SGLang vs 
OpenLLM
SGLang vs OpenLLM compared for 2026 — features, license, ease of use, performance and which one to choose. Fast serving with structured outputs vs Serve any open model as an OpenAI API in one command.
Updated regularly · curated by OpenSourceAI.tech
SGLang vs OpenLLM at a glance
| Spec | SGLang | OpenLLM |
|---|---|---|
| Category | Inference server | Inference server |
| Type | Inference server | Serving framework |
| License | Apache-2.0 | Apache-2.0 |
| Runs locally | Self-hosted | Yes |
| Primary language | Python | Python |
| Ease of use | Advanced | Beginner |
| Best for | teams needing structured-output serving | going from model name to production endpoint fast |
| GitHub stars | 30.2k | 12.4k |
Feature comparison
| Feature | SGLang | OpenLLM |
|---|---|---|
| OpenAI-compatible API | ✓ | ✓ |
| Continuous batching | ✓ | ✓ |
| Quantization | ✓ | ✓ |
| Multi-GPU | ✓ | ✓ |
| Structured output | ✓ | ✗ |
| Docker | ✓ | ✓ |
How SGLang and OpenLLM score
| Criterion | SGLang | OpenLLM |
|---|---|---|
| Popularity | 4.0 | 3.0 |
| Maintenance | 5.0 | 5.0 |
| Ease of use | 2.5 | 5.0 |
| Privacy | 4.5 | 5.0 |
| License freedom | 5.0 | 5.0 |
Scores are computed automatically from public signals — GitHub stars (popularity), recent commit activity (maintenance), license type (freedom), local-first design (privacy) and onboarding complexity (ease of use). Indicative, not a verdict.
What each one is
SGLang
SGLang is a fast serving framework for LLMs and vision-language models, featuring RadixAttention and strong support for structured and programmatic generation.
- Very fast with RadixAttention caching
- First-class structured / programmatic generation
- Strong vision-language model support
OpenLLM
OpenLLM by BentoML runs open models behind an OpenAI-compatible endpoint with one command, adds a chat UI, and packages everything for Docker or cloud deployment.
- One command from model to OpenAI-compatible API
- Built-in chat UI for quick testing
- Clean path to Docker and cloud deployment via BentoML
Key differences
SGLang is inference server, while OpenLLM is serving framework. SGLang leans more advanced-friendly, whereas OpenLLM is more suited to beginner users. They also differ in how they run (Self-hosted vs Yes). In short, SGLang fits teams needing structured-output serving, and OpenLLM fits going from model name to production endpoint fast.
Which should you choose?
Choose SGLang for teams needing structured-output serving. Choose OpenLLM for going from model name to production endpoint fast.
There is rarely one winner — many setups use both. The right pick depends on your hardware, your team's skills, and whether you value simplicity or control.
Frequently asked questions
Is SGLang or OpenLLM easier to use?
OpenLLM is generally the easier of the two to get started with, while SGLang rewards more setup with more control.
Are SGLang and OpenLLM free?
SGLang is free and open source (Apache-2.0), and OpenLLM is free and open source (Apache-2.0). Neither charges for the core software.
Can I run SGLang and OpenLLM locally?
SGLang: self-hosted · OpenLLM: yes. Both can be used without sending your data to a third-party cloud where their setup allows.
SGLang vs OpenLLM — which should I pick in 2026?
Choose SGLang for teams needing structured-output serving. Choose OpenLLM for going from model name to production endpoint fast.
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