head to head · open source
serviette vs vllm-omni
serviette has 58,732 GitHub stars, 1,547 forks, 8 open issues and last shipped yesterday. vllm-omni has 7,494 stars, 2,037 forks, 1,977 open issues and last shipped yesterday. serviette leads on adoption by 684% (58,732 vs 7,494 stars). serviette is written in Python under MIT; vllm-omni is written in Python under Apache-2.0. serviette has attracted 3% as many forks as stars, vllm-omni 27%. vllm-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.
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Side by side
| serviette | vllm-omni | |
|---|---|---|
| GitHub stars | ★ 59K | ★ 7.5K |
| License | MIT | Apache-2.0 |
| Written in | Python | Python |
| Last push | 2026-11-02 | 2026-11-02 |
| Forks | ⑂ 1.5K | ⑂ 2.0K |
| Self-hosting | Yes | Yes |
| Data ownership | Your server | Your server |
pick serviette if
- You weight community size — 59K stars and counting
- You want the MIT license terms
- Your stack matches Python
- You value the larger contributor base for long-term maintenance
pick vllm-omni if
- You want the vllm-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 vllm-omni fits your workflow better
About serviette
serviette Looking for llm app templates? All templates, cookbooks and examples are still available in full in the Pathway repository under examples/llm app . A no code, always up to date RAG server for any vector database — built on the Pathway Live Data Framework. Point serviette at a folder of documents, pick a vector database and an embedder in one YAML file, and run two commands: you get a chat and a REST API that answer from those documents and cite their sources. From then on, any change to the documents — an edit, a new file, a deletion — is reflected in the answers within seconds. serviette runs on…
read the full serviette overview →
About vllm-omni
Easy, fast, and cheap omni modality model serving for everyone Latest News 🔥 [2026/08] We released 0.28.0, featuring production ready MiniMax H3 serving on GPU and NPU, a unified AR/DiT paged KV cache runtime, and enhanced realtime full duplex serving for the MiniCPM o series. [2026/08] VeRL Omni v0.2.0 is released: faster diffusion RL powered by vLLM Omni (request level/step wise batching with FA3), rebuilt Qwen3 Omni multimodal training (DPO & GSPO), plus LTX 2.3, Qwen Image Edit support and more. See the release notes. [2026/08] We released 0.26.0 aligned with the vLLM 0.26 release line, featuring MiniMax H3…
read the full vllm-omni overview →
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Frequently asked questions
Is serviette or vllm-omni more popular?
serviette has 58,732 GitHub stars and vllm-omni has 7,494. serviette has the larger community by that measure.
Are serviette and vllm-omni free?
Both are open source. serviette is licensed under MIT and vllm-omni under Apache-2.0. Neither carries a licence fee.
What is the difference between serviette and vllm-omni?
serviette is written in Python and vllm-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, serviette or vllm-omni?
Choose serviette if you want the larger community (58,732 stars) or its MIT licence terms. Choose vllm-omni if its feature set, stack or Apache-2.0 licence fits better. Both are self-hostable.