๐๐๐ฏ๐ฟ๐ถ๐ฑ ๐๐ ๐ฃ๐ถ๐ฝ๐ฒ๐น๐ถ๐ป๐ฒ๐: ๐๐ฝ๐ฝ๐น๐ฒ ๐ข๐ป-๐๐ฒ๐๐ถ๐ฐ๐ฒ + ๐๐น๐ฎ๐๐ฑ๐ฒ ๐๐น๐ผ๐๐ฑ
Stop choosing between speed and intelligence. You can have both in your iOS apps.
I use a tiered inference pipeline for every AI project on iOS. This architecture routes simple tasks to Apple on-device models. It sends complex reasoning tasks to the Claude API.
A protocol-based adapter keeps your code clean. Your feature layer does not need to know which provider answers the request.
How to build it:
Define a Provider Protocol Create a single interface for all AI providers. One version wraps Apple's LanguageModelSession. The other wraps the Anthropic SDK. This allows you to swap engines with a config change.
Build an Intelligent Router The router checks task complexity and token counts.
- Use on-device for sentiment analysis, extraction, or autocomplete. This offers low latency and zero cost.
- Use Claude for chain-of-thought reasoning or long document analysis.
Use Combine for Streaming Wrap both providers in a Combine pipeline. This keeps your UI responsive. Your SwiftUI views subscribe to one publisher. They do not care if the tokens come from Apple Silicon or the cloud.
Manage Your Cloud Budget Use an actor to track token usage. If you hit your daily limit, the router switches to on-device only. Your app stays functional instead of failing.
Enforce Privacy Boundaries Centralize your privacy logic in the router.
- Tier 1 (on-device): Health data and financial records.
- Tier 2 (cloud): Generic content and public data. The router checks data classification before picking a provider.
Key Tips:
- Classify tasks by complexity, not by gut feeling.
- Remember that on-device memory limits your context window.
- Use an adapter to normalize structured outputs from different engines.
- Design for offline-first. On-device should be your baseline.
Building an adapter layer now saves you a rewrite later. This approach optimizes for latency, cost, and privacy all at once.