head to head · open source

vllm vs matrixone

vllm has 92,055 GitHub stars, 22,349 forks, 7,953 open issues and last shipped today. matrixone has 1,889 stars, 312 forks, 745 open issues and last shipped today. vllm leads on adoption by 4,773% (92,055 vs 1,889 stars). vllm is written in Python under Apache-2.0; matrixone is written in Go under Apache-2.0. vllm has attracted 24% as many forks as stars, matrixone 17%. vllm 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.

vllm ★ 92K matrixone ★ 1.9K category AI & Machine Learning

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Side by side

vllm matrixone
GitHub stars ★ 92K ★ 1.9K
License Apache-2.0 Apache-2.0
Written in Python Go
Last push 2026-09-18 2026-09-18
Forks ⑂ 22K ⑂ 312
Self-hosting Yes Yes
Data ownership Your server Your server

pick vllm if

  • You weight community size — 92K stars and counting
  • You want the Apache-2.0 license terms
  • Your stack matches Python
  • You value the larger contributor base for long-term maintenance

full vllm profile →

pick matrixone if

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

full matrixone profile →

About vllm

vLLM is a high throughput, memory efficient Python library for LLM inference and serving, built for ML engineers and platform teams who need to run open weight models on their own hardware at production scale.

read the full vllm overview →

About matrixone

Docs Official Website Research Paper English 简体中文 Connect with us: Contents ======== What is MatrixOne Get Started in 60 Seconds Tutorials & Demos Installation & Deployment Architecture Python SDK Citing MatrixOne Contributing License What is MatrixOne? MatrixOne is the industry's first database to bring Git style version control to data , combined with MySQL compatibility, AI native capabilities, and cloud native architecture. At its core, MatrixOne is a HTAP (Hybrid Transactional/Analytical Processing) database with a hyper converged HSTAP engine that seamlessly handles transactional (OLTP), analytical (OLAP), …

read the full matrixone overview →

More in AI & Machine Learning

OpenClaw ★ 390K Hermes Agent ★ 247K Ollama ★ 181K Dify ★ 156K Open WebUI ★ 152K langchain ★ 147K

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ollama vs vllm llama-cpp vs vllm ollama vs llama-cpp ollama vs gpt4all ollama vs llama-index ollama vs localai ollama vs pageindex ollama vs langfuse 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.

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

Frequently asked questions

Is vllm or matrixone more popular?

vllm has 92,055 GitHub stars and matrixone has 1,889. vllm has the larger community by that measure.

Are vllm and matrixone free?

Both are open source. vllm is licensed under Apache-2.0 and matrixone under Apache-2.0. Neither carries a licence fee.

What is the difference between vllm and matrixone?

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

Which should I choose, vllm or matrixone?

Choose vllm if you want the larger community (92,055 stars) or its Apache-2.0 licence terms. Choose matrixone if its feature set, stack or Apache-2.0 licence fits better. Both are self-hostable.