Many AI applications work during a pilot because traffic is low, prompts are short, and someone is watching the logs. Scaling exposes different constraints: long-running requests, provider rate limits, token throughput, queue backlog, budget ceilings, and data stores that were not designed for concurrent retrieval.

Scaling AI applications means controlling concurrency and cost as first-class resources. Adding more web servers helps only if the downstream model, vector store, and budget can absorb the additional work.

In this chapter, we look at the practical pieces of scaling AI applications for real usage.