Designing High-Performance AI APIs: Lessons from Serving Millions of Real-Time Requests

Serving AI inference across mobile browsers, native apps and enterprise integrations is not a single engineering problem. Latency tolerances, client capabilities and failure models all differ. Getting it wrong at scale means degraded experiences for millions of users.

In this session, Wayne Liu, Chief Growth Officer and U.S. President of Perfect Corp., will share concrete engineering lessons from building and operating API-first AI infrastructure that handles millions of real-time interactions across diverse deployment environments.

During this session, Wayne Liu will uncover: - The latency vs. accuracy tradeoff in real-time AI APIs and how to deliberate decisions - Architecture patterns for serving AI across varied clients and what changes between mobile, web and enterprise environments - How abstraction layers that simplify complex inference (such as facial mapping and real-time rendering) work under the hood - Infrastructure decisions that have an outsized impact on cost, reliability and developer experience in high-volume AI deployments