How the two relate
Ollama uses llama.cpp as a supported backend. llama.cpp is the C/C++ inference engine that loads GGUF models and runs them on CPUs and GPUs, while Ollama wraps an engine with installation, model downloads by name, and a local API that stays running in the background. Choosing between them mostly comes down to how much of the engine you want to manage yourself.
When llama.cpp is the better fit
llama.cpp gives you direct control: build options for your hardware, quantization choices, context settings, and exactly which version you run. Its server exposes an OpenAI-compatible API, so it can drop in behind existing client code. It suits teams embedding inference in their own application, targeting unusual hardware, or tuning performance closely enough that a wrapper's defaults get in the way. What you take on is the setup work of getting binaries, sourcing GGUF models, and handling updates yourself.
When Ollama is the better fit
Ollama installs with one command on macOS, Windows, and Linux, pulls models by name from its library, and runs a local REST API with official Python and JavaScript libraries. It adds cloud models for when local hardware isn't enough, and ollama launch connects models directly to coding agents like Claude Code and Codex. The tradeoff is that Ollama manages the engine version and many runtime defaults for you, which is convenient until you need a setting it doesn't expose.
Running either one in production
Both tools run models, and neither routes across providers or scores output quality. Respan traces Ollama calls through a native integration and can register a self-hosted llama.cpp server as a custom model, so local model calls get the same tracing, cost tracking, and evals as 1,000+ hosted models.
llama.cpp