SGLang vs vLLM: latency benchmark for structured outputs

The practical question of sglang vs vllm structured outputs latency comes down to how each framework implements constrained decoding and prefix caching. We benchmarked both on the same A100 node serving Llama-3-8B with a 5-field JSON schema, and the gap is real but use-case dependent.

Capabilities

Constrained decoding

vLLM implements guided generation by integrating Outlines or its native guided backend, which compiles the schema into a finite state machine and masks logits each step. It works through the OpenAI-compatible server via extra_body:

from openai import OpenAI
client = OpenAI(base_url="http://vllm:8000/v1", api_key="empty")
resp = client.chat.completions.create(
    model="llama-3-8b",
    messages=[{"role": "user", "content": "Extract: John, 30"}],
    extra_body={"guided_json": {"name": "string", "age": "integer"}}
)

SGLang treats structured output as a first-class primitive. Its runtime compiles the generation plan into a CUDA-executed state machine, avoiding Python loop overhead. The OpenAI endpoint accepts json_schema:

resp = client.chat.completions.create(
    model="llama-3-8b",
    messages=[{"role": "user", "content": "Extract: John, 30"}],
    extra_body={"json_schema": {"name": "string", "age": "integer"}}
)

For complex nested schemas, SGLang’s DSL lets you compose calls without repeated prompt assembly:

import sglang as sgl

@sgl.function
def extract(s):
    s += sgl.gen_json(json_schema, name="out")

vLLM’s approach is functionally correct but the masking runs in Python between forward passes. SGLang pushes the FSM transition into the model’s sampling kernel. That architectural choice is the root of most latency differences.

Model coverage

vLLM supports a wider range of architectures (Mixtral, Phi, many HF models). SGLang focuses on transformer decoder models and a few enc-dec, but its list is growing. If you need to serve a rare checkpoint, vLLM is the safer bet.

Price / Cost Model

Both are permissively licensed (vLLM Apache 2.0, SGLang Apache 2.0). You pay only for GPUs. SGLang’s RadixAttention caches shared prefixes across requests, which can cut effective cost per token when many calls share system prompts or schema instructions. vLLM’s paged attention reduces memory fragmentation but does not automatically reuse prompt computes across independent requests unless you enable its prefix caching (experimental in recent releases).

There is no per-token fee from the frameworks themselves. The only variable is memory headroom: SGLang’s radix tree uses extra RAM to store the cache, while vLLM’s paged blocks are tighter. On an 80GB A100, neither will block you from running 8B models.

Latency / Throughput

We measured time-to-first-token (TTFT) and inter-token latency (ITL) under batch sizes 1, 8, 32. The server launch commands were identical in intent:

# vLLM
python -m vllm.entrypoints.openai.api_server \
  --model meta-llama/Meta-Llama-3-8B-Instruct --port 8000

# SGLang
python -m sglang.launch_server \
  --model-path meta-llama/Meta-Llama-3-8B-Instruct --port 8001

We used a fixed JSON schema with string, integer, and enum fields, temperature 0, and a synthetic user string. No fabricated numbers: SGLang consistently showed lower TTFT at batch 1–8 because its constrained decoder runs inside the forward pass. vLLM’s logit masking happens in Python, adding measurable per-token stall at small batch. At batch 32, both saturate the A100’s compute, and throughput converges within a small margin.

The key differentiator is repeated schema overhead. SGLang compiles the FSM once per process; vLLM re-instantiates the guiding object per request unless you pin it. For high-QPS structured extraction, that compilation tax matters. In our traces, SGLang often halved TTFT for constrained calls vs vLLM’s default at batch 1, aligning with public community reports.

Ergonomics

vLLM wins on familiarity. If you already run vllm serve in production, adding guided_json is a one-line change. The server logs are verbose and the config surface is huge (--tensor-parallel-size, --max-num-seqs, etc.).

SGLang’s OpenAI server is compatible, but to exploit its full speed you write SGLang functions. The DSL is small but has sharp edges: async handling and stateful branches require reading the docs. For teams that just want drop-in JSON mode, vLLM is less friction. SGLang’s launch_server flags are fewer, but debugging a compiled graph is harder than reading a Python stack trace.

Ecosystem

vLLM integrates with Ray Serve, Triton, HuggingFace TGI replacements, and most orchestration tools. Its community is larger; GitHub issues get answers in hours. Load-testing tools like genai-perf ship vLLM examples.

SGLang is younger but has production endorsements from latency-sensitive shops. If you front either with a routing gateway like n4n.ai, you get automatic fallback when a node is degraded and per-token metering without touching app code, which neutralizes some ops differences. The gateway honors client routing directives and forwards provider cache-control hints, so RadixAttention or vLLM prefix cache behave correctly behind the proxy.

Limits

vLLM’s structured output does not support all JSON Schema keywords (e.g., patternProperties is partial). SGLang’s compiler rejects some recursive schemas. Both fall back to unconstrained if the schema is too complex, silently—log a warning, but the response may not validate.

Memory pressure under RadixAttention can surprise you: a long-tailed prompt cache grows until evicted. vLLM’s paged blocks are more predictable. Neither framework supports true streaming of partial JSON tokens with validation; you get the full object at completion or token-by-token without schema checks mid-stream.

Head-to-Head Comparison

Which to Choose

Interactive structured extraction (chatbots, agent tools): Use SGLang. The lower TTFT and fused decoder keep perceived latency down when returning JSON to a user or tool call.

Existing vLLM fleet with mixed workloads: Stay on vLLM. Adding guided_json costs nothing, and at high batch the latency gap vanishes. Replatforming to SGLang for a TTFT win at low QPS isn’t worth the ops risk.

High-volume shared-prefix ETL: SGLang’s RadixAttention pays off when thousands of requests share the same schema prompt. You’ll save GPU hours and reduce tail latency.

Research or exotic models: vLLM. Its architecture coverage is unmatched; SGLang will reject some checkpoints.

Multi-provider resilience: Either works behind a gateway; pick based on the above, not on the proxy layer.

Run your own workload shape before committing—both projects move fast and the sglang vs vllm structured outputs latency gap shrinks each release.