vLLM vs SGLang: Performance, Features & Deployment Compared

DeepInfra's blog post discusses the comparison between vLLM and SGLang, two inference engines used for AI workloads, emphasizing that benchmark numbers often fail to provide a clear decision-making guide due to differences in configurations and workloads. The article highlights that choosing between vLLM and SGLang should be based on specific workload characteristics, such as prefix reuse, batch shape, structured output share, and model topology, rather than relying solely on throughput benchmarks. It also points out the importance of considering whether to self-host or use a managed endpoint, factoring in costs, regulatory requirements, workload types, and operational complexities. The post underscores the need to measure individual workload characteristics to make an informed decision and suggests that the choice between self-hosting and using a managed service depends on utilization patterns, regulatory needs, and operational priorities.

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