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SGLang

Open-source serving framework for LLMs and multimodal models, with OpenAI-compatible endpoints and multi-GPU deployment.

Open source
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What is SGLang?

SGLang is an open-source inference engine for running large language, vision-language and diffusion models in production. You install it with pip, uv or Docker, start a server with a single command that points to a model, and query it through OpenAI-compatible endpoints. It scales from one accelerator to distributed clusters and applies optimizations such as disaggregated prefill and decode, speculative decoding, several kinds of parallelism, a zero-overhead scheduler and tuned GPU kernels. Hardware support listed on the site covers NVIDIA and AMD GPUs, Google TPU, CPU servers, Ascend NPUs and XPU, and the model list includes DeepSeek, Qwen, GPT-OSS, Llama, Mistral and GLM families plus embedding, reward and classification models. A separate SGLang Model Gateway routes traffic across workers over HTTP, gRPC and OpenAI-compatible backends. The project is released under the Apache-2.0 license, so there is no license fee, but you supply the GPU capacity and operations skills.

Use cases

  • Serve open LLMs at scale
  • Run vision-language models
  • Generate images and video with diffusion models
  • Route traffic across a model fleet
  • Deploy on Kubernetes or Docker
  • Host embedding and reward models

Who it's for

  • ML engineers
  • Infrastructure teams
  • AI researchers
  • Platform engineers

Why use SGLang

  • It is free to use under an open-source license, so cost comes from hardware rather than software fees.
  • OpenAI-compatible endpoints let existing client code switch to a self-hosted model with little change.
  • Release notes report frequent throughput gains for new models and chips, such as DeepSeek on H200 and AMD hardware.
  • The same engine covers language, vision-language, diffusion, embedding and reward models.
  • The model gateway adds routing, retries and rate limiting for multi-worker deployments.

SGLang features

  • OpenAI-compatible API endpoints
  • Disaggregated prefill and decode serving
  • Speculative decoding
  • Zero-overhead scheduler and optimized GPU kernels
  • Support for LLM, vision-language and diffusion models
  • Embedding, reward and classification model support
  • NVIDIA, AMD, TPU, CPU, Ascend NPU and XPU hardware
  • SGLang Model Gateway with load balancing and circuit breakers
  • Install via pip, uv, Docker or from source
  • Prometheus and OpenTelemetry observability in the gateway

SGLang integrations

  • Hugging Face
  • Kubernetes
  • Prometheus
  • OpenTelemetry
  • PostgreSQL
  • Oracle ATP
  • AWS EFA

How to get started with SGLang

  1. Install SGLang with pip, uv or the lmsysorg/sglang Docker image.
  2. Launch the server with one command that points at your model.
  3. Query it through the OpenAI-compatible endpoints from your application.
  4. Scale out with parallelism, prefill/decode disaggregation or the Model Gateway for larger fleets.

SGLang limitations

  • Requires your own GPU or accelerator capacity
  • Current install docs target Linux with NVIDIA GPUs and CUDA 13
  • Running it in production needs infrastructure expertise

SGLang pricing

SGLang is open source, so you can use and self-host it for free.

Prices can change and may vary by country or exclude tax. Check the official pricing page.

SGLang alternatives

The main SGLang alternatives listed on Debuly are vLLM, llama.cpp, Ollama, DeepInfra, Vast.ai and Replicate. The table compares pricing and platforms; order is A–Z.

SGLang compared with its alternatives
ToolPricingStarting pricePlatforms
SGLangOpen source—Linux, Docker, API, Self-hosted
DeepInfraPaid—API, Web
llama.cppOpen source—macOS, Windows, Linux, CLI
OllamaOpen source$20/momacOS, Windows, Linux, API
ReplicatePaid$0.09, usage-basedAPI, Web, Python, Node.js
Vast.aiPaid—Web, API, CLI
vLLMOpen source—Docker, Python, API, Self-hosted

  1. DeepInfra

    AI inference cloud with an OpenAI-compatible API for 100+ open models, plus private GPU deployments, GPU rental and hosted agents.

    Developer platformsCodingAutomation and agents
    PaidVisit DeepInfra (official site)

  2. llama.cpp

    Open-source engine and desktop app for running LLMs locally, with a built-in OpenAI-compatible API server and web chat UI.

    Developer platformsCodingChat assistants
    Open sourceVisit llama.cpp (official site)

  3. Ollama

    Open-source app and CLI for running open AI models locally on macOS, Windows and Linux, with optional paid cloud models.

    Developer platformsCodingChat assistants
    Open sourceVisit Ollama (official site)

  4. Replicate

    Cloud API for running, fine-tuning and deploying open-source and proprietary AI models, billed by compute time or by output.

    Developer platformsImage generationVideo
    PaidVisit Replicate (official site)

  5. Vast.ai

    GPU cloud marketplace for renting on-demand, interruptible or reserved GPUs with per-second billing.

    Developer platformsAutomation and agents
    PaidVisit Vast.ai (official site)

  6. vLLM

    Open-source engine for serving LLMs with PagedAttention, continuous batching and an OpenAI-compatible API.

    Developer platformsCoding
    Open sourceVisit vLLM (official site)

SGLang FAQ

How much does SGLang cost?

SGLang itself is free and has no published plans or prices. You pay for the GPUs or cloud instances that run it.

Is SGLang open source?

Yes. It is an Apache-2.0 project maintained by the sgl-project community and published on PyPI, with Docker images on Docker Hub.

Which models does SGLang support?

The site lists DeepSeek, Qwen, GPT-OSS, Llama, Mistral and GLM families, plus vision-language, diffusion, embedding, classification and reward models.

Which hardware does SGLang run on?

NVIDIA and AMD GPUs, Google TPU, CPU servers, Ascend NPUs and XPU are listed. The main install guide targets Linux with NVIDIA GPUs and CUDA 13.

Does SGLang have an API?

Yes. It serves OpenAI-compatible endpoints, and the Model Gateway adds inference, tokenization, classification and worker-management APIs.

What are alternatives to SGLang?

Common alternatives for self-hosted LLM inference include vLLM, TensorRT-LLM, Hugging Face Text Generation Inference and llama.cpp.

Sources & verification

Facts on this page come from the official sources below, each with the date we read it. Written by Debuly; the vendor did not review this page.

SourceTypeAccessed
sglang.ioofficial
github.com/sgl-project/sglangrepository
sglang.io/official
docs.sglang.io/docs/advanced_features/overviewdocs
docs.sglang.io/docs/supported-modelsdocs
docs.sglang.io/docs/get-started/installdocs
docs.sglang.io/docs/advanced_features/sgl_model_gatewaydocs

Image credits

  • Logo: from sglang.io · fetched · Vendor trademark, used only to identify the product; removed on request
  • Cover: none collected yet, so a generated tile is shown.

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