head to head · open source
vllm vs deep-searcher
vllm has 96,158 GitHub stars, 24,317 forks, 7,953 open issues and last shipped yesterday. deep-searcher has 8,366 stars, 815 forks, 55 open issues and last shipped 1 months ago. vllm leads on adoption by 1,049% (96,158 vs 8,366 stars). vllm is written in Python under Apache-2.0; deep-searcher is written in Python under Apache-2.0. vllm has attracted 25% as many forks as stars, deep-searcher 10%. vllm was the more recently maintained of the two, and both are self-hostable with no licence fee. The two share 2 topic tags (deepseek, 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.
← all 20902 open source comparisons
Side by side
| vllm | deep-searcher | |
|---|---|---|
| GitHub stars | ★ 96K | ★ 8.4K |
| License | Apache-2.0 | Apache-2.0 |
| Written in | Python | Python |
| Last push | 2026-11-02 | 2026-09-22 |
| Forks | ⑂ 24K | ⑂ 815 |
| 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 deep-searcher if
- You want the deep-searcher 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 deep-searcher 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 deep-searcher
DeepSearcher combines cutting edge LLMs (OpenAI o3, Qwen3, DeepSeek, Grok 4, Claude 4 Sonnet, Llama 4, QwQ, etc.) and Vector Databases (Milvus, Zilliz Cloud etc.) to perform search, evaluation, and reasoning based on private data, providing highly accurate answer and comprehensive report. This project is suitable for enterprise knowledge management, intelligent Q&A systems, and information retrieval scenarios. 🚀 Features Private Data Search : Maximizes the utilization of enterprise internal data while ensuring data security. When necessary, it can integrate online content for more accurate answers. Vector…
read the full deep-searcher overview →
More in AI & Machine Learning
Related comparisons
More Machine Learning Infrastructure projects
Compare either of these against the rest of the Machine Learning Infrastructure field.
Frequently asked questions
Is vllm or deep-searcher more popular?
vllm has 96,158 GitHub stars and deep-searcher has 8,366. vllm has the larger community by that measure.
Are vllm and deep-searcher free?
Both are open source. vllm is licensed under Apache-2.0 and deep-searcher under Apache-2.0. Neither carries a licence fee.
What is the difference between vllm and deep-searcher?
vllm is written in Python and deep-searcher 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 deep-searcher?
Choose vllm if you want the larger community (96,158 stars) or its Apache-2.0 licence terms. Choose deep-searcher if its feature set, stack or Apache-2.0 licence fits better. Both are self-hostable.