Ollama vs SGLang
Short answer
Ollama: The simplest way to download and run open-weight LLMs locally behind an API. SGLang: Fast serving engine for LLMs and multimodal models on GPU servers, with an OpenAI-style API. Both are in our Local model runtimes category.
Choose Ollama if you want: no GPU server needed and larger community (185k vs 38.3k GitHub stars).
Choose SGLang if you want: simpler deploy (one container vs a multi-container stack).
Ollama 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
Ollama vs SGLang side by side
| Ollama | SGLang | |
|---|---|---|
| Category | Local model runtimes | Local model runtimes |
| What it is | The simplest way to download and run open-weight LLMs locally behind an API. | Fast serving engine for LLMs and multimodal models on GPU servers, with an OpenAI-style API. |
| License | MIT | Apache-2.0 |
| License type | Permissive | Permissive |
| License conditions | Permissive. Internal and commercial use allowed. Individual models have their own licenses. | Permissive. Internal and commercial use allowed. Models keep their own licenses. |
| Status | Active | Active |
| GitHub stars | 185k | 38.3k |
| Last commit | Oct 30, 2026 | Oct 31, 2026 |
| Latest release | v0.40.13 | v0.5.22 |
| Minimum hardware | Not published | Not published |
| Recommended hardware | Not published | Not published |
| RAM measured by us | 85 MiB idle, 1.9 GiB working (our test, v0.40.8, Oct 22, 2026) | Not tested yet |
| GPU | NVIDIA GPUs need compute capability 5.0+ and driver 550+ (570+ for compute capability 5.0–6.2). CPU-only also works. | 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. |
| Est. server cost | from €19.99/mo (based on our test, not official) | GPU server (not estimated) |
| Deploy method | Docker (or install script) | Docker (NVIDIA GPU) |
| Known gotchas | 2 listed | 4 listed |
| Telemetry | Not documented | Not documented |
| Calls home by default | Yes, by default | Not documented |
| SSO (OIDC / SAML / LDAP) | Not documented | Not documented |
| Audit log | Not documented | Not documented |
| RBAC | Not documented | Not documented |
| Details | Full Ollama guide → | Full SGLang guide → |
Key differences
- License: Ollama: MIT (Permissive). SGLang: Apache-2.0 (Permissive).
- GPU: Ollama: optional / see notes. SGLang: needed for practical use.
- Est. server cost: Ollama: not estimated (no official RAM figure). SGLang: GPU server (not estimated).
- Deploy: Ollama: Docker (or install script) · multi-container stack. SGLang: Docker (NVIDIA GPU) · single container.
- GitHub stars: Ollama: 185k. SGLang: 38.3k.
- Last commit: Ollama: Oct 30, 2026. SGLang: Oct 31, 2026.
- Latest release: Ollama: v0.40.13 (Oct 28, 2026). SGLang: v0.5.22 (Oct 19, 2026).
Same for both: Category: Local model runtimes; Official hardware: not published; Status: Active.
Ollama vs SGLang: FAQ
Ollama or SGLang: which should a small business choose?
Choose Ollama if you want: no GPU server needed and larger community (185k vs 38.3k GitHub stars). Choose SGLang if you want: simpler deploy (one container vs a multi-container stack). Ollama publishes no official RAM figure, so we cannot compare hosting cost directly.
Which is cheaper to self-host, Ollama or SGLang?
Ollama: not estimated (no official RAM figure). SGLang: GPU server (not estimated). 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 Ollama and SGLang need?
Ollama: not published. SGLang: not published. SGLang needs a GPU for practical use.
What is the license difference between Ollama and SGLang?
Ollama: MIT (Permissive). Permissive. Internal and commercial use allowed. Individual models have their own licenses. SGLang: Apache-2.0 (Permissive). Permissive. Internal and commercial use allowed. Models keep their own licenses. This is a summary, not legal advice.
Is Ollama or SGLang more actively maintained?
Ollama: Active, last commit Oct 30, 2026. SGLang: Active, last commit Oct 31, 2026. GitHub data refreshed Oct 31, 2026.
Which is easier to deploy, Ollama or SGLang?
Ollama installs via Docker (or install script) (multi-container stack); SGLang via Docker (NVIDIA GPU) (single container). Ollama lists 2 known gotchas and SGLang lists 4; see each app page.
Related
- Ollama self-hosting guide · SGLang self-hosting guide
- All Ollama alternatives, ranked
- All SGLang alternatives, ranked
- GPUStack vs Ollama
- GPUStack vs SGLang
- Jan vs Ollama
- llama.cpp vs Ollama
- llama.cpp vs SGLang
- LocalAI vs Ollama
- LocalAI vs SGLang
- Ollama vs TextGen (text-generation-webui)
- Ollama vs vLLM
- Ollama vs Xinference
- SGLang vs TextGen (text-generation-webui)
- SGLang vs vLLM
- Add a third app in the interactive comparison
Sources
- Ollama GitHub repository: github.com/ollama/ollama
- Ollama documentation, Ollama deploy instructions: docs.ollama.com/docker
- Ollama license: github.com/ollama/ollama/blob/main/LICENSE
- Ollama hardware requirements: docs.ollama.com/gpu
- 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
- 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.