Why On-Device AI? QVAC vs Cloud AI, Answered Simply
Fast, plain-language answers on QVAC vs cloud AI — privacy, offline use, cost, and the catch, in one page.
The single biggest choice in how AI is built is where it runs. Cloud AI runs in a data center and needs your data sent there. QVAC runs on your device and keeps your data put. This is the short, plain-answer version of that comparison — for the full breakdown, see QVAC vs Cloud AI.
Is on-device AI actually more private?
Yes, and in a stronger way than cloud "privacy" usually means. With cloud AI, privacy is a promise about what a company does with data you already sent it. With QVAC, your data never leaves the device — so there's no server to leak it, no policy to trust, nothing to hand over. It's privacy by architecture, not by policy.
Does it work offline?
Yes. Once the model is downloaded, QVAC-powered apps work with no internet at all — plane, tunnel, off-grid. Cloud AI can't, by definition, because it needs to reach a server.
Is it cheaper?
For usage, yes. Cloud AI charges per request, so heavy use scales your costs with every user and every action. QVAC runs on the user's own hardware, so there's no per-request meter. The trade is that the user needs a reasonably capable device.
Is it faster?
For the network part, yes — there's no round trip to a data center, so responses start instantly. The actual generation speed then depends on the device's hardware and the model size.
What's the catch?
Capability ceiling. The largest, most capable AI models can't fit on a phone, so a local model won't match a frontier cloud model on the very hardest tasks. On-device design leans on capable small models and good retrieval rather than one giant model. For most everyday app features — writing help, categorization, search, transcription, Q&A over your own data — that's more than enough.
So which should I choose?
- The data is personal (notes, health, money)? → on-device (QVAC) is the better fit.
- You need frontier capability on non-sensitive data and users are always online? → cloud may fit better.
- Many products use both — cloud for heavy non-sensitive tasks, on-device for anything private.
Why we went on-device
At localhost/AI, our upcoming apps will handle the most personal data people own. For that, "send it to a data center" was never acceptable, however capable the model. QVAC lets us build real AI features that keep your data from ever leaving your device. Here's how it works.
Common questions
Is on-device AI actually more private?
Yes, and in a stronger way than cloud "privacy" usually means. With cloud AI, privacy is a promise about what a company does with data you already sent it. With QVAC, your data never leaves the device — so there's no server to leak it, no policy to trust, nothing to hand over. It's privacy by architecture, not by policy.
Does it work offline?
Yes. Once the model is downloaded, QVAC-powered apps work with no internet at all — plane, tunnel, off-grid. Cloud AI can't, by definition, because it needs to reach a server.
Is it cheaper?
For usage, yes. Cloud AI charges per request, so heavy use scales your costs with every user and every action. QVAC runs on the user's own hardware, so there's no per-request meter. The trade is that the user needs a reasonably capable device.
Is it faster?
For the network part, yes — there's no round trip to a data center, so responses start instantly. The actual generation speed then depends on the device's hardware and the model size.
What's the catch?
Capability ceiling. The largest, most capable AI models can't fit on a phone, so a local model won't match a frontier cloud model on the very hardest tasks. On-device design leans on capable small models and good retrieval rather than one giant model. For most everyday app features — writing help, categorization, search, transcription, Q&A over your own data — that's more than enough.