head to head · open source

llama.cpp vs Helicone

llama.cpp has 128,880 GitHub stars, 23,468 forks, 2,464 open issues and last shipped today. Helicone has 6,165 stars, 672 forks, 156 open issues and last shipped 4 days ago. llama.cpp leads on adoption by 1,991% (128,880 vs 6,165 stars). llama.cpp is written in C++ under MIT; Helicone is written in TypeScript under Apache-2.0. llama.cpp has attracted 18% as many forks as stars, Helicone 11%. 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 Helicone ★ 6.2K category AI & Machine Learning

← all 20902 open source comparisons

Side by side

	llama.cpp	Helicone
GitHub stars	★ 129K	★ 6.2K
License	MIT	Apache-2.0
Written in	C++	TypeScript
Last push	2026-09-20	2026-09-16
Forks	⑂ 23K	⑂ 672
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 Helicone if

You want the Helicone feature set and don't need the biggest community
You prefer the Apache-2.0 license terms
Your stack matches TypeScript
You evaluated both and Helicone fits your workflow better

full Helicone 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 Helicone

Helicone is an open source AI gateway and LLM observability platform for AI engineers who need to monitor, evaluate, and experiment with applications built on large language models.

read the full Helicone overview →

More in AI & Machine Learning

Related comparisons

More Machine Learning Infrastructure projects

Compare either of these against the rest of the Machine Learning Infrastructure field.

llama.cpp vs Ollama llama.cpp vs vllm llama.cpp vs GPT4All llama.cpp vs llama_index llama.cpp vs LocalAI llama.cpp vs faiss llama.cpp vs PageIndex llama.cpp vs Langfuse llama.cpp vs cognee llama.cpp vs taipy llama.cpp vs dagster llama.cpp vs zvec

Frequently asked questions

Is llama.cpp or Helicone more popular?

llama.cpp has 128,880 GitHub stars and Helicone has 6,165. llama.cpp has the larger community by that measure.

Are llama.cpp and Helicone free?

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

What is the difference between llama.cpp and Helicone?

llama.cpp is written in C++ and Helicone in TypeScript. 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 Helicone?

Choose llama.cpp if you want the larger community (128,880 stars) or its MIT licence terms. Choose Helicone if its feature set, stack or Apache-2.0 licence fits better. Both are self-hostable.