SGLang vs vLLM
| Feature | SGLang | vLLM |
|---|---|---|
| Category | LLMs & AI Infra | LLMs & AI Infra |
| Sub-category | LLM Serving | LLM Serving |
| Maturity | stable | stable |
| Complexity | advanced | advanced |
| Performance tier | enterprise grade | enterprise grade |
| License | Apache-2.0 | Apache-2.0 |
| License type | permissive | permissive |
| Pricing | fully free | fully free |
| GitHub stars | 8.0K | 45.0K |
| Contributors | 150 | 600 |
| Commit frequency | daily | daily |
| Plugin ecosystem | none | none |
| Docs quality | good | good |
| Backing org | UC Berkeley | UC Berkeley / vLLM Team |
| Funding model | vc_backed | vc_backed |
| Min RAM | 8 GB | 8 GB |
| Min CPU cores | 4 | 4 |
| Scaling pattern | horizontal | horizontal |
| Self-hostable | Yes | Yes |
| K8s native | Yes | Yes |
| Offline capable | No | No |
| Vendor lock-in | none | none |
| Languages | Python | Python, C++, CUDA |
| API type | REST | REST |
| Protocols | HTTP | HTTP |
| Deployment | pip, docker | pip, docker |
| SDK languages | python | python |
| Team size fit | small, medium, enterprise | small, medium, enterprise |
| First release | 2024 | 2023 |
| Latest version | — | — |
When to use SGLang
- ✓ Structured JSON output from LLMs at scale
- ✓ Vision-language model serving
- ✓ Prefix caching for repeated prompt patterns
When to use vLLM
- ✓ Serve LLMs in production with high throughput
- ✓ Multi-model serving for AI gateway
- ✓ Batch inference for document processing
- ✓ Low-latency chatbot backend
SGLang anti-patterns
- ✕ Newer project — less battle-tested
- ✕ Smaller community than vLLM
- ✕ Documentation still maturing
vLLM anti-patterns
- ✕ Requires GPU - no CPU-only mode
- ✕ Complex setup compared to Ollama
- ✕ Not for single-user local development