head to head · open source

llama.cpp vs datachain

llama.cpp has 133,181 GitHub stars, 25,063 forks, 2,464 open issues and last shipped yesterday. datachain has 2,830 stars, 178 forks, 101 open issues and last shipped yesterday. llama.cpp leads on adoption by 4,606% (133,181 vs 2,830 stars). llama.cpp is written in C++ under MIT; datachain is written in Python under Apache-2.0. llama.cpp has attracted 19% 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.

llama.cpp ★ 133K datachain ★ 2.8K category AI & Machine Learning

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Side by side

	llama.cpp	datachain
GitHub stars	★ 133K	★ 2.8K
License	MIT	Apache-2.0
Written in	C++	Python
Last push	2026-11-02	2026-11-02
Forks	⑂ 25K	⑂ 178
Self-hosting	Yes	Yes
Data ownership	Your server	Your server

pick llama.cpp if

You weight community size — 133K stars and counting
You want the MIT license terms
Your stack matches C++
You value the larger contributor base for long-term maintenance

full llama.cpp profile →

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

full datachain profile →

About llama.cpp

llama.cpp LLM inference in C/C++ ggml / ops / maintainer PRs%20sort%3Aupdated desc) / dev stats / lib llama API / llama server REST API Quick start A few options to get llama.cpp installed on your machine: Visit and follow the instructions Run with Docker see our Docker documentation Download pre built binaries from the releases page Build from source by cloning this repository check out our build guide Once installed: VLM session with llama cli Built in web UI against llama serve Description The main goal of llama.cpp is to enable LLM (and VLM) inference with minimal setup and state of the art performance on a…

read the full llama.cpp 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.cpp or datachain more popular?

llama.cpp has 133,181 GitHub stars and datachain has 2,830. llama.cpp has the larger community by that measure.

Are llama.cpp and datachain free?

Both are open source. llama.cpp is licensed under MIT and datachain under Apache-2.0. Neither carries a licence fee.

What is the difference between llama.cpp and datachain?

llama.cpp is written in C++ 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.cpp or datachain?

Choose llama.cpp if you want the larger community (133,181 stars) or its MIT licence terms. Choose datachain if its feature set, stack or Apache-2.0 licence fits better. Both are self-hostable.