head to head · open source
vllm vs ome
vllm has 96,158 GitHub stars, 24,317 forks, 7,953 open issues and last shipped yesterday. ome has 523 stars, 106 forks, 134 open issues and last shipped yesterday. vllm leads on adoption by 18,286% (96,158 vs 523 stars). vllm is written in Python under Apache-2.0; ome is written in Go under Apache-2.0. vllm has attracted 25% as many forks as stars, ome 20%. vllm was the more recently maintained of the two, and both are self-hostable with no licence fee. The two share 3 topic tags (deepseek, llama, llm), so they are genuine substitutes rather than adjacent tools.
Two open source projects, one decision. Both are free and self-hostable — the differences are community size, license terms, language stack and release pace.
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Side by side
| vllm | ome | |
|---|---|---|
| GitHub stars | ★ 96K | ★ 523 |
| License | Apache-2.0 | Apache-2.0 |
| Written in | Python | Go |
| Last push | 2026-11-02 | 2026-11-02 |
| Forks | ⑂ 24K | ⑂ 106 |
| Self-hosting | Yes | Yes |
| Data ownership | Your server | Your server |
pick vllm if
- You weight community size — 96K 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
pick ome if
- You want the ome 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 ome fits your workflow better
About vllm
Easy, fast, and cheap LLM serving for everyone 🔥 We have built a vLLM website to help you get started with vLLM. Please visit vllm.ai to learn more. For events, please visit vllm.ai/events to join us. About vLLM is a fast and easy to use library for LLM inference and serving. Originally developed in the Sky Computing Lab at UC Berkeley, vLLM has grown into one of the most active open source AI projects built and maintained by a diverse community of many dozens of academic institutions and companies from over 2000 contributors. vLLM is fast with: State of the art serving throughput Efficient management of…
About ome
OME (Open Model Engine) — Kubernetes Operator for LLM Serving What is OME? OME (Open Model Engine) is a Kubernetes operator for enterprise grade management and serving of Large Language Models (LLMs). It optimizes the deployment and operation of LLMs by automating model management, intelligent runtime selection, efficient resource utilization, and sophisticated deployment patterns. Read the documentation to learn more about OME capabilities and features. Features Overview Model Management: Models are first class citizen custom resources in OME. Sophisticated model parsing extracts architecture, parameter count…
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Frequently asked questions
Is vllm or ome more popular?
vllm has 96,158 GitHub stars and ome has 523. vllm has the larger community by that measure.
Are vllm and ome free?
Both are open source. vllm is licensed under Apache-2.0 and ome under Apache-2.0. Neither carries a licence fee.
What is the difference between vllm and ome?
vllm is written in Python and ome 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 ome?
Choose vllm if you want the larger community (96,158 stars) or its Apache-2.0 licence terms. Choose ome if its feature set, stack or Apache-2.0 licence fits better. Both are self-hostable.