The fast serving framework for LLMs and VLMs with structured programmatic control.
SGLang pairs a fast runtime (RadixAttention for prefix caching) with a frontend language for structured generation. You write Python-like programs that call the model, branch on output, and constrain responses — ideal for agents and complex pipelines.
Who it's for: Researchers and engineers building structured, multi-step LLM workflows.
Shares prompt prefixes across requests to slash latency.
Force JSON / regex / grammar-conforming outputs natively.
Compose calls, loops, and branches in a clean Python-like syntax.
Serves multimodal models with the same high throughput.
SGLang is the strongest alternative to vLLM when you need programmatic control over generation. If your app is agentic or structured-heavy, it's worth benchmarking against vLLM directly.