Engineering
Local vs Cloud AI
PawOS's hybrid approach, and why we didn't pick one side.
The local-vs-cloud AI debate usually gets framed as a binary choice. PawOS's architecture treats it as a layering problem instead: execution is local, reasoning is cloud-routed, and memory sits local-first with an explicit opt-in to share.
Execution has to be local because that's where the file system, the terminal, and the browser you're actually using live. Reasoning is cloud-routed because that's where the capable models are, today. Pretending either constraint doesn't exist would mean either a much weaker assistant or a much less honest one.
This is also why PawOS routes reasoning through named Paw models rather than exposing raw provider choice — the router can change which provider backs "Paw Core" without that being a decision you have to manage, while the local execution layer stays exactly where it needs to be: on your machine.