How Diffuse compares

An honest look at where Diffuse sits next to the alternatives. No option is best at everything.

At a glance

	Diffuse	Hosted API	Local model	Other split inference
Your prompt leaves in clear text	never on the wire	yes, to the provider	no	often yes
A single party sees all traffic	no	yes	no	varies
Needs a powerful machine	no	no	yes	no
Runs models bigger than one machine	yes	yes	no	yes
Speed	slow	fast	fast	slow
Cost	free	paid	hardware	free
Setup	one binary	account	model download	varies

Versus a hosted API (OpenAI, Anthropic, others)

Hosted APIs are fast, reliable, and effortless. In exchange, every prompt you send passes through one company that can log it, and you need an account and a budget. Diffuse is slower and community-run, but no single party receives all of your prompts and there is no account. Use a hosted API when you need speed and polish; use Diffuse when you want decentralization and to keep your words off a central server.

Versus running a model locally

Running the model yourself (Ollama, llama.cpp, LM Studio) is the strongest privacy option: nothing leaves your machine at all. The catch is you can only run models that fit on your hardware. Diffuse exists for the case where the model is too big for any one of your machines but a group of machines can hold it together. If your machine can run the model alone, running it locally is simpler and more private.

Versus other distributed inference

Other projects also split a model across peers. The usual difference is what a peer sees: many send the prompt or its plain embeddings to another machine, so a peer can read what you asked. Diffuse encrypts every hop end to end and is built so the earliest, most invertible representations can stay on your device. It is also explicit and honest about the current gap on the default path, where the entry node still sees your token ids until local first layers land.

Choosing

• Need speed and do not mind a provider seeing your prompts: hosted API.
• The model fits on your machine: run it locally.
• The model is too big for one machine and you value decentralization: Diffuse.
• You want a private network for a team or lab: Diffuse, self-hosted.