass context history into your tools, then this is the exact shape of workload SGLang was designed around.

Chatbots with a long system prompt — If “You are a helpful assistant, here are your 40 rules…” repeats on every single request, why recompute it every time?

Lower TTFT — Since SGLang reduces the computation part for prefixes, often its TTFT will be comparatively lower than vLLM and might be ideal for your workload.

Hosting Image/Video Models — SGLang is known to serve and cater to a wide variety of multi-modal models with minimal issues. vLLM is catching up quickly, but if your workload is heavily dependent on hosting image/video models, then SGLang is probably the ideal option for you.

If your workload looks like any of the above, you have what I’d call a high cache-hit-rate workload. SGLang is quite literally built for these exact shapes of problems.

Pick vLLM if you’re building any of these

Independent, unrelated request — For example, translation services where every document is different. If your workload looks like random one-off Q&A where no two users are asking about the same thing and there’s almost zero overlap between requests.

You want the safer option — vLLM’s been around longer, the community support and ecosystem for vLLM is larger, more people have encountered and have provided solutions for the weird edge cases already.

Pure throughput at scale — You just want to serve as many unrelated requests as possible, as cheaply as possible, and prefix caching isn’t going to help you because there’s nothing to cache.

If your workload has a low cache-hit-rate; basically, nobody’s requests look like anyone else’s, then prefix caching barely matters. The final performance comes down to scheduler efficiency and memory management, and vLLM is just as good here, sometimes within a few percent.

The Trap: Don’t pick based on benchmark charts alone

Here’s the thing nobody actually tells you. You might read a blog or a twitter post, with benchmarks stating that “SGLang is 35% faster” and another saying “they’re identical”, typically they’re both correct but it is because you don’t have the full context and the comparison isn’t fair. They’re probably testing on completely different workloads.

A benchmark run on independent, unrelated prompts will show vLLM and SGLang neck and neck. The exact same benchmark run on shared-context agent workloads will show SGLang pulling way ahead.

The same two frameworks, benchmarked on the same hardware, will tell you a completely different story depending on what you feed them.

Quick Decision Guide

Ask yourself these, in order:

1. Do my requests heavily share a system prompt, document, or conversation history? Yes → SGLang
   
2. Am I building agents/tools that loop and resend context? Yes → SGLang
   
3. Is every request basically unrelated to every other request? Yes → vLLM
   
4. Do I just need max raw throughput and don’t care about TTFT specifically? Yes → vLLM
   
5. Is TTFT my highest priority even if throughput reduces a little? Yes → SGLang
   
6. Do I serve and work mostly with Image/Video models? Yes → SGLang
   
7. Still not sure? Probably vLLM. As it’s the safer default, and it’ll perform close enough to SGLang on generic workloads anyway.

One last thing

Neither framework makes the model architecture itself faster. They just organize the same GPU’s time differently: One is optimized for volume of unrelated work, the other is optimized for reusing work that’s already been done.

Pick based on your actual workload shape, not on whichever benchmark chart you saw first on the Internet.