Every AI tool makes a fundamental architectural choice: where does the computation happen? On your device, or on a server somewhere else? That choice has cascading consequences for your privacy, your workflow, your costs, and what the AI can actually do.
Cloud AI: How It Works
Cloud-based AI services all work the same way. When you send a message:
- Your text (and any attached files) travels over the internet to a data center
- A large model — running on thousands of GPUs — processes your request
- The response is sent back to your device
The advantage is clear: you get access to very large, capable models without needing powerful hardware. A monthly subscription gives you access to models that cost tens of millions of dollars to train and millions of dollars per month to serve.
On-Device AI: How It Works
Local AI tools — like Kynora on Mac and iOS — run the model on your own hardware. When you send a message:
- Your text stays on your device
- A model running on your CPU, GPU, or Neural Engine processes it locally
- The response is generated locally
Nothing leaves your device. Not your question, not your document, not the answer. The internet is not involved in the process.
The Full Comparison
| Dimension | Cloud AI | On-Device AI |
|---|---|---|
| Privacy | Data sent to servers | Data never leaves device |
| Works offline | No | Yes |
| Model size | Unlimited (server-side) | Limited by device RAM |
| Monthly cost | $10–$200+ | Zero after initial hardware/app cost |
| Speed | Depends on internet connection | Depends on hardware |
| Data retention | Provider's policy | Zero — nothing stored remotely |
| Air-gap compatible | Never | Yes |
| Regulatory compliance | Requires provider agreements | Inherently local by design |
| Model updates | Automatic server-side | Manual update required |
When Cloud AI Makes Sense
Cloud AI is the right choice when:
- You are asking general knowledge questions where privacy is not a concern
- You need access to the very largest available models for complex reasoning
- You are working on collaborative projects where multiple people need shared AI access
- You do not have a capable local device (older hardware, limited RAM)
- You need real-time web search integrated into your AI workflow
When On-Device AI Makes Sense
On-device AI is the right — and sometimes only — choice when:
- You work with confidential, privileged, or proprietary documents
- You operate in a regulated industry (legal, medical, finance, defense)
- You need offline access — traveling, field work, air travel, secure facilities
- You want permanent privacy guarantees rather than policy-based promises
- You need cited, auditable answers traced to specific sources in your documents
- You want to eliminate per-query costs permanently
The model quality gap is closing. In 2022, local models were dramatically less capable than cloud models. Today, well-tuned 7B–14B models rival earlier-generation cloud models for domain-specific document work — and in RAG pipelines, local models with precise context can outperform larger cloud models on your specific documents.
The Architecture That Matters
For document-centric professional work, the relevant comparison is not a cloud model versus a local model in a vacuum. It is "which tool gives me accurate, cited answers from my specific documents, reliably, with the privacy guarantees my work requires?"
On that question, on-device RAG with a well-implemented local model beats a cloud AI that treats your documents as general context. The local tool knows your document corpus precisely. The cloud tool knows everything and your documents vaguely.
Kynora is built around this insight — on-device AI for professionals who need cited, private answers from their own document collections.