head to head · open source
vllm vs dstack
vllm has 96,095 GitHub stars, 24,279 forks, 7,953 open issues and last shipped yesterday. dstack has 2,298 stars, 272 forks, 68 open issues and last shipped 4 days ago. vllm leads on adoption by 4,082% (96,095 vs 2,298 stars). vllm is written in Python under Apache-2.0; dstack is written in Python under MPL-2.0. vllm has attracted 25% as many forks as stars, dstack 12%. vllm was the more recently maintained of the two, and both are self-hostable with no licence fee. The two share 2 topic tags (amd, inference), 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 | dstack | |
|---|---|---|
| GitHub stars | ★ 96K | ★ 2.3K |
| License | Apache-2.0 | MPL-2.0 |
| Written in | Python | Python |
| Last push | 2026-11-02 | 2026-10-30 |
| Forks | ⑂ 24K | ⑂ 272 |
| 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 dstack if
- You want the dstack feature set and don't need the biggest community
- You prefer the MPL-2.0 license terms
- Your stack matches Python
- You evaluated both and dstack 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 dstack
dstack is a unified control plane for GPU provisioning and orchestration that works with any GPU cloud, Kubernetes, or on prem clusters. It streamlines development, training, and inference, and is compatible with any hardware, open source tools, and frameworks. Accelerators dstack supports NVIDIA , AMD , Google TPU , and Tenstorrent accelerators out of the box. Latest news ✨ [2026/08] dstack 0.21.0: Pydantic v2, Gateway replicas [2026/07] dstack 0.20.29: Presets — agent driven inference optimization (experimental) [2026/07] dstack 0.20.27: Slurm backend [2026/05] dstack 0.20.21: Kubernetes multiple clusters…
read the full dstack overview →
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Frequently asked questions
Is vllm or dstack more popular?
vllm has 96,095 GitHub stars and dstack has 2,298. vllm has the larger community by that measure.
Are vllm and dstack free?
Both are open source. vllm is licensed under Apache-2.0 and dstack under MPL-2.0. Neither carries a licence fee.
What is the difference between vllm and dstack?
vllm is written in Python and dstack 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, vllm or dstack?
Choose vllm if you want the larger community (96,095 stars) or its Apache-2.0 licence terms. Choose dstack if its feature set, stack or MPL-2.0 licence fits better. Both are self-hostable.