head to head · open source

llama.cpp vs Spanlens

llama.cpp has 133,181 GitHub stars, 25,063 forks, 2,464 open issues and last shipped yesterday. Spanlens has 13 stars, 1 forks, 1 open issues and last shipped yesterday. llama.cpp leads on adoption by 1,024,369% (133,181 vs 13 stars). llama.cpp is written in C++ under MIT; Spanlens is written in TypeScript under MIT. llama.cpp has attracted 19% as many forks as stars, Spanlens 8%. 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 Spanlens ★ 13 category AI & Machine Learning

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Side by side

	llama.cpp	Spanlens
GitHub stars	★ 133K	★ 13
License	MIT	MIT
Written in	C++	TypeScript
Last push	2026-11-02	2026-11-02
Forks	⑂ 25K	⑂ 1
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 Spanlens if

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

full Spanlens 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 Spanlens

Spanlens Open source LLM observability you can turn on in one line. Point your OpenAI, Anthropic, or Gemini client at Spanlens and every call is logged with cost, tokens, latency, and full agent traces. No SDK rewrite, no platform migration. Eleven providers supported, plus native Vercel AI SDK, LangChain, and LlamaIndex integrations, and you can query it all from Cursor or Claude Desktop through the bundled MCP server. Self hostable in one Docker command. MIT. Why it exists. I shipped an LLM app on OpenAI and Gemini and hit a wall. The provider dashboards showed total spend and nothing else. I could not tell…

read the full Spanlens overview →

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Frequently asked questions

Is llama.cpp or Spanlens more popular?

llama.cpp has 133,181 GitHub stars and Spanlens has 13. llama.cpp has the larger community by that measure.

Are llama.cpp and Spanlens free?

Both are open source. llama.cpp is licensed under MIT and Spanlens under MIT. Neither carries a licence fee.

What is the difference between llama.cpp and Spanlens?

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

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