Llama Stack vs SGLang
A side-by-side comparison of two self-hosted self-hosted ai options — licensing, setup difficulty, resource needs, and what each one replaces.
| Feature | Llama Stack | SGLang |
|---|---|---|
| Deploy effort | Under-an-hour setup | Under-an-hour setup |
| Health score | 92 · Excellent | 99 · Excellent |
| Category | Self-Hosted AI | Self-Hosted AI |
| License | MIT | Apache-2.0 |
| Language | Python | Python |
| Setup difficulty | Medium | Hard |
| Min. RAM | 4,096 MB | 16,384 MB |
| Deployment | docker, source | docker, kubernetes, bare-metal |
| GitHub stars | ★ 8,437 | ★ 36,383 |
| First released | 2024 | 2024 |
| Replaces | OpenAI API | OpenAI API |
What are Llama Stack and SGLang?
Llama Stack
Llama Stack from Meta defines and implements a set of standardized APIs for inference, RAG, agents, safety and evaluation, with multiple provider backends. It can be self-hosted as a unified server for building local generative AI applications.
- Standardized AI APIs
- Pluggable provider backends
- Agents and RAG support
- Self-hostable server
SGLang
SGLang is a high-performance serving framework for large language and vision-language models. It features a fast runtime with RadixAttention and a flexible programming language for complex LLM applications.
- RadixAttention caching
- Structured generation
- OpenAI-compatible server
- Multi-GPU scaling
Llama Stack vs SGLang: key differences
Both projects are written in Python. Licensing differs — MIT for Llama Stack versus Apache-2.0 for SGLang. Llama Stack is the lighter option, starting around 4,096 MB of RAM against 16,384 MB for SGLang. SGLang has the considerably larger community, at 36,383 GitHub stars versus 8,437.
Why pick each one
Choose Llama Stack if…
- Released under the MIT license
- First-class Docker support for quick deployment
- Mature project with 8.4k GitHub stars
- Written in Python
Choose SGLang if…
- Very high throughput
- RadixAttention prefix caching
- Vision model support
Watch out for
- Serious GPU required
- Complex tuning options
Frequently asked questions
Is Llama Stack or SGLang better?
Neither is universally better. SGLang has the larger community, while Llama Stack is simpler to set up (medium difficulty). Choose based on the comparison table above and your own setup.
Are Llama Stack and SGLang free and open-source?
Yes. Llama Stack is licensed under MIT and SGLang under Apache-2.0. Both can be self-hosted at no software cost.
Can I run Llama Stack and SGLang with Docker?
Llama Stack: yes. SGLang: yes.
Which is lighter on resources, Llama Stack or SGLang?
Llama Stack has the smaller minimum footprint at 4,096 MB of RAM, compared to about 16,384 MB for SGLang. Real-world usage depends on library size, user count, and enabled features.