On-Device AI in Mobile Apps: What You Can Build Today

Modern phones include dedicated neural processors, and both Apple and Google now provide on-device foundation model capabilities and machine-learning frameworks to app developers. That means many AI features can run entirely on the user's phone — fast, private and even offline.

What on-device AI is good at

• Text features: summarising, rewriting, smart replies, extracting dates and addresses
• Image features: object detection, background removal, document scanning, OCR
• Audio features: speech-to-text, wake words, audio classification
• Personalisation: ranking content based on behaviour without sending data to servers
• Offline experiences: features that keep working without a connection

Benefits

• Privacy: data never leaves the device — a strong message for health, finance and personal apps
• Speed: no network round trip
• Cost: no per-request cloud AI bill
• Reliability: works in basements, flights and rural areas

Limitations

• Smaller models are less capable than cloud frontier models
• Not every device supports every feature, especially older and budget Android phones
• App size can grow if you bundle your own models
• Battery and thermal limits for heavy workloads

The hybrid pattern

Run quick, private tasks on the device and send complex requests to the cloud, with the user's consent. Fall back gracefully on devices that lack the capability.

Platform tools

• iOS: Core ML, the Vision and Natural Language frameworks, and Apple's on-device foundation model APIs on supported devices
• Android: ML Kit, LiteRT (formerly TensorFlow Lite) and Gemini Nano on supported devices
• Cross-platform: Flutter and React Native plugins wrapping these native capabilities

Designing the experience

Explain what runs on-device, make AI suggestions easy to accept or ignore, and never block core functions on an AI feature that may be unavailable.

Our iOS and Android teams build on-device AI features with careful device-capability fallbacks.