Open-Source AI · Inference server

vLLM vs SGLang

vLLM vs SGLang compared for 2026 — features, license, ease of use, performance and which one to choose. High-throughput serving for production vs Fast serving with structured outputs.

Updated regularly · curated by olud.ai

Choose vLLM for production teams serving models at scale. Choose SGLang for teams needing structured-output serving.

vLLM vs SGLang at a glance

Spec	vLLM	SGLang
Category	Inference server	Inference server
Type	Inference server	Inference server
License	Apache-2.0	Apache-2.0
Runs locally	Self-hosted	Self-hosted
Primary language	Python	Python
Ease of use	Advanced	Advanced
Best for	production teams serving models at scale	teams needing structured-output serving
GitHub stars	86.8k	30.6k

Feature comparison

Feature	vLLM	SGLang
OpenAI-compatible API	✓	✓
Continuous batching	✓	✓
Quantization	✓	✓
Multi-GPU	✓	✓
Structured output	✓	✓
Docker	✓	✓

How vLLM and SGLang score

🤝 Too close to call — vLLM and SGLang land within a hair (4.3 vs 4.2 / 5). Pick on fit, not on score.

Criterion	vLLM	SGLang
Popularity	4.5	4.0
Maintenance	5.0	5.0
Ease of use	2.5	2.5
Privacy	4.5	4.5
License freedom	5.0	5.0

Scores are computed automatically from public signals — GitHub stars (popularity), recent commit activity (maintenance), license type (freedom), local-first design (privacy) and onboarding complexity (ease of use). Indicative, not a verdict.

What each one is

vLLM

Inference server · Apache-2.0

vLLM is a high-throughput inference and serving engine using PagedAttention to maximize GPU utilization, the default choice for serving open models at scale.

Best-in-class throughput via PagedAttention
OpenAI-compatible server, broad model support
The de-facto standard for production serving

See the vLLM page →

SGLang

Inference server · Apache-2.0

SGLang is a fast serving framework for LLMs and vision-language models, featuring RadixAttention and strong support for structured and programmatic generation.

Very fast with RadixAttention caching
First-class structured / programmatic generation
Strong vision-language model support

See the SGLang page →

Key differences

vLLM is inference server, while SGLang is inference server. In short, vLLM fits production teams serving models at scale, and SGLang fits teams needing structured-output serving.

Which should you choose?

Choose vLLM for production teams serving models at scale. Choose SGLang for teams needing structured-output serving.

There is rarely one winner — many setups use both. The right pick depends on your hardware, your team's skills, and whether you value simplicity or control.

Frequently asked questions

Is vLLM or SGLang easier to use?

Both sit at a similar level (Advanced). Your choice should come down to fit rather than difficulty.

Are vLLM and SGLang free?

vLLM is free and open source (Apache-2.0), and SGLang is free and open source (Apache-2.0). Neither charges for the core software.

Can I run vLLM and SGLang locally?

vLLM: self-hosted · SGLang: self-hosted. Both can be used without sending your data to a third-party cloud where their setup allows.

vLLM vs SGLang — which should I pick in 2026?

Choose vLLM for production teams serving models at scale. Choose SGLang for teams needing structured-output serving.

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