head to head · open source
llama_index vs openlake
llama_index has 52,779 GitHub stars, 8,500 forks, 770 open issues and last shipped 8 days ago. openlake has 2,645 stars, 420 forks, 138 open issues and last shipped 2 days ago. llama_index leads on adoption by 1,895% (52,779 vs 2,645 stars). llama_index is written in Python under MIT; openlake is written in Rust under Apache-2.0. llama_index has attracted 16% as many forks as stars, openlake 16%. openlake 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
| llama_index | openlake | |
|---|---|---|
| GitHub stars | ★ 53K | ★ 2.6K |
| License | MIT | Apache-2.0 |
| Written in | Python | Rust |
| Last push | 2026-10-26 | 2026-11-01 |
| Forks | ⑂ 8.5K | ⑂ 420 |
| 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 openlake if
- You want the openlake feature set and don't need the biggest community
- You prefer the Apache-2.0 license terms
- Your stack matches Rust
- You evaluated both and openlake 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 openlake
Fast, easy and efficient storage for LLM Inference and Training 🔥 Distributed storage for GPU workloads. Built on Rust on io uring , OpenLake is a state of the art storage engine delivering million+ iops within 1ms. Updates [2026/09] 🔥 OpenLake tops the MLPerf Storage v3.0 2026 object checkpointing, leading NVIDIA and Nebius (blog). [2026/08] ExANS : a lossless GPU codec for BF16 KV cache: 1.51× cost savings, now available in OpenLake v0.8 (blog). [2026/07] Breaking the KV Wall: managing 100 TB of KV cache and 8× inference throughput with deferred materialization (blog). Older updates [2026/07] Introducing…
read the full openlake overview →
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Frequently asked questions
Is llama_index or openlake more popular?
llama_index has 52,779 GitHub stars and openlake has 2,645. llama_index has the larger community by that measure.
Are llama_index and openlake free?
Both are open source. llama_index is licensed under MIT and openlake under Apache-2.0. Neither carries a licence fee.
What is the difference between llama_index and openlake?
llama_index is written in Python and openlake in Rust. 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 openlake?
Choose llama_index if you want the larger community (52,779 stars) or its MIT licence terms. Choose openlake if its feature set, stack or Apache-2.0 licence fits better. Both are self-hostable.