head to head · open source

Ollama vs sglang-omni

Ollama has 181,161 GitHub stars, 17,917 forks, 3,957 open issues and last shipped yesterday. sglang-omni has 1,216 stars, 496 forks, 644 open issues and last shipped yesterday. Ollama leads on adoption by 14,798% (181,161 vs 1,216 stars). Ollama is written in Go under MIT; sglang-omni is written in Python under Apache-2.0. Ollama has attracted 10% as many forks as stars, sglang-omni 41%. sglang-omni was the more recently maintained of the two, and both are self-hostable with no licence fee.

Two open source projects, one decision. Both are free and self-hostable — the differences are community size, license terms, language stack and release pace.

Ollama ★ 181K sglang-omni ★ 1.2K category AI & Machine Learning

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Side by side

Ollama sglang-omni
GitHub stars ★ 181K ★ 1.2K
License MIT Apache-2.0
Written in Go Python
Last push 2026-09-17 2026-09-17
Forks ⑂ 18K ⑂ 496
Self-hosting Yes Yes
Data ownership Your server Your server

pick Ollama if

  • You weight community size — 181K stars and counting
  • You want the MIT license terms
  • Your stack matches Go
  • You value the larger contributor base for long-term maintenance

full Ollama profile →

pick sglang-omni if

  • You want the sglang-omni feature set and don't need the biggest community
  • You prefer the Apache-2.0 license terms
  • Your stack matches Python
  • You evaluated both and sglang-omni fits your workflow better

full sglang-omni profile →

About Ollama

Ollama is a Go based, MIT licensed runtime that downloads and runs open source large language models such as DeepSeek, Qwen, Gemma, GLM, MiniMax and gpt oss locally on a user's own machine, and it is aimed at developers and teams that want model inference without routing prompts through a hosted API.

read the full Ollama overview →

About sglang-omni

SGLang Omni is an Apache 2.0 Python serving framework that runs audio models (text to speech, automatic speech recognition, music generation) and unified multimodal models behind OpenAI compatible endpoints, aimed at teams deploying those models on their own GPU infrastructure.

read the full sglang-omni overview →

More in AI & Machine Learning

OpenClaw ★ 390K Hermes Agent ★ 246K Dify ★ 156K Open WebUI ★ 152K langchain ★ 147K ponytail ★ 141K

Related comparisons

ollama vs llama-cpp ollama vs vllm ollama vs gpt4all ollama vs llama-index ollama vs localai ollama vs pageindex ollama vs langfuse llama-cpp vs vllm openclaw vs hermes-agent openclaw vs open-webui openclaw vs lobechat openclaw vs anythingllm openclaw vs cherry-studio openclaw vs nanobot openclaw vs jan openclaw vs librechat dify vs langchain dify vs ponytail langchain vs ponytail dify vs graphify langchain vs graphify ponytail vs graphify dify vs claude-mem dify vs ragflow

More Machine Learning Infrastructure projects

Compare either of these against the rest of the Machine Learning Infrastructure field.

Ollama vs llama.cpp Ollama vs vllm Ollama vs GPT4All Ollama vs llama_index Ollama vs LocalAI Ollama vs PageIndex Ollama vs Langfuse Ollama vs cognee Ollama vs taipy Ollama vs dagster Ollama vs zvec Ollama vs langchain4j

Frequently asked questions

Is Ollama or sglang-omni more popular?

Ollama has 181,161 GitHub stars and sglang-omni has 1,216. Ollama has the larger community by that measure.

Are Ollama and sglang-omni free?

Both are open source. Ollama is licensed under MIT and sglang-omni under Apache-2.0. Neither carries a licence fee.

What is the difference between Ollama and sglang-omni?

Ollama is written in Go and sglang-omni in Python. The practical differences are community size, licence terms, language stack and release cadence — all compared in the table above.

Which should I choose, Ollama or sglang-omni?

Choose Ollama if you want the larger community (181,161 stars) or its MIT licence terms. Choose sglang-omni if its feature set, stack or Apache-2.0 licence fits better. Both are self-hostable.