deputyOS vs Ollama
Ollama runs models. deputyOS is the assistant and the appliance around them. deputyOS can use Ollama as its model provider.
Last reviewed
Short answer: This is not a choice. Ollama is a model runtime; deputyOS is the assistant on top. If you only want to chat with a local model, Ollama alone is the right answer.
Where Ollama is the better choice
- The simplest way to run a local model — genuinely one command
- Excellent model library with easy pulls and swaps
- Runs on macOS, Windows and Linux directly, no VM needed
- Small, focused and dependable; it does one thing well
- Huge ecosystem — nearly every local-AI tool speaks to it
Where deputyOS is the better choice
- A complete assistant: channels, memory, tools and scheduling, not just inference
- An OS image with a security baseline, signed updates and rollback
- Remote access, encrypted backup and multi-device management
- Runs headless on a Pi or VPS as an always-on service
- Air-gapped operation with weights and runtime baked in
Different jobs
Ollama serves models. Give it a prompt over an OpenAI-compatible endpoint and it returns tokens. That is the whole job, and it does it well enough that most of the local-AI ecosystem is built on top of it.
deputyOS is what sits above that: an assistant with channels, persistent memory, tools, scheduling and file access, packaged as an appliance with a security baseline, signed updates, remote access and backup.
deputyOS can use Ollama as its model provider. They are layers, not rivals.
When Ollama alone is the right answer
If what you want is to chat with a local model on your laptop, install Ollama and stop. Adding an appliance underneath buys you nothing.
The picture changes when you want it always-on rather than when your laptop is awake; reachable from your phone; connected to Telegram, Slack or email; remembering things across sessions; acting on a schedule; and backed up.
Those are assistant problems, not inference problems, and none of them are Ollama’s job.
What “batteries included” costs
Running Ollama plus an interface plus memory plus channels plus a scheduler means
assembling and maintaining five things. deputyOS ships them baked, which makes the
result deterministic and the update path atomic — but means adding something not in
the image needs a rebuild rather than a docker run.
If you like assembling, that is a real cost and Ollama plus your own stack is the better path.