Table of Contents
SGLang (LMSYS, 2024) is a serving framework with a unique frontend language and aggressive optimisations for structured / multi-step generation. By April 2026, it's a credible alternative to vLLM for specific workloads. Most production deployments still default to vLLM; SGLang wins for specific patterns.
vLLM: production default, broadest ecosystem, OpenAI-compatible, mature tooling. SGLang: faster on structured outputs (~2-3×), faster on agentic / multi-step workloads, frontend language for complex generation patterns. For most chat / RAG production: vLLM. For agent loops + structured outputs at scale: SGLang.
Comparison
| Aspect | vLLM | SGLang |
|---|---|---|
| Production maturity | Highest | Mature |
| Throughput on chat | Fast | Fast |
| Throughput on structured outputs | Fast | ~2-3× faster |
| Throughput on agent loops | Fast | ~2× faster |
| OpenAI API compatibility | Yes | Yes |
| Custom frontend language | No | Yes (SGLang program) |
| Ecosystem | Broadest | Growing |
| Multi-LoRA serving | Yes | Yes |
When each
- vLLM wins for: chatbots, RAG, simple completion APIs, OpenAI-compatible drop-in replacement, ecosystem maturity
- SGLang wins for: structured output at scale, multi-step agent loops, RadixAttention prefix caching, complex generation programs
- TensorRT-LLM still wins for: max throughput on Hopper / Blackwell at large scale, specific NVIDIA-stack deployments
Verdict
For most production self-hosted AI in 2026, vLLM remains the default. SGLang is worth evaluating specifically when your workload is structured-output-heavy or agent-heavy — the throughput advantage is real and meaningful. Most teams: vLLM as primary, SGLang for specific routes / agent backends if measured benefit.
Bottom line
vLLM default; SGLang for structured / agent. See three-way comparison.
