AI infrastructure is no longer a subfield of MLOps — it is the convergence of six traditional disciplines. Here is how Terraform, Kubernetes, GPU scheduling, inference routing, SRE, and FinOps are collapsing into a single operating model.
GPUs are no longer a hidden infrastructure cost. They are a tracked business metric, tied to revenue, margins, and team accountability. This post unpacks why utilization moved from engineering dashboards to board decks, and what it means for the way you build, schedule, and bill ML workloads.
Once a model fits on one accelerator, inference is mostly GEMMs and kv cache lookups. Once it doesn’t, you inherit every problem distributed systems has spent thirty years failing to solve.
Kubernetes has quietly become the de facto control plane for production AI inference. This post walks through the architecture, the GPU plumbing, and the patterns teams use to serve models that actually meet SLOs.
We follow a single agent request from the user prompt down through the AI gateway, inference server, GPU/Kubernetes scheduler, and east-west networking — then turn around and look at how observability, security, and FinOps wrap around the whole thing.