Side-by-side
| Axis | Ollama | llama.cpp |
|---|---|---|
| Setup time | One-line install. Pull a model, run a prompt. 5 minutes from zero. | Compile from source (or grab a binary). Configure quantisation, threads, GPU offload manually. 30+ minutes for first-timers. |
| Security model | Sane defaults, no fuss. OS keyring not relevant — local inference, no credentials. | Fully local. You control every flag. |
| Model support | Pulls from Ollama's model registry. Supports GGUF and the main families (Llama, Mistral, Qwen, Phi, etc.). | Loads any GGUF. Most cutting-edge quantisations land here first. |
| Cost | Free. Runs anywhere from a Pi 5 to a Mac Studio. | Free. Compiles for any platform with a recent compiler. |
| Ecosystem | OpenAI-compatible API out of the box, integrates with most agent frameworks trivially. | Lower-level — you'd typically wrap it yourself or use it via Ollama (Ollama uses llama.cpp under the hood). |
| Best for | Most self-hosters. Especially anyone integrating with Hermes, Nanobot, ZeroClaw or similar. | Researchers, fine-tuners, anyone who needs specific quantisation schemes or non-standard sampling. |
Verdict
Use Ollama unless you have a specific reason not to. It's a thin convenience layer over llama.cpp — you get the same engine with better ergonomics. For 99% of self-hosted AI use cases, Ollama is the answer.
Notes
- Ollama is built on llama.cpp. Recommending Ollama is recommending llama.cpp with better defaults.
- If you want to run a model that's released as raw GGUF without an Ollama package, you'll touch llama.cpp directly.
- For fine-tuning, neither is the right tool — see Hugging Face's transformers + PEFT.
Going deeper
For the full landscape report including hosting economics, security posture and regulatory context, see the 2026 landscape report. For the OpenClaw-specific history, see the complete OpenClaw timeline.
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