AI systems are moving quickly from experiments to production, and that shift is changing the way cloud infrastructure is designed.
In this new series, AI Infrastructure for Cloud Engineers, I’ll look at the technologies behind that shift, including Kubernetes, GPUs, observability, FinOps, GitOps, and platform engineering, and how they come together to run AI workloads reliably at scale.
AI applications are moving beyond prototypes.
Teams are now running model inference, AI agents, embedding services, vector databases, and other AI workloads in production.
Once that happens, a familiar set of engineering questions appears:
