TLDR Tech / DevOps
Building a Kubernetes-native pattern for AI infrastructure at scale
Scaling AI on Kubernetes requires moving from simple model deployment to a unified, Kubernetes-native architecture that manages model lifecycle, inference routing, and GPU scheduling declaratively for reliable, event-driven workloads. Using tools like KAITO, liteLLM, and Flex Nodes enables consistent APIs, elastic multi-cloud GPU utilization, and predictable operations, reducing fragmentation and supporting scalable, low-latency AI infrastructure.
Read the full story