head to head · open source

llama.cpp vs dagster

llama.cpp has 133,181 GitHub stars, 25,063 forks, 2,464 open issues and last shipped yesterday. dagster has 16,364 stars, 2,395 forks, 2,589 open issues and last shipped 4 days ago. llama.cpp leads on adoption by 714% (133,181 vs 16,364 stars). llama.cpp is written in C++ under MIT; dagster is written in Python under Apache-2.0. llama.cpp has attracted 19% as many forks as stars, dagster 15%. 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 ★ 133K dagster ★ 16K category AI & Machine Learning

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Side by side

	llama.cpp	dagster
GitHub stars	★ 133K	★ 16K
License	MIT	Apache-2.0
Written in	C++	Python
Last push	2026-11-02	2026-10-30
Forks	⑂ 25K	⑂ 2.4K
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 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 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 dagster

Dagster is a cloud native data pipeline orchestrator for the whole development lifecycle, with integrated lineage and observability, a declarative programming model, and best in class testability. It is designed for developing and maintaining data assets , such as tables, data sets, machine learning models, and reports. With Dagster, you declare—as Python functions—the data assets that you want to build. Dagster then helps you run your functions at the right time and keep your assets up to date. Here is an example of a graph of three assets defined in Python: The graph loaded into Dagster's web UI: Dagster is…

read the full dagster overview →

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Frequently asked questions

Is llama.cpp or dagster more popular?

llama.cpp has 133,181 GitHub stars and dagster has 16,364. 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 (133,181 stars) or its MIT licence terms. Choose dagster if its feature set, stack or Apache-2.0 licence fits better. Both are self-hostable.