SGLang vs vLLM
Detailed side-by-side comparison to help you choose the right tool
SGLang
🔴DeveloperLLM Inference
High-performance open-source serving framework for LLMs and multimodal models, optimized for structured generation and complex agent workloads.
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CustomvLLM
🔴DeveloperLLM Inference
High-throughput, memory-efficient open-source inference and serving engine for LLMs, used as the default backend at many AI companies.
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CustomFeature Comparison
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SGLang - Pros & Cons
Pros
- ✓RadixAttention is a real throughput win for agent loops with shared prefixes
- ✓Constrained decoding makes JSON/tool-call output cheap
- ✓Often leads vLLM on DeepSeek MoE and structured workloads
- ✓Apache 2.0 — no license cost, fully self-hostable
- ✓OpenAI-compatible API means most client SDKs work unchanged
Cons
- ✗Operational complexity higher than vLLM
- ✗Smaller ecosystem of third-party guides and integrations
- ✗Parallelism sharding is unforgiving — misconfigurations hurt throughput badly
- ✗Smaller managed-service ecosystem than vLLM
- ✗Documentation assumes prior inference-serving experience
vLLM - Pros & Cons
Pros
- ✓Industry-standard backend with broad community support
- ✓PagedAttention makes high-concurrency serving practical on single GPUs
- ✓OpenAI-compatible API means clients work unchanged
- ✓Apache 2.0 — no license cost, no rug-pull risk
- ✓Runs almost any popular open model on almost any accelerator
Cons
- ✗SGLang sometimes outperforms on shared-prefix agent workloads
- ✗Peak throughput requires careful parallelism and quantization tuning
- ✗Multi-replica cluster operations are real DevOps work
- ✗Newer model architectures sometimes lag a release behind
- ✗Self-hosting only makes economic sense above a meaningful volume threshold
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