On-deviceEdgeMobileLatency
On-Device AI vs API Models: When Small Models Win
•6 min read
On-device models trade absolute capability for latency, privacy, and offline robustness. For many UX paths, that trade is exactly what you want.
The decision is about constraints
If your feature needs sub-200ms interactions, offline support, or strict privacy boundaries, on-device inference becomes compelling.
If your feature requires deep reasoning over large documents, APIs still dominate. Many products succeed with a hybrid approach.
Hybrid patterns
Use on-device models for: intent detection, quick rewrites, and privacy-preserving classification. Escalate to server models for complex tasks.
Cache and reuse embeddings locally where possible, and keep a clean separation between user-private data and server requests.