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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 ...
Komodor Brings Autonomous AI to SRE With Reliability-First Cloud Optimization
SRE teams are under constant pressure to balance reliability, performance, and cloud costs. Komodor is tackling the challenge with new AI-powered Capacity Intelligence and Predictive Placement capabilities designed to proactively eliminate infrastructure ...
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 ...
DevZero Launches Automation Platform to Dynamically Rightsize Kubernetes Clusters
DevZero today launched an autonomous infrastructure optimization platform for Kubernetes clusters based on a profiler that continuously monitors clusters, nodes, and individual workloads to build statistical models of demand for resources. Company ...
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 ...
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 ...
Microsoft Introduces Execution Containers to Keep AI Agents in Check
Microsoft Execution Containers (MXC) brings policy-driven, OS-level containment to AI agents on Windows. Here's what enterprises need to know ...
