head to head · open source
llama_index vs VectorDBBench
llama_index has 52,206 GitHub stars, 8,165 forks, 770 open issues and last shipped today. VectorDBBench has 1,178 stars, 437 forks, 184 open issues and last shipped 7 days ago. llama_index leads on adoption by 4,332% (52,206 vs 1,178 stars). llama_index is written in Python under MIT; VectorDBBench is written in Python under MIT. llama_index has attracted 16% as many forks as stars, VectorDBBench 37%. llama_index was the more recently maintained of the two, and both are self-hostable with no licence fee. The two share 1 topic tag (vector-database), 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
| llama_index | VectorDBBench | |
|---|---|---|
| GitHub stars | ★ 52K | ★ 1.2K |
| License | MIT | MIT |
| Written in | Python | Python |
| Last push | 2026-09-18 | 2026-09-11 |
| Forks | ⑂ 8.2K | ⑂ 437 |
| Self-hosting | Yes | Yes |
| Data ownership | Your server | Your server |
pick llama_index if
- You weight community size — 52K stars and counting
- You want the MIT license terms
- Your stack matches Python
- You value the larger contributor base for long-term maintenance
pick VectorDBBench if
- You want the VectorDBBench feature set and don't need the biggest community
- You prefer the MIT license terms
- Your stack matches Python
- You evaluated both and VectorDBBench fits your workflow better
About llama_index
LlamaIndex is an MIT licensed, open source Python framework for building agentic applications — retrieval augmented generation systems, agents and multi agent workflows — on top of private documents and data, and it is aimed at AI engineers and teams who need to connect large language models to their own sources of context.
read the full llama_index overview →
About VectorDBBench
VectorDBBench(VDBBench): A Benchmark Tool for VectorDB What is VDBBench VDBBench is not just an offering of benchmark results for mainstream vector databases and cloud services, it's your go to tool for the ultimate performance and cost effectiveness comparison. Designed with ease of use in mind, VDBBench is devised to help users, even non professionals, reproduce results or test new systems, making the hunt for the optimal choice amongst a plethora of cloud services and open source vector databases a breeze. Understanding the importance of user experience, we provide an intuitive visual interface. This not only …
read the full VectorDBBench overview →
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Frequently asked questions
Is llama_index or VectorDBBench more popular?
llama_index has 52,206 GitHub stars and VectorDBBench has 1,178. llama_index has the larger community by that measure.
Are llama_index and VectorDBBench free?
Both are open source. llama_index is licensed under MIT and VectorDBBench under MIT. Neither carries a licence fee.
What is the difference between llama_index and VectorDBBench?
llama_index is written in Python and VectorDBBench 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, llama_index or VectorDBBench?
Choose llama_index if you want the larger community (52,206 stars) or its MIT licence terms. Choose VectorDBBench if its feature set, stack or MIT licence fits better. Both are self-hostable.