SGLang vs Xinference
Short answer
SGLang: Fast serving engine for LLMs and multimodal models on GPU servers, with an OpenAI-style API. Xinference: Model-serving platform for LLM, embedding, rerank, speech and image models behind one OpenAI-compatible API. Both are in our Local model runtimes category.
Choose SGLang if you want: larger community (38.3k vs 9.6k GitHub stars).
Choose Xinference if you want: no GPU server needed.
Xinference publishes no official RAM figure, so we cannot compare hosting cost directly.
Last updated · computed from each project's official docs and live GitHub data · sources
SGLang vs Xinference side by side
| SGLang | Xinference | |
|---|---|---|
| Category | Local model runtimes | Local model runtimes |
| What it is | Fast serving engine for LLMs and multimodal models on GPU servers, with an OpenAI-style API. | Model-serving platform for LLM, embedding, rerank, speech and image models behind one OpenAI-compatible API. |
| License | Apache-2.0 | Apache-2.0 |
| License type | Permissive | Permissive |
| License conditions | Permissive. Internal and commercial use allowed. Models keep their own licenses. | Permissive. Internal and commercial use allowed; keep the Apache-2.0 license and notices. Xinference Enterprise is a separate commercial edition. Models keep their own licenses. |
| Status | Active | Active |
| GitHub stars | 38.3k | 9.6k |
| Last commit | Oct 31, 2026 | Oct 31, 2026 |
| Latest release | v0.5.22 | v4.1.0 |
| Minimum hardware | Not published | Not published |
| Recommended hardware | Not published | Not published |
| RAM measured by us | Not tested yet | Not tested yet |
| GPU | Yes for the documented install path: Linux with a supported NVIDIA GPU and a CUDA 13-compatible driver. The project also documents AMD Instinct, Google TPU, Intel GPU and Xeon CPU, Ascend NPU and other platforms on separate hardware pages. | The default Docker image requires an NVIDIA GPU (host CUDA 13.0 or newer, driver 580 or newer, NVIDIA Container Toolkit). For CPU, use the image tag with a -cpu suffix; pip installs on Apple Silicon can use the MLX engine. |
| Est. server cost | GPU server (not estimated) | Not estimated |
| Deploy method | Docker (NVIDIA GPU) | Docker |
| Known gotchas | 4 listed | 4 listed |
| Telemetry | Not documented | Not documented |
| Calls home by default | Not documented | Yes, by default |
| SSO (OIDC / SAML / LDAP) | Not documented | Free (OIDC free) |
| Audit log | Not documented | Yes, free |
| RBAC | Not documented | Yes, free |
| Details | Full SGLang guide → | Full Xinference guide → |
Key differences
- GPU: SGLang: needed for practical use. Xinference: optional / see notes.
- Est. server cost: SGLang: GPU server (not estimated). Xinference: not estimated (no official RAM figure).
- Deploy: SGLang: Docker (NVIDIA GPU) · single container. Xinference: Docker · single container.
- GitHub stars: SGLang: 38.3k. Xinference: 9.6k.
- Latest release: SGLang: v0.5.22 (Oct 19, 2026). Xinference: v4.1.0 (Oct 25, 2026).
Same for both: Category: Local model runtimes; License: Apache-2.0 (Permissive); Official hardware: not published; Status: Active; Last commit: Oct 31, 2026.
SGLang vs Xinference: FAQ
SGLang or Xinference: which should a small business choose?
Choose SGLang if you want: larger community (38.3k vs 9.6k GitHub stars). Choose Xinference if you want: no GPU server needed. Xinference publishes no official RAM figure, so we cannot compare hosting cost directly.
Which is cheaper to self-host, SGLang or Xinference?
SGLang: GPU server (not estimated). Xinference: not estimated (no official RAM figure). Our estimate is the cheapest Hetzner Cloud plan that meets each project's official RAM and vCPU figure; it covers the server only.
How much hardware do SGLang and Xinference need?
SGLang: not published. Xinference: not published. SGLang needs a GPU for practical use.
What is the license difference between SGLang and Xinference?
SGLang: Apache-2.0 (Permissive). Permissive. Internal and commercial use allowed. Models keep their own licenses. Xinference: Apache-2.0 (Permissive). Permissive. Internal and commercial use allowed; keep the Apache-2.0 license and notices. Xinference Enterprise is a separate commercial edition. Models keep their own licenses. This is a summary, not legal advice.
Is SGLang or Xinference more actively maintained?
SGLang: Active, last commit Oct 31, 2026. Xinference: Active, last commit Oct 31, 2026. GitHub data refreshed Oct 31, 2026.
Which is easier to deploy, SGLang or Xinference?
SGLang installs via Docker (NVIDIA GPU) (single container); Xinference via Docker (single container). SGLang lists 4 known gotchas and Xinference lists 4; see each app page.
Related
- SGLang self-hosting guide · Xinference self-hosting guide
- All SGLang alternatives, ranked
- All Xinference alternatives, ranked
- GPUStack vs SGLang
- GPUStack vs Xinference
- llama.cpp vs SGLang
- llama.cpp vs Xinference
- LocalAI vs SGLang
- LocalAI vs Xinference
- Ollama vs SGLang
- Ollama vs Xinference
- SGLang vs TextGen (text-generation-webui)
- SGLang vs vLLM
- TextGen (text-generation-webui) vs Xinference
- vLLM vs Xinference
- Add a third app in the interactive comparison
Sources
- SGLang GitHub repository: github.com/sgl-project/sglang
- SGLang documentation, SGLang deploy instructions: docs.sglang.io/docs/get-started/install
- SGLang license: github.com/sgl-project/sglang/blob/main/LICENSE
- SGLang hardware requirements: github.com/sgl-project/sglang#supported-hardware
- Xinference GitHub repository: github.com/xorbitsai/inference
- Xinference documentation, Xinference hardware requirements, Xinference deploy instructions: inference.readthedocs.io/en/latest/getting_started/using_docker_image.html
- Xinference license: github.com/xorbitsai/inference/blob/main/LICENSE
- Hetzner Cloud pricing used for cost estimates (read 2026-10-25): www.hetzner.com/cloud/regular-performance
- GitHub REST API (stars, commits, releases): docs.github.com/en/rest
Every value is generated from the same verified data as the app pages. Spotted something wrong? Tell us.