CloudOptimo Blog

A pod stuck in CrashLoopBackOff with no error in the logs is one of the most common ways engineers meet Kubernetes probes for the first time, usually the hard way. The container isn't broken. The kubelet ran a health check, decided the container had failed it too many times, and restarted it, not because anything inside the container actually crashed.

Namespaces are usually the first tool we reach for when multiple teams need to share a Kubernetes cluster. Add RBAC, ResourceQuotas, and NetworkPolicies, and each team gets a reasonably isolated place to run its workloads. That works well for a lot of environments, but namespaces still share the same Kubernetes control plane. CRDs are cluster-scoped, cluster-level configuration is shared, and every namespace is tied to the same Kubernetes version. vCluster takes a different approach. Instead of giving a team another namespace inside the same Kubernetes API, it gives that team its own Kubernetes control plane.

Amazon EC2 T8i is the latest x86-based member of AWS’s burstable T instance family. It runs on custom sixth-generation Intel Xeon Scalable processors, code-named Granite Rapids, and is built on the AWS Nitro System. The family currently includes nano, micro, small, and medium sizes. T8i is designed for applications whose CPU demand is usually modest but can rise for short periods. Examples include development and staging environments, low-traffic websites, lightweight microservices, demo systems, login services, and some data-processing tasks.

Most teams configure Auto Scaling, watch it work in a load test, and assume the problem is solved. A real traffic spike behaves nothing like a load test. Here is what actually happens inside AWS during those first critical minutes - and why the gap between what teams expect and what they get is where incidents are born.

Slow CI/CD pipeline eating into your team's focus? This blog breaks down the eight things that actually move the needle: smart caching (done right, not just turned on), true parallelization, running only the tests a change actually touches, taming flaky tests without blind retries, redesigning pipeline architecture around fail-fast checks, right-sizing infrastructure, and measuring what matters so none of it quietly regresses. No fluff, no vague "best practices" list, just what actually cuts build time and keeps developers in flow.

A production incident rarely follows the path anyone predicted. Traffic spikes on the wrong service, a deployment interacts badly with a config change from two weeks ago, and the alert that fires points at a symptom three layers away from the cause. Traditional automation handles the incidents it was written for. It doesn't reason through the ones it wasn't.

How a pod actually proves its identity to AWS without a static credential anywhere, the trust policy mistake that quietly grants cluster-wide access, and why the newer, simpler replacement still cannot fully retire the older one. A pod needs to read from an S3 bucket. The easy way is to bake an AWS access key into an environment variable or a Secret and move on. The problem shows up later: that key does not expire, it is not scoped to just that one pod, and if it ever leaks, whoever has it can use it from anywhere, indefinitely, with no connection to the cluster at all.

Serverless computing has become a common architecture for applications that need flexible execution without directly managing servers. Two prominent options are Cloudflare Workers and AWS Lambda, but they approach serverless execution from different infrastructure models.

A Kubernetes Pod can run one or more containers that work together as a single application unit. Traditionally, CPU and memory requirements have been defined mainly at the container level, even when several containers belong to the same Pod. Kubernetes has been extending this model so resources can also be defined for the Pod as a whole. Pod-Level Resources became Beta in Kubernetes 1.34. Kubernetes 1.37 builds on this by moving Pod-Level Resource Managers to Beta, allowing the kubelet’s CPU, Memory, and Topology Managers to use Pod-level resource specifications when making allocation decisions. The feature remains disabled by default.