Mobile & On-Device AI Instance Matrix
Compare On-Device SLMs (Apple CoreML, Android AICore, WebGPU) against Cloud Foundation Models. Benchmark weekly volume, $/1M pricing, throughput (tok/s), latency, SWE-bench coding, and agent task economics.
AI Model & Autonomous Agent Instance Matrix
Comprehensive pricing, hardware throughput, and benchmark telemetry across all leading cloud foundation models and mobile agent runtimes.
Mobile & Edge AI Engineering Guides
Production architecture, CoreML integration, and cross-platform benchmarks for mobile engineers.
Agentic AI Integration in Mobile Apps
Architect autonomous multi-turn tool-calling loops in React Native, Flutter, and native Swift with on-device execution.
Read GuideReact Native vs Flutter AI Benchmarks
Detailed NPU memory consumption, bridge latency, and WebGPU inference benchmarks for cross-platform mobile apps.
Read GuideFrequently Asked Questions
Common questions about deploying on-device SLMs, LPUs, and cloud AI architectures.
What are the primary advantages of on-device mobile AI?
On-device models (like Apple Foundation 3B via CoreML and Gemini Nano on Android) run locally on device NPUs with zero cloud API costs ($0.00), 100% offline privacy, and zero network round-trip latency.
How does AppZed calculate monthly inference bills?
AppZed calculates your total tokens based on MAU, prompts per user, prompt caching discount rates, and agent tool-calling turns, giving you transparent, real-world monthly cloud cost estimates.