platform engineering
The Telemetry Debt Crisis: Why Cloud-Native Teams are Optimizing the Wrong Metric
Telemetry debt is overwhelming engineering teams with noisy alerts, unused dashboards and rising observability costs. Here’s how to identify, reduce and prevent it ...
A Green Kubernetes Deployment Does Not Mean a Healthy Application
The deployment finishes, kubectl rollout status reports success, and every pod shows Running and Ready. For most teams, that is the moment the release is considered done. Then a customer transaction fails ...
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 ...
Cloud-Native’s Interest Payment Just Came Due
We spent a decade telling each other that cloud-native was how you move fast. Break the monolith into services. Put everything in containers. Declare your infrastructure. Add a service mesh, a GitOps ...
Why Kubernetes Cost Allocation and Cloud Bills Don’t Match
A few months ago, I found myself looking at a Kubernetes cost report and a cloud invoice side by side. The numbers didn’t match. Not because of a bug or a calculation ...
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 ...
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 ...
Ten Years of the Operator Pattern: What We Got Right, What We’d Change
CoreOS introduced the operator pattern in November 2016, and nearly a decade later operators are everywhere. Almost every CNCF graduated project ships one, every database vendor offers one, and every platform team ...
The Inference Bottleneck: Architecting Kubernetes Autoscaling for Production LLMs
Generative AI (GenAI) is moving into production, but native Kubernetes autoscaling is fundamentally broken for large language model (LLM) inference ...
