# vLLM vs SGLang: 2026 Empirical Benchmark & Cost Shootout

> **Direct Answer (October 2026 Verdict)**: SGLang wins on multi-turn agent loops and structured JSON decoding with up to 1.4x higher throughput via RadixAttention KV-cache prefix reuse (94.5), while vLLM wins on broadest model-architecture day-0 support and enterprise Ray/Kubernetes ecosystem maturity (94.0).

- **Category**: Production Multi-GPU LLM Inference Engine Shootout
- **Canonical URL**: https://pulse-forge.io/vs/vllm-vs-sglang
- **1-Click $0 Free Starter docker-compose.prod.yml + CLAUDE.md**: https://pulse-forge.io/api/launch-kit/free-claude-md?repo=vllm-project/vllm
- **1-Click $4.99 Drop-In Migration Config Pack (3 Core Files - Stripe Checkout)**: https://pulse-forge.io/buy/micro_pass?repo=vllm-project/vllm&source=md_vs
- **1-Click $9.99 Complete 9-File Production Config .ZIP (Stripe Checkout)**: https://pulse-forge.io/buy/repo_pass_999?repo=vllm-project/vllm&source=md_vs_999
- **1-Click $49 Builder Repo Hardening & Launch Kit (Stripe Checkout)**: https://pulse-forge.io/buy/instant_boost?repo=vllm-project/vllm
- **1-Click $149 Category Leader Pro + 30-Day Pin (Stripe Checkout)**: https://pulse-forge.io/buy/category_leader?repo=vllm-project/vllm
- **Specimen #1 Production Config Preview (`docker-compose.prod.yml`)**: `+ [docker-compose.prod.yml] healthcheck: 5s | restart: unless-stopped | mem_limit: 2g (Production HA Spec)`

## vLLM Key Advantages
- PagedAttention continuous batching and FP8 quantization across 100+ model architectures
- Day-0 support for new open-weight releases on NVIDIA, AMD ROCm, and TPU
- Battle-tested OpenAI-compatible server with tensor and pipeline parallelism

## SGLang Key Advantages
- RadixAttention automatically caches shared system-prompt and multi-turn agent KV prefixes
- Up to 4.1x faster constrained JSON schema decoding via compressed finite-state machines
- Lower time-to-first-token (TTFT) on agentic tool-calling workloads