What’s putting your K8s workloads at risk? You probably didn’t immediately think of memory and CPU resources—yet, these pose significant threats to cost and performance in your public cloud Kubernetes and OpenShift deployments. Learn about the top 12 K8s risks and how you can visualize the spread of risk in your containers deployment. You'll also hear a methodology for drilling down to individual misconfigurations and resolving them.

Complex cloud container environments worldwide are fraught with: Watch to understand our findings and hear strategies for achieving operational excellence in container FinOps.

Densify’s histograms provide a unique way to visualize the resource health of your environment. Histograms are a powerful way to see the efficiencies and inefficiencies for container CPU Requests and Limits, as well as Memory Requests and Limits. Explore this analysis for your own environment.

Cost analysis and allocation tools like CloudHealth, CloudCheckr, and Cloudability play an important role in many organizations’ FinOps journeys by assisting with keeping Finance informed, enabling forecasting, and driving accountability. These tools may also help provide visibility and direction around long-term purchases like Savings Plans and RIs. But, slicing and dicing your cloud costs is only the tip of the FinOps iceberg: 85%+ of your cloud savings potential lies beyond refining how you buy cloud services. You also need to optimize what you’re actually purchasing.

See how it is possible to visualize your entire Kubernetes and OpenShift deployments and see a spread of CPU and memory resource risks to your workload performance and container infrastructure costs.

Learn how to accelerate the operationalization of resource optimizations in your cloud and container infrastructures. Pegah Garousi describes options for automating reporting of optimizations and for subscribing to optimization directives and recommendations.

Most containers, pods, and namespaces in Kubernetes and OpenShift have been deployed with incorrect CPU and memory resource specifications. It isn’t just suboptimal DevOps—it’s dangerous from a service delivery perspective. Watch and see how managing capacity operationally prevents pods from swallowing unused capacity, frees nodes, and keeps the OOM killer away. This bulletproof strategy for maximizing utilization continuously integrates the optimal resources into the source code controlling your nodes and clusters.

Cost analysis and allocation tools like CloudHealth, CloudCheckr, and Cloudability play an important role in many organizations’ FinOps journeys by assisting with keeping Finance informed, enabling forecasting, and driving accountability. These tools may also help provide visibility and direction around long-term purchases like Savings Plans and RIs. But, slicing and dicing your cloud costs is only the tip of the FinOps iceberg: 85%+ of your cloud savings potential lies beyond refining how you buy cloud services. You also need to optimize what you’re actually purchasing.

Engineering job #1 is maximizing availability and performance. Engineering must proactively balance workload and service delivery with finding efficiencies and ensuring optimal cloud spend.The only way for engineers to tackle FinOps is to embed more awareness of capacity and cost into their operational processes. Watch and learn.

Three reasons you need optimization for your K8s resources: Watch this training now and learn how you can start optimizing the compute resources that power your Kubernetes environments.