Operations | Monitoring | ITSM | DevOps | Cloud

Author Jeff Duntemann said a good tool improves how you work, whereas a great tool transforms your thinking. Companies that want to improve their cloud-based operations can rely on cloud metrics as an effective tool for transforming their cloud operations. You can’t fix what you don’t measure. Cloud metrics are the logs of data that a cloud infrastructure or application generates.

Running Kubernetes on AWS? You're probably using Karpenter, the open-source autoscaler that dynamically provisions new instances as your EKS workloads grow. Karpenter launches rightsized instances in real time in response to pending pods, based on available instance types and the resources applications need. It also terminates underutilized nodes to reduce costs.

Google Cloud released Google Kubernetes Engine (GKE) as a commercial version of native Kubernetes (K8s). GKE promises a user-friendly, reliable, and cost-effective service. Yet calculating GKE costs can be daunting, including understanding what you’re paying for and maximizing your return on investment. In this GKE pricing guide, we’ll discuss how GKE pricing works, what it costs, and more.

You spun up Azure SQL for your app backend, added Cosmos DB for global performance, and let your devs explore PostgreSQL freely. Everything worked — until the invoice hit. Your engineers need high availability and performance. Your CFO wants predictability. And you’re stuck trying to untangle what, exactly, is driving your Azure bill. You’re not alone. Between service types, pricing tiers, and throughput models, Azure database pricing can surprise even experienced teams.

Kubernetes was never just about cost savings. It was built to be a robust, scalable, and efficient platform for orchestrating containerized applications. And it was meant to abstract infrastructure away so developers could move quickly and go about their business of developing. But as Kubernetes adoption scaled, so did cloud bills. FinOps tools emerged to rein in spending, but most only scratch the surface.

Many companies are rapidly adopting cloud-native computing services, like containers, microservices, and serverless computing. Unlike monolithic applications, these technologies rely on distributed architectures. Whether you are running them in the cloud, on-premises, or both, distributed systems consist of thousands or millions of processes and components. The challenge now is to make these complex systems’ inner workings visible, controllable, and improvable.

In this blog series we’re examining the Five Myths of Kubernetes Resource Optimization. The fifth and final myth in this series relates to another common assumption of many Kubernetes users: Dynamic Allocation for Apache Spark applications automatically prevents Spark from overprovisioning resources while improving workload utilization levels.

In recent years, cloud financial management has evolved beyond what many cloud stakeholders anticipated. The overwhelm has led too many companies to struggle to accurately monitor, allocate, and optimize their cloud costs. This issue cost companies about 30% of their cloud budgets in 2022 alone, according to Gartner. With FinOps, you can prevent this bleeding without sacrificing innovation. Yet, taking a manual approach to FinOps can be inefficient and error-prone.

Kubernetes (K8s) is the most popular container orchestration platform today. But it can also be quite complex. To overcome this management challenge, you can deploy your Kubernetes containers using the Google Kubernetes Engine, which is a fully managed service. Yet, to get the most from GKE, you still need to follow best practices. The following tips and best practices for monitoring GKE clusters will help you get started.