head to head · open source
llama_index vs infinity
llama_index has 52,779 GitHub stars, 8,500 forks, 770 open issues and last shipped 8 days ago. infinity has 4,755 stars, 458 forks, 126 open issues and last shipped yesterday. llama_index leads on adoption by 1,010% (52,779 vs 4,755 stars). llama_index is written in Python under MIT; infinity is written in C++ under Apache-2.0. llama_index has attracted 16% as many forks as stars, infinity 10%. infinity was the more recently maintained of the two, and both are self-hostable with no licence fee.
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 | infinity | |
|---|---|---|
| GitHub stars | ★ 53K | ★ 4.8K |
| License | MIT | Apache-2.0 |
| Written in | Python | C++ |
| Last push | 2026-10-26 | 2026-11-02 |
| Forks | ⑂ 8.5K | ⑂ 458 |
| 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 infinity if
- You want the infinity feature set and don't need the biggest community
- You prefer the Apache-2.0 license terms
- Your stack matches C++
- You evaluated both and infinity 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 infinity
The AI native database built for LLM applications, providing incredibly fast hybrid search of dense embedding, sparse embedding, tensor and full text Document Benchmark Twitter Discord Infinity is a cutting edge AI native database that provides a wide range of search capabilities for rich data types such as dense vector, sparse vector, tensor, full text, and structured data. It provides robust support for various LLM applications, including search, recommenders, question answering, conversational AI, copilot, content generation, and many more RAG (Retrieval augmented Generation) applications. Key Features Get…
read the full infinity overview →
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Frequently asked questions
Is llama_index or infinity more popular?
llama_index has 52,779 GitHub stars and infinity has 4,755. llama_index has the larger community by that measure.
Are llama_index and infinity free?
Both are open source. llama_index is licensed under MIT and infinity under Apache-2.0. Neither carries a licence fee.
What is the difference between llama_index and infinity?
llama_index is written in Python and infinity in C++. 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 infinity?
Choose llama_index if you want the larger community (52,779 stars) or its MIT licence terms. Choose infinity if its feature set, stack or Apache-2.0 licence fits better. Both are self-hostable.