Use Cases & Case Studies

Beginner – Intermediate

Should You Use SGLang?

Decision Tree

Do your requests share common prefixes (system prompts, few-shot examples, multi-turn history)?

When to Use It

Multi-turn conversation services

RadixAttention caches conversation history. Each new message only processes new tokens, giving ~10% throughput advantage over vLLM, growing larger with longer conversations.

Structured output at scale

Integrated grammar backends (XGrammar, Outlines) enforce JSON, code, or custom formats at the token level with minimal overhead via GPU bitmask operations.

Few-shot and system-prompt-heavy workloads

1,000 requests sharing a 4,000-token system prompt? The prompt is processed once and cached. Subsequent requests skip to the unique portion.

Agentic AI workflows

The SGLang DSL's fork() and gen() primitives express branching logic (ReAct, tree-of-thought) so the runtime can optimize across branches.

High-throughput batch inference

Continuous batching and cache-aware scheduling maximize GPU utilization, delivering up to 45% more value per GPU hour than standard deployments.

When NOT to Use It

Not ideal for these scenarios Single-shot, extreme-TTFT workloads: vLLM's C++ routing may offer lower TTFT. Small models on CPU: Use llama.cpp or Ollama instead. Quick prototyping: transformers + model.generate() is simpler. No prefix reuse: RadixAttention provides no benefit; consider --disable-radix-cache.

Real-World Deployments

xAI (Grok)

Uses SGLang to serve Grok models at scale. Leverages efficient multi-GPU serving and RadixAttention for conversational workloads. Expert parallelism support is critical for MoE architectures.

Cursor (AI Code Editor)

Powers real-time code completion. Code editing context creates natural prefix sharing that RadixAttention exploits. Constrained decoding ensures syntactically valid code.

LinkedIn

Deploys SGLang for AI features across the platform. Benefits from production-grade serving and OpenAI-compatible API for drop-in replacement.

NVIDIA, AMD, Intel

All three GPU vendors integrate SGLang. NVIDIA provides official container images; AMD contributes ROCm-optimized kernels.