head to head · open source
vllm vs llama_index
vllm has 96,158 GitHub stars, 24,317 forks, 7,953 open issues and last shipped yesterday. llama_index has 52,779 stars, 8,500 forks, 770 open issues and last shipped 8 days ago. vllm leads on adoption by 82% (96,158 vs 52,779 stars). vllm is written in Python under Apache-2.0; llama_index is written in Python under MIT. vllm has attracted 25% as many forks as stars, llama_index 16%. vllm was the more recently maintained of the two, and both are self-hostable with no licence fee. The two share 1 topic tag (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 | llama_index | |
|---|---|---|
| GitHub stars | ★ 96K | ★ 53K |
| License | Apache-2.0 | MIT |
| Written in | Python | Python |
| Last push | 2026-11-02 | 2026-10-26 |
| Forks | ⑂ 24K | ⑂ 8.5K |
| 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 llama_index if
- You want the llama_index feature set and don't need the biggest community
- You prefer the MIT license terms
- Your stack matches Python
- You evaluated both and llama_index 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 llama_index
🗂️ LlamaIndex (OSS Framework) 🦙 [!NOTE] The current focus of LlamaIndex is to build the best AI powered engine for document parsing and extraction. LlamaParse is our enterprise platform for agentic OCR, parsing, extraction, indexing and more. LiteParse represents our efforts to build the best free, fast, cheap text parser in the market. ParseBench and ExtractBench represent our commitment towards open benchmarking for parsing and extraction. The company itself has undergone an evolution since when this OSS framework first launched 3 years ago in 2023. Since the early days, the framework has consisted of a…
read the full llama_index overview →
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Frequently asked questions
Is vllm or llama_index more popular?
vllm has 96,158 GitHub stars and llama_index has 52,779. vllm has the larger community by that measure.
Are vllm and llama_index free?
Both are open source. vllm is licensed under Apache-2.0 and llama_index under MIT. Neither carries a licence fee.
What is the difference between vllm and llama_index?
vllm is written in Python and llama_index 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 llama_index?
Choose vllm if you want the larger community (96,158 stars) or its Apache-2.0 licence terms. Choose llama_index if its feature set, stack or MIT licence fits better. Both are self-hostable.