Many AI applications work well when a handful of users interact with them. The real challenge begins when thousands or millions of requests start hitting your system. Large models are computationally expensive, inference latency can grow quickly, and costs can spiral if the system is not designed to scale efficiently.

Scaling AI applications requires rethinking how you handle concurrency, how you manage costs, and how you absorb traffic patterns that are fundamentally different from traditional web traffic.

In this chapter, we explore how to scale AI applications for real-world usage.