Originally published at https://blog.runc.ai/sglang-vs-vllm/ .

Key Takeaways

vLLM is still the default starting point for many teams because it is widely adopted, easy to get running, and strongly associated with high-throughput LLM serving.

SGLang is increasingly compelling when you care about aggressive serving optimizations, structured outputs, multimodal support, and lower-level serving control.

Both frameworks expose OpenAI-compatible APIs, so the practical decision often comes down to feature fit, operational preference, and model support rather than API style alone.

The best choice is usually workload-specific: vLLM for broad default adoption, SGLang for teams that want deeper serving-system optimization or more specialized features.

If you plan to deploy either framework in production, the infrastructure choice still matters.…