head to head · open source
llama_index vs VectorDBBench
llama_index has 52,779 GitHub stars, 8,500 forks, 770 open issues and last shipped 8 days ago. VectorDBBench has 1,199 stars, 450 forks, 184 open issues and last shipped 5 days ago. llama_index leads on adoption by 4,302% (52,779 vs 1,199 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 38%. VectorDBBench 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 | ★ 53K | ★ 1.2K |
| License | MIT | MIT |
| Written in | Python | Python |
| Last push | 2026-10-26 | 2026-10-29 |
| Forks | ⑂ 8.5K | ⑂ 450 |
| Self-hosting | Yes | Yes |
| Data ownership | Your server | Your server |
pick llama_index if
- You weight community size — 53K 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 (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 →
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 52,779 GitHub stars and VectorDBBench has 1,199. 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 (52,779 stars) or its MIT licence terms. Choose VectorDBBench if its feature set, stack or MIT licence fits better. Both are self-hostable.