Operations | Monitoring | ITSM | DevOps | Cloud

In this blog series we are examining the Five Myths of Kubernetes Resource Optimization. So far we’ve looked at Myth 1: Observability and Monitoring and Myth 2: Cluster Autoscaling. Stay tuned for the entire series! The third myth addresses another common assumption of many Kubernetes practitioners: Choosing the right instances will eliminate waste in a cluster.

Running workloads on Google Kubernetes Engine (GKE) delivers impressive scalability and flexibility. Yet, it can also introduce a tricky challenge: tracking GKE costs accurately. Remember, GKE costs rarely scale linearly. Overprovisioned nodes, idle autoscalers, and orphaned workloads can quietly balloon your bill in the background. And while GKE’s native tools offer some visibility, they often miss the full picture.

A decade after launching Reserved Instances (RIs), Amazon Web Services (AWS) introduced Savings Plans as a more flexible alternative to RIs. AWS Savings Plans are not meant to replace Reserved Instances; they are complementary. SPs and RIs have some significant differences that make each better suited to specific uses. For example, while Savings Plans apply to both EC2 and Fargate instances, RIs only apply to EC2 instances. Let’s break down AWS Savings Plans vs.

Cloud computing promised speed, scale, and freedom. And it delivered. Engineers can deploy in seconds. Teams can scale globally overnight. But somewhere between all that freedom and speed, control got blurry. Resources piled up. Budgets ballooned. And suddenly, no one could answer the simple question: What are we paying for and why? Cloud resource management is how we reclaim that control, without slowing down.

Log volume is exploding, costs are rising, and most teams are stuck duct-taping together short-term fixes. During our webinar, "Optimizing Log Management in Datadog: Cut Costs Without Losing Insights," we discuss how DevOps and engineering leaders are navigating the growing pains of observability, especially in environments where tools like Datadog are mission-critical but challenging to manage. Here’s a recap of the key takeaways.

It’s summertime and things are heating up — in a good way! Forgive us for being a little self-centered this month. Some exciting things are happening at CloudZero: Ditch your towel and flip-flops because it’s time to dive in.

OpenShift delivers a lot right out of the box. And for many teams running at enterprise scale, it’s exactly what they need. The platform offers a built-in container registry, observability tools, and service mesh support. You also get integrations for GitOps, serverless, and even ML workflows. OpenShift combines powerful orchestration with developer tooling, CI/CD pipelines, and enterprise-grade security. All under one roof.

The cloud gave us agility, but it also introduced fragmentation. And in most companies, no one’s fully owning the sprawl. One team deploys a new service in a hurry. Another forgets to shut down a dev environment. Meanwhile, batch jobs run 24/7 on oversized instances. And no one quite knows why your bill is $10K higher this month. The result? A growing source of cost overruns, performance headaches, and operational inefficiencies. This is exactly why cloud workload management is so crucial.

AI spending continues to accelerate. In 2025, experts project that companies will collectively spend about $644 billion on generative AI alone — a whopping 76.4% increase from 2024. This puts it a mere $79 billion behind the public cloud as a whole, signaling the most seismic interval of new infrastructure investment since the dawn of the public cloud.