head to head · open source

llama.cpp vs dagster

llama.cpp has 128,581 GitHub stars, 23,334 forks, 2,464 open issues and last shipped yesterday. dagster has 16,169 stars, 2,294 forks, 2,589 open issues and last shipped yesterday. llama.cpp leads on adoption by 695% (128,581 vs 16,169 stars). llama.cpp is written in C++ under MIT; dagster is written in Python under Apache-2.0. llama.cpp has attracted 18% as many forks as stars, dagster 14%. dagster 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 dagster ★ 16K category AI & Machine Learning

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Side by side

	llama.cpp	dagster
GitHub stars	★ 129K	★ 16K
License	MIT	Apache-2.0
Written in	C++	Python
Last push	2026-09-17	2026-09-17
Forks	⑂ 23K	⑂ 2.3K
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 dagster if

You want the dagster 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 dagster fits your workflow better

full dagster 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 dagster

Dagster is a cloud native data pipeline orchestrator written in Python and released under the Apache 2.0 licence, built for data engineers, data scientists, and machine learning teams who declare the tables, data sets, models, and reports their pipelines should produce and need those assets tested, observable, and kept up to date from local development through production.

read the full dagster overview →

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Frequently asked questions

Is llama.cpp or dagster more popular?

llama.cpp has 128,581 GitHub stars and dagster has 16,169. llama.cpp has the larger community by that measure.

Are llama.cpp and dagster free?

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

What is the difference between llama.cpp and dagster?

llama.cpp is written in C++ and dagster 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 dagster?

Choose llama.cpp if you want the larger community (128,581 stars) or its MIT licence terms. Choose dagster if its feature set, stack or Apache-2.0 licence fits better. Both are self-hostable.