Operations | Monitoring | ITSM | DevOps | Cloud

Deployments fail for dozens of reasons. Most of them are obvious from the error messages or pod events. But when a deployment rolls out successfully according to Kubernetes but your application starts experiencing latency spikes and error rate increases, the investigation becomes significantly harder. This scenario walks through a configuration drift incident where the deployment appeared healthy but available replicas were constantly flapping, creating cascading reliability issues.

Static delivery pipelines are becoming a bottleneck. The best CI/CD tools for Kubernetes are those that move beyond simple code builds to provide total environment orchestration and developer self-service.

Container security has finally gone mainstream. When Docker announced hardened container images in late 2025, complete with minimal attack surfaces, non-root defaults, continuous CVE scanning, and automated updates, the response was enthusiastic. For teams managing their own infrastructure, this was a real step forward. Secure-by-default containers are no longer niche or expensive. They are expected.

As Kubernetes platforms scale, one part of the system consistently resists standardization and predictability: networking. While compute and storage have largely matured into predictable, operationally stable subsystems, networking remains a primary source of complexity and operational risk This complexity is not the result of missing features or immature technology.

Kubernetes v1.35 marks an important turning point for cluster networking. The IPVS backend for kube-proxy has been officially deprecated, and future Kubernetes releases will remove it entirely. If your clusters still rely on IPVS, the clock is now very much ticking. Staying on IPVS is not just a matter of running older technology. As upstream support winds down, IPVS receives less testing, fewer fixes, and less attention overall.

Is the "Heroku Tax" draining your budget? Compare Heroku vs. Kubernetes in 2026. Learn how to solve complex orchestration challenges, like queue-based autoscaling and microservice sprawl, without the DevOps toil.

Is the EKS operational burden outweighing its benefits? Learn how to migrate from EKS to ECS, the technical trade-offs of AWS-native orchestration, and how to get ECS-level simplicity without losing Kubernetes power.

When a pod fails during a TensorFlow training job, the investigation usually starts with the obvious questions. The answers rarely come quickly, especially when the failure involves GPU hardware that most engineers don’t troubleshoot regularly. This scenario walks through an actual GPU hardware failure and shows how AI-augmented investigation changes both the time to resolution and the expertise required to handle it.