SGLang vs vLLM

SGLang

Fine-tuning

vLLM

Fine-tuning

Tagline

SGLang

Open-source high-throughput inference engine for LLMs and multimodal models with OpenAI-compatible serving.

vLLM

Open-source high-throughput inference engine for serving LLMs with PagedAttention and continuous batching.

Pricing

SGLang

Free· Free, open-source (Apache 2.0); self-hosted infra cost only

vLLM

Free· Free and open-source (Apache 2.0); self-hosted infrastructure costs apply

Free trial

SGLang

Yes

vLLM

Yes

API

SGLang

Yes

vLLM

Yes

Platforms

SGLang

api

vLLM

macosapi

Open source

SGLang

Yes · Apache-2.0

vLLM

Yes · Apache-2.0

GitHub stars

SGLang

36,602

checked 2026-09-29

vLLM

92,958

checked 2026-09-29

Last GitHub push

SGLang

2026-09-29

vLLM

2026-09-29

First commit

SGLang

2024-01

vLLM

2023-02

Model used

SGLang

Multi-model (DeepSeek, Qwen, Llama, Mistral, GLM, GPT-OSS)

vLLM

Multi-model (open-weight LLMs: Llama, Qwen, DeepSeek, Mistral, Gemma, Phi, etc.)

Best for

SGLang

Pick SGLang if you are running open-weight LLMs on your own GPUs and need top-tier throughput with an OpenAI-compatible interface.

vLLM

Pick vLLM if you are self-hosting open-weight LLMs at any meaningful scale and need an OpenAI-compatible endpoint with maximum tokens-per-dollar.

Not for

SGLang

Skip it if you want a hosted inference API you can hit with a credit card and zero ops.

vLLM

Skip it if you don't run your own GPUs or you'd rather pay a managed inference provider than tune batching, parallelism, and KV cache yourself.

Editorial score

SGLang

8.2 / 10

vLLM

8.3 / 10

Use cases

SGLang

llm-servingmultimodal-inferenceself-hostingopenai-compatible-apihigh-throughput-inference

vLLM

llm-servingself-hosted-inferenceopenai-api-replacementhigh-throughput-batchingmulti-gpu-deployment

Pros

SGLang

• State-of-the-art throughput via speculative decoding and disaggregated prefill/decode
• OpenAI-compatible endpoints make migration from hosted APIs trivial
• Broad hardware coverage: NVIDIA, AMD, TPU, Ascend, XPU, CPU
• Backed by real production users (NVIDIA, xAI, Oracle, LinkedIn)
• Fully open source under Apache 2.0

vLLM

• PagedAttention delivers industry-leading throughput on the same hardware
• Drop-in OpenAI-compatible API makes migration from hosted models trivial
• Broad hardware support spanning NVIDIA, AMD, Intel, TPU, and Neuron
• Apache-2.0, no per-token cost, no vendor lock-in
• Backed by Berkeley + major-cloud sponsors with very active release cadence

Cons

SGLang

• Self-hosted only; no managed inference offering
• Tuning for peak throughput requires real ML-infra expertise
• Documentation assumes you already know LLM-serving concepts

vLLM

• You provide and operate the GPUs; no managed offering
• Steep learning curve for tuning parallelism, quantization, and KV cache
• Bleeding-edge model support sometimes lags the model's release by days
• Multi-node deployment requires Ray or Kubernetes plumbing

Website

SGLang

sglang.io

vLLM

vllm.ai

Editorial score: rule-based, 0–10, from AI-assisted profile inputs (see /methodology) — not a user rating; “—” means unscored. “Not listed” means we have no record of it, not that it is absent. GitHub figures and prices carry the date they were checked or captured; prices are shown as published, unconverted.