SGLang vs vLLM: which LLM serving framework should you choose?
Use SGLang if you need structured output constraints or complex prompt/generation workflows. Use vLLM if you prioritize raw throughput and OpenAI API compatibility.
VERDICT
Side-by-side comparison
| Feature | SGLang | vLLM | Winner |
|---|---|---|---|
| Throughput (7B model, A100) | ~1,200 tok/s (with constraints) | ~2,000 tok/s | vLLM |
| Structured output support | Native (Regex/JSON/CFG) | Plugin-based (Outlines) | SGLang |
| OpenAI API compatibility | Partial (custom /generate endpoint) | Full (/v1/chat/completions) | vLLM |
| Multi-turn conversation support | Language-native (SGLang DSL) | Basic (message history) | SGLang |
| Installation complexity | pip install sglang | pip install vllm | Tie |
| Model support | Llama, Qwen, Mistral, Deepseek | 1000+ HuggingFace models | vLLM |
| Production readiness | Active development (2025) | Battle-tested production | vLLM |
| Research-friendly | Designed for prompting research | Inference optimization focus | SGLang |
Performance benchmarks
Throughput (Llama 2 7B, A100, 128 batch size)
SGLang slower due to constraint checking; vLLM uses pure continuous batching. Both reach higher throughput without output constraints.
Time-to-first-token (7B model, A100)
vLLM's RadixAttention is slightly faster; SGLang overhead from structured parsing is minimal at scale.
Memory (7B model, fp16)
Both use similar memory footprint with continuous batching; quantization support is identical.
Structured output constraint overhead
SGLang's constraints are tighter but more intuitive; vLLM's Outlines is more experimental.
When to use each
- ✓ You need JSON or regex-constrained output without post-processing: SGLang enforces it at generation time, eliminating invalid outputs
- ✓ Building a multi-turn conversational system with complex state management: SGLang's native DSL makes prompt chaining trivial
- ✓ Running research experiments on prompting techniques: SGLang is built by researchers for iterative prompt engineering
- ✓ You need function calling with guaranteed schema adherence: SGLang's structured generation is tighter than Outlines
- ✓ Deployments where structured output validation is non-negotiable (data extraction, form filling, API generation)
- ✓ Serving 100+ concurrent users where throughput is critical: vLLM's continuous batching gives 3-5x better batch efficiency
- ✓ You need a drop-in OpenAI API replacement without client code changes: vLLM's /v1/chat/completions is fully compatible
- ✓ Production deployments with established DevOps / scaling frameworks: vLLM has proven deployment patterns and battle-tested stability
- ✓ Supporting any HuggingFace model without reimplementation: vLLM's model coverage is broader and more stable
- ✓ You prioritize maximum throughput-per-GPU-dollar for cost-sensitive inference (cost per 1M tokens)
Common misconceptions
SGLang
SGLang is a complete replacement for vLLM
SGLang is built on top of vLLM's inference engine: it adds a constraints layer, not a replacement. You're trading throughput for output safety.
SGLang's structured output eliminates all post-processing
Regex/JSON constraints prevent malformed output, but schema validation (e.g., required fields, type coercion) still requires downstream logic.
SGLang is production-ready for large-scale serving
SGLang is actively developed (2025) but less battle-tested at >1000 concurrent requests. vLLM has longer production track record.
vLLM
vLLM has built-in structured output support
vLLM's structured output is via the Outlines plugin, which is experimental and doesn't guarantee format adherence the way SGLang does.
vLLM is optimized for prompting research workflows
vLLM is optimized for inference speed, not prompt management. Multi-turn conversations require external state management or prompt template libraries.
vLLM's OpenAI compatibility means zero code changes
vLLM matches the API surface, but batching behavior, token limits per request, and error handling differ from OpenAI's hosted API.
Code examples
Task: Generate a JSON object with constrained fields (name, age, city) from unstructured text
from sglang import function, gen, set_default_backend
from sglang.srt.constrained import Regex
@function
def extract_person(s):
s += "Extract person info from: Alice is 28 and lives in NYC\n"
# SGLang enforces JSON schema at generation time
s += "Return valid JSON: {\"name\": ..., \"age\": <int>, \"city\": ...}\n"
s += gen("output", regex=r'{"name": "[^"]+", "age": \d+, "city": "[^"]+"}')
return s["output"]
set_default_backend("local", model_path="meta-llama/Llama-2-7b-hf")
result = extract_person()
print(result) # Guaranteed valid JSON: no post-processing neededSGLang's regex constraint guarantees well-formed JSON output at generation time; no invalid JSON can be produced regardless of model randomness.
from vllm import LLM, SamplingParams
import json
import re
llm = LLM(model="meta-llama/Llama-2-7b-hf")
sampling_params = SamplingParams(temperature=0.7, max_tokens=100)
prompt = """Extract person info from: Alice is 28 and lives in NYC
Return valid JSON: {\"name\": ..., \"age\": <int>, \"city\": ...}"""
output = llm.generate([prompt], sampling_params)[0]
raw_text = output.outputs[0].text
# vLLM does not enforce constraints; post-processing required
try:
result = json.loads(raw_text)
print(result)
except json.JSONDecodeError:
# Handle invalid JSON: extract manually or retry
print(f"Invalid JSON: {raw_text}")vLLM generates text freely without output constraints; JSON validation must be handled downstream, risking parse failures and retries.
Migration path
- Switching from vLLM to SGLang:
- Install: pip install sglang.
- Replace LLM initialization: from vllm import LLM becomes from sglang import set_default_backend.
- Convert prompts to SGLang DSL: replace llm.generate() with @function-decorated functions.
- Add constraints: use gen(..., regex=...) or gen(..., json_schema=...) for structured output.
- API compatibility: vLLM's /v1/chat/completions becomes SGLang's /generate endpoint (different client expectations). Switching from SGLang to vLLM:
- Remove @function decorators and regex constraints.
- Replace gen() calls with simple string concatenation for prompts.
- Use vllm.LLM instead of sglang backend.
- Add Outlines import for constrained generation if needed: from outlines import models.
- Migrate API: SGLang's /generate becomes OpenAI-compatible /v1/chat/completions. Full migration typically requires rewriting prompt templates (1-2 hours for small codebases).
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