Contributed Content
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 ...
GitOps in Practice: How to Design a Scalable CI/CD Pipeline with GitLab and GKE
A scalable CI/CD pipeline on GitLab and Google Kubernetes Engine starts with one decision: do you treat the pipeline as a delivery system you design, or as a YAML file you copy ...
When Your Cluster Won’t Sit Still: The Hidden Cost of Kubernetes Autonomy During Incidents
I’ve spent the better part of the last few years on the receiving end of Kubernetes pages, both as an operator and as someone building tooling for platform teams. The pattern I’ve ...
Why Kubernetes Admission Control Is Really a Security UX Problem
Most Kubernetes admission webhooks treat security as binary: accept the configuration, or reject it. That binary thinking has matured an entire category of policy engines (OPA Gatekeeper, Kyverno, ValidatingAdmissionPolicy with CEL) that ...
Stop Treating Your Models Like Microservices
A few years ago, it felt like Kubernetes had become the universal answer to infrastructure problems. Teams wanted resiliency? Kubernetes. Faster deployments? Kubernetes. Scalability? Kubernetes again. Eventually, the industry stopped treating cloud-native ...
How Cloud-Native Complexity is Outpacing Test Automation Strategy
Cloud-native architectures evolve faster than most test automation strategies. Understand where the gap is widest and what teams can do about it. ...
Docker Networking in Production: What Most Teams Get Wrong
Docker makes networking feel simple. You run a container, expose a port, add a –network flag and things connect. In development, that is enough. In production, it is where teams quietly accumulate ...
Why Blue-Green Deployments Fail at Scale in Kubernetes — and What Works Instead
While blue-green deployments promise zero downtime, implementing them at scale in Kubernetes introduces hidden resource costs, database sync issues, and session traffic complexities. Explore a practical framework utilizing rolling updates, canaries, and ...
Pod Disruption Budgets: A Field Guide to What Actually Works
In Kubernetes, PodDisruptionBudgets are simple to write, easy to misuse, and cause more “why won’t this node drain?” confusions than any other Kubernetes primitive. After tracing too many node lifecycle automation problems ...
Stop Wasting GPU Budget: Autoscaling AI Inference on Kubernetes with KEDA
The rush to deploy Large Language Models (LLMs) and generative AI has created a massive infrastructure bottleneck. Platform engineering teams are spinning up expensive GPU node pools on Kubernetes, but they are ...
