What is SGLang used for?

SGLang is an open-source framework for serving language, vision-language and diffusion models, built for fast inference at scale. It is a good fit for self-hosting open-weight models in production, serving agent workloads at scale and running reinforcement-learning rollouts.

Is SGLang open source?

Yes. SGLang is open source under the Apache-2.0 licence. Its source code is on GitHub at sgl-project/sglang and is written mainly in Python.

Yes. SGLang is open source, so the software itself is free to use.

Can I self-host SGLang?

Yes. SGLang can be self-hosted on your own server or infrastructure; there is no official hosted version.

What is SGLang an alternative to?

SGLang is an open-source alternative to Amazon Bedrock, Amazon SageMaker, Replicate and Together AI. Other open-source alternatives to Amazon Bedrock include vLLM.

Is SGLang actively maintained?

Yes. The most recent commit to SGLang was on 2 October 2026, and the latest release is v0.5.21, published on 2 October 2026. The project has 37k stars on GitHub.

SGLang: open-source alternative to Amazon Bedrock

About SGLang

SGLang is an inference framework, released as open source, for language models, vision-language models and diffusion models. It is optimized for agentic workloads, reinforcement-learning rollouts and large-scale serving. The project includes SGLang Diffusion, a built-in engine for image and video generation that ships in the same repository and Python package. It is released under the Apache-2.0 license.

Getting started takes either a prebuilt Docker image or a Python install using uv, followed by a launch command for the chosen model. A cookbook helps pick a model and hardware pair and generates a ready-to-run command. Supported hardware spans NVIDIA and AMD GPUs, Google TPUs, Intel GPUs and CPUs, Apple Silicon through Metal and MLX, and Huawei Ascend NPUs, with more integrations in progress. The wider SGLang ecosystem adds educational projects and community events.

Key features

Serving for LLMs, vision-language and diffusion models
Docker image and uv-based Python install
Cookbook with ready-to-run launch commands
Runs on NVIDIA, AMD, TPU, Intel and Ascend hardware
Built-in image and video generation engine
Tuned for agentic and RL rollout workloads

Good fit for

→Self-hosting open-weight models in production
→Serving agent workloads at scale
→Running reinforcement-learning rollouts

Built with: Python
Tags: llm; inference; model-serving; gpu; python; multimodal; diffusion; ai-infrastructure

SGLang: questions and answers

What is SGLang used for?: SGLang is an open-source framework for serving language, vision-language and diffusion models, built for fast inference at scale. It is a good fit for self-hosting open-weight models in production, serving agent workloads at scale and running reinforcement-learning rollouts.
Is SGLang open source?: Yes. SGLang is open source under the Apache-2.0 licence. Its source code is on GitHub at sgl-project/sglang and is written mainly in Python.
Is SGLang free?: Yes. SGLang is open source, so the software itself is free to use.
Can I self-host SGLang?: Yes. SGLang can be self-hosted on your own server or infrastructure; there is no official hosted version.
What is SGLang an alternative to?: SGLang is an open-source alternative to Amazon Bedrock, Amazon SageMaker, Replicate and Together AI. Other open-source alternatives to Amazon Bedrock include vLLM.
Is SGLang actively maintained?: Yes. The most recent commit to SGLang was on 2 October 2026, and the latest release is v0.5.21, published on 2 October 2026. The project has 37k stars on GitHub.