vLLM vs SGLang: Head-to-Head Comparison

Quick Verdict

vLLM is the better pick for production GPU inference at scale. SGLang is the better pick for high-performance serving with structured generation.

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At a Glance

Feature	vLLM	SGLang
Best For	Production GPU inference at scale	High-performance serving with structured generation
Pricing	Free and open source	Free and open source
Free to Start	Yes	Yes
License	Open source	Open source
Deployment	Self-hosted	Self-hosted
Link	Visit vLLM	Visit SGLang

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Detailed Breakdown

vLLM

High-throughput LLM serving engine

Pros:

• PagedAttention for high throughput
• OpenAI-compatible server
• Broad model support

Cons:

• Requires GPUs and ops expertise
• Not aimed at laptops

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SGLang

Fast serving framework for LLMs and vision models

Pros:

• RadixAttention prefix caching
• Fast structured outputs
• Strong multi-GPU support

Cons:

• Requires GPU expertise
• Younger than vLLM

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Key Differences

• Positioning: vLLM — high-throughput LLM serving engine. SGLang — fast serving framework for LLMs and vision models.
• Both share the same licensing model (open source), so the decision comes down to features and workflow fit.
• Pricing: vLLM — free and open source. SGLang — free and open source.
• Signature strength: vLLM — PagedAttention for high throughput. SGLang — RadixAttention prefix caching.

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Frequently Asked Questions

Is vLLM better than SGLang?

It depends on your requirements. vLLM is a strong fit for production GPU inference at scale, while SGLang suits high-performance serving with structured generation.

Is vLLM free to use?

Yes, you can start with vLLM for free. Pricing model: Free and open source.

Is SGLang free to use?

Yes, you can start with SGLang for free. Pricing model: Free and open source.

Can I self-host vLLM or SGLang?

vLLM can be self-hosted. Deployment options: self-hosted. SGLang can be self-hosted. Deployment options: self-hosted.

What are the main drawbacks of vLLM and SGLang?

vLLM: requires GPUs and ops expertise; not aimed at laptops. SGLang: requires GPU expertise; younger than vLLM.