TokenMix Research Lab · 2026-04-30

SGLang OpenAI-Compatible API 2026: Server Setup And Cost Guide

Last Updated: 2026-04-30
Author: TokenMix Research Lab
Data checked: 2026-04-30

SGLang exposes OpenAI-compatible endpoints for self-hosted models. Use it when throughput, structured generation, and GPU control matter.

SGLang's OpenAI API docs say it provides OpenAI-compatible APIs for chat/completions and completions, with the server applying a Hugging Face tokenizer chat template when available. The newer backend docs also describe /v1/chat/completions, /v1/completions, embeddings, model listing, health checks, API-key auth, structured outputs, and SGLang-specific extensions. The short version: SGLang is a strong OpenAI-compatible serving runtime. It is not a hosted API gateway by itself.

Table of Contents

• Quick Answer
• Confirmed vs Caveat
• Architecture Snapshot
• Install And Launch
• Python OpenAI SDK Example
• Node TypeScript Example
• Request Parameters
• SGLang Extensions
• SGLang vs TGI vs vLLM vs TokenMix.ai
• Operational Cost Math
• Production Checklist
• When To Use TokenMix.ai Instead
• Final Recommendation
• FAQ
• Related Articles
• Sources

Quick Answer

SGLang OpenAI-compatible API means you can launch an SGLang server and call it with OpenAI SDK clients:

http://localhost:30000/v1/chat/completions

The common base URL is:

http://localhost:30000/v1

Use SGLang if you are serving open models on your own GPUs and care about low latency, high throughput, reasoning parsers, structured output, LoRA, MoE routing visibility, or production inference tuning. Use TokenMix.ai if you want one OpenAI-compatible API across hosted model providers without operating SGLang clusters.

Confirmed vs Caveat

Architecture Snapshot

SGLang is a production inference framework. TokenMix.ai is an API access and routing layer. Do not confuse the layers.

Install And Launch

SGLang's installation docs recommend uv for faster installs on common NVIDIA GPU platforms:

pip install --upgrade pip
pip install uv
uv pip install sglang

Launch a local server:

python3 -m sglang.launch_server \
  --model-path meta-llama/Llama-3.1-8B-Instruct \
  --host 0.0.0.0 \
  --port 30000

Docker path:

docker run --gpus all \
  --shm-size 32g \
  -p 30000:30000 \
  -v ~/.cache/huggingface:/root/.cache/huggingface \
  --env "HF_TOKEN=<secret>" \
  --ipc=host \
  lmsysorg/sglang:latest-runtime \
  python3 -m sglang.launch_server \
    --model-path meta-llama/Llama-3.1-8B-Instruct \
    --host 0.0.0.0 \
    --port 30000

Python OpenAI SDK Example

pip install openai

from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:30000/v1",
    api_key="EMPTY",
)

response = client.chat.completions.create(
    model="meta-llama/Llama-3.1-8B-Instruct",
    messages=[
        {"role": "system", "content": "You are a concise API assistant."},
        {"role": "user", "content": "Explain SGLang in one sentence."},
    ],
    temperature=0.3,
    max_tokens=128,
)

print(response.choices[0].message.content)

Streaming:

stream = client.chat.completions.create(
    model="meta-llama/Llama-3.1-8B-Instruct",
    messages=[{"role": "user", "content": "List three SGLang use cases."}],
    stream=True,
)

for chunk in stream:
    delta = chunk.choices[0].delta.content
    if delta:
        print(delta, end="")

Node TypeScript Example

npm install openai

import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "http://localhost:30000/v1",
  apiKey: "EMPTY",
});

const response = await client.chat.completions.create({
  model: "meta-llama/Llama-3.1-8B-Instruct",
  messages: [
    { role: "system", content: "You are a concise API assistant." },
    { role: "user", content: "Explain SGLang OpenAI compatibility." },
  ],
  temperature: 0.3,
  max_tokens: 128,
});

console.log(response.choices[0].message.content);

With auth enabled:

python3 -m sglang.launch_server \
  --model-path meta-llama/Llama-3.1-8B-Instruct \
  --api-key your-secret-key

Then set apiKey: "your-secret-key" in the client.

Request Parameters

SGLang's docs say the chat completions API accepts OpenAI Chat Completions parameters and extends the standard API through extra_body.

SGLang Extensions

This is where SGLang becomes more than a plain OpenAI clone.

Reasoning example:

response = client.chat.completions.create(
    model="Qwen/Qwen3-4B",
    messages=[{"role": "user", "content": "How many r's are in strawberry?"}],
    extra_body={
        "chat_template_kwargs": {"enable_thinking": True},
        "separate_reasoning": True,
    },
)

print(response.choices[0].message.reasoning_content)
print(response.choices[0].message.content)

LoRA example shape:

response = client.chat.completions.create(
    model="qwen/qwen2.5-0.5b-instruct:adapter_a",
    messages=[{"role": "user", "content": "Convert this request into SQL."}],
    max_tokens=50,
)

SGLang vs TGI vs vLLM vs TokenMix.ai

If your team is choosing among SGLang, TGI, and vLLM, the question is runtime ownership. If your team is choosing TokenMix.ai, the question is provider access and API gateway speed.

Operational Cost Math

Like TGI and vLLM, SGLang cost is mostly GPU utilization and engineering time.

The $2/hour number is an example, not a market quote. Use your actual cloud/GPU pricing and measured throughput.

Cost decision:

Production Checklist

When To Use TokenMix.ai Instead

Use TokenMix.ai when your application wants model choice, not inference-server ownership.

SGLang is the right tool when you own inference. TokenMix.ai is the right tool when you want a unified API over many model providers.

Final Recommendation

Use SGLang OpenAI-compatible API for production self-hosted inference where you need throughput, runtime control, structured generation, or specialized model support.

Use TokenMix.ai when the priority is one OpenAI-compatible endpoint, model choice, fallback, and lower operational burden.

FAQ

Does SGLang have an OpenAI-compatible API?

Yes. SGLang provides OpenAI-compatible APIs for chat completions and completions.

What base URL should I use?

For a local server, use http://localhost:30000/v1 as the OpenAI SDK base URL.

What endpoint handles chat?

Use POST /v1/chat/completions.

How do I launch an SGLang server?

Use python3 -m sglang.launch_server --model-path <model> --host 0.0.0.0 --port 30000, or run the official Docker image.

Does SGLang support streaming?

Yes. The chat completions API supports streaming responses.

Does SGLang support structured outputs?

Yes. SGLang documents structured outputs with JSON, regex, and EBNF constraints.

Is SGLang the same as TokenMix.ai?

No. SGLang is an inference serving runtime. TokenMix.ai is an OpenAI-compatible API gateway and model access layer.

Should I use SGLang or a hosted API?

Use SGLang if you need to own the runtime and can operate GPUs. Use a hosted API or TokenMix.ai if you need faster launch and less infrastructure work.

Related Articles

• OpenAI-Compatible API Guide 2026: One SDK, Many Models
• Text Generation Inference OpenAI-Compatible API 2026 Guide
• Ollama OpenAI-Compatible API: Local Setup and Hosted Alternatives
• Gemini OpenAI-Compatible API: Use Gemini with the OpenAI SDK
• Anthropic OpenAI-Compatible API 2026: Claude SDK Setup Guide
• LLM API Gateway 2026: Routing, Fallbacks, Spend Control
• OpenRouter Alternatives 2026: Pricing, Models, Payments

Sources

• SGLang OpenAI APIs - Completions: https://docs.sglang.io/docs/basic_usage/openai_api_completions
• SGLang OpenAI Compatible API backend docs: https://sgl-project-sglang-93.mintlify.app/backend/openai-compatible-api
• SGLang installation docs: https://docs.sglang.io/docs/get-started/install
• SGLang homepage: https://docs.sglang.io/
• SGLang supported language models: https://docs.sglang.io/supported_models/generative_models.html