AI infrastructure
Containers Became the Unit of Speed. AI Agents Are Making VMs the Unit of Trust
The container is not disappearing. But as autonomous agents generate code, install packages and invoke tools, cloud-native infrastructure is placing a VM-grade security boundary around it ...
Autoscaling AI Workloads on Kubernetes With KEDA and What it Means for Agentic Systems
KEDA can scale Kubernetes AI workloads on real demand signals such as queue depth, helping model-serving and agent workloads respond faster while reducing idle compute costs ...
Kubeflow’s Graduation Is a Vote for Kubernetes as the AI Control Plane
Kubeflow’s CNCF graduation signals growing confidence in Kubernetes as a common control plane for production AI workloads, from training and pipelines to governance and inference ...
Kubernetes Wasn’t Built for GPUs. Make It Behave
Kubernetes counts whole GPUs and treats pods as disposable. An LLM pod is neither. Share the silicon with MIG/MPS/time-slicing and stop paying for idle ...
Stop Treating GPUs Like Web Pods
Kubernetes schedules accelerators as opaque integers, and your bill pays for it. Share the silicon, scale on the right signal and keep weights out of the image ...
NVIDIA Is Putting Real Skin in the Open AI Game
Open AI requires community-governed infrastructure and companies willing to contribute code, engineering and costly GPU cycles. NVIDIA is doing exactly that ...
The Foundation Was Already Poured
Techstrong's Experts Exchange this October, Cloud Native Now: The AI Stack, and this November's KubeCon in Salt Lake City are both making the same case for cloud native and AI. The argument ...
Beyond the Model: Why AI Agent Orchestration Requires Cloud-Native Engineering
AI agent orchestration is a distributed systems challenge. Cloud-native engineering provides the resilience, observability, security and scalability needed for production AI ...
Your Model Works in the Notebook and Breaks in the Cluster
A model working in a notebook gives you a particular kind of confidence. The metrics look good, the code runs top to bottom, the researcher demos it, leadership nods, and everyone agrees ...
GitOps Wasn’t Built for Models, and It Shows
GitOps won the deployment argument. Everything goes in Git, the cluster reconciles itself to match, and your repository becomes the one place that tells you what’s actually running. It’s clean and auditable ...
