AI workloads stress cloud architecture in ways traditional applications do not: bursty GPU demand, large datasets that are expensive to move, and inference paths where every hundred milliseconds matters.
The patterns that work are pragmatic. Keep compute close to data. Separate training environments, which can be elastic and spot-priced, from inference environments, which need predictable latency. Cache aggressively. Measure cost per prediction as a first-class metric.
Infrastructure as code and automated pipelines are not optional here — they are how teams iterate on models safely and roll back quickly when something regresses.
We design AI platforms on AWS, Azure and Google Cloud with these principles, choosing managed services where they reduce risk and open tooling where it preserves flexibility.
