vLLM vs SGLang: Which Open-Source LLM Serving Engine Should You Use in Production?

Short answer

vLLM is the safer default for most production LLM-serving teams. It has a mature open-source serving engine, OpenAI-compatible HTTP API, PagedAttention-based memory management, continuous batching, structured outputs, metrics, and Kubernetes-oriented production guidance. SGLang is a serious alternative for teams whose workloads involve repeated prefixes, multi-call agent flows, structured generation, or LLM-program-like execution patterns. The best answer is to start with vLLM, then benchmark SGLang if your workload clearly matches its strengths.

Performance model: PagedAttention vs RadixAttention

vLLM's core performance story is PagedAttention, which manages KV cache in blocks rather than requiring contiguous memory. In practice, that reduces fragmentation and helps the server batch more concurrent requests efficiently. SGLang includes many modern serving optimizations too, but its distinctive concept is RadixAttention: automatic KV-cache reuse across prompts and generation calls that share prefixes. If your traffic is mostly independent chat/completion requests, vLLM is usually the more conservative starting point. If many requests reuse the same long system prompt, retrieval context, tool schema, or agent state, SGLang deserves direct measurement.

OpenAI-compatible APIs

Both engines provide OpenAI-compatible APIs, which makes them practical migration targets for teams already using OpenAI-style clients. vLLM's OpenAI-compatible server is one of its biggest adoption advantages and is well documented for production usage. SGLang also offers OpenAI-compatible endpoints for moving from hosted APIs to self-hosted local models. Compatibility is not the same as identical behavior, so teams should validate streaming, chat templates, tool calls, structured outputs, and edge-case parameters before changing production traffic.

Structured outputs

Structured generation matters for extraction, agents, tool use, and reliable application pipelines. vLLM supports JSON schema, regex, choice, grammar, whitespace, and structural tag modes through structured-output backends. SGLang supports JSON schema, regex, and EBNF-style constraints, with XGrammar used in current documentation. LMSYS has published benchmark-specific work around faster structured and JSON decoding, so SGLang can be especially interesting when output schemas are complex or latency-sensitive. The right move is not to trust generic benchmarks, but to test your real schemas and expected output lengths.

Production operations and observability

vLLM exposes production metrics through a metrics endpoint and has documentation for production-stack and Kubernetes deployments. That makes it easier to reason about latency, token timing, KV-cache utilization, and service health in a conventional infrastructure setup. SGLang also supports Prometheus metrics, request tracing, benchmarking, and profiling documentation. vLLM currently feels like the broader production serving layer; SGLang feels like a fast-moving runtime system with ambitious optimizations for complex LLM applications.