head to head · open source
llama_index vs datachain
llama_index has 52,779 GitHub stars, 8,500 forks, 770 open issues and last shipped 8 days ago. datachain has 2,830 stars, 178 forks, 101 open issues and last shipped yesterday. llama_index leads on adoption by 1,765% (52,779 vs 2,830 stars). llama_index is written in Python under MIT; datachain is written in Python under Apache-2.0. llama_index has attracted 16% as many forks as stars, datachain 6%. datachain 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 | datachain | |
|---|---|---|
| GitHub stars | ★ 53K | ★ 2.8K |
| License | MIT | Apache-2.0 |
| Written in | Python | Python |
| Last push | 2026-10-26 | 2026-11-02 |
| Forks | ⑂ 8.5K | ⑂ 178 |
| 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 datachain if
- You want the datachain feature set and don't need the biggest community
- You prefer the Apache-2.0 license terms
- Your stack matches Python
- You evaluated both and datachain 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 datachain
DataChain: The Context Layer for Unstructured Data A Python library that turns files in S3, GCS, and Azure into versioned, typed datasets, queryable at warehouse speed. Compute Engine : parallel Python over files, distributed on Studio. Async I/O, checkpoint recovery, incremental updates. Dataset DB : Pydantic schemas, versioning, file pointers, automatic lineage. Sub second filter, join, and group by over millions of typed records locally, hundreds of millions on Studio. Vector search over the same rows, no separate store. Optional, for agent workflows: Knowledge Base : markdown summaries derived from the…
read the full datachain overview →
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Frequently asked questions
Is llama_index or datachain more popular?
llama_index has 52,779 GitHub stars and datachain has 2,830. llama_index has the larger community by that measure.
Are llama_index and datachain free?
Both are open source. llama_index is licensed under MIT and datachain under Apache-2.0. Neither carries a licence fee.
What is the difference between llama_index and datachain?
llama_index is written in Python and datachain 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 datachain?
Choose llama_index if you want the larger community (52,779 stars) or its MIT licence terms. Choose datachain if its feature set, stack or Apache-2.0 licence fits better. Both are self-hostable.