head to head · open source

llama.cpp vs datachain

llama.cpp has 128,581 GitHub stars, 23,334 forks, 2,464 open issues and last shipped yesterday. datachain has 2,819 stars, 157 forks, 101 open issues and last shipped yesterday. llama.cpp leads on adoption by 4,461% (128,581 vs 2,819 stars). llama.cpp is written in C++ under MIT; datachain is written in Python under Apache-2.0. llama.cpp has attracted 18% as many forks as stars, datachain 6%. llama.cpp 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 ★ 129K datachain ★ 2.8K category AI & Machine Learning

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Side by side

	llama.cpp	datachain
GitHub stars	★ 129K	★ 2.8K
License	MIT	Apache-2.0
Written in	C++	Python
Last push	2026-09-17	2026-09-17
Forks	⑂ 23K	⑂ 157
Self-hosting	Yes	Yes
Data ownership	Your server	Your server

pick llama.cpp if

You weight community size — 129K 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 is a C/C++ library and set of command line tools for running large language models (LLMs) and vision language models (VLMs) locally. It enables inference without external dependencies, targeting diverse hardware including Apple Silicon, x86 CPUs, NVIDIA GPUs (via CUDA), AMD GPUs (via HIP), and other accelerators. The project lives in the ggml ecosystem, leveraging the ggml tensor computation library for low level operations and quantized model execution.

read the full llama.cpp overview →

About datachain

DataChain is an Apache 2.0 Python library that turns files in S3, GCS, and Azure into versioned, typed datasets queryable at warehouse speed, and it is built for data and machine learning teams handling unstructured data as well as for the AI coding agents that operate on those datasets.

read the full datachain overview →

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Frequently asked questions

Is llama.cpp or datachain more popular?

llama.cpp has 128,581 GitHub stars and datachain has 2,819. 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 (128,581 stars) or its MIT licence terms. Choose datachain if its feature set, stack or Apache-2.0 licence fits better. Both are self-hostable.