Product Klip: Komodor's Advanced Cost Optimization Capabilities скачать в хорошем качестве

Product Klip: Komodor's Advanced Cost Optimization Capabilities

This Product Klip covers Komodor's cost optimization features, highlighting how the platform helps users reduce Kubernetes spending while maintaining operational stability. Key features discussed include: Cost Overview Page: Provides a high-level executive summary of total monthly costs, cost trends, and most importantly, active savings (Komodor helped you realize) and potential savings based on actual usage. Cost Allocation Page: Displays cost allocation across clusters over the last 30 days, groupable by cluster, namespace, and service, showing CPU/memory usage, cost, and an optimization score. Workload Right-Sizing: Addresses the common issue of overprovisioned workloads, which cause massive unnecessary spending. Komodor automates this by providing recommendations based on real-life usage, ensuring performance isn't sacrificed. Users can apply recommendations manually or set up automated policies for continuous right-sizing, leading to a clear downward trend in cost and reduced toil. Komodor's automated right-sizing acts for you, and recommendations are based on real-life usage. The active savings table shows workloads continuously right-sized by Komodor, demonstrating a clear downward trend in cost and monthly savings. Intelligent Pod Placement: Solves problems where autoscalers get stuck due to unevictable pods. Komodor identifies these stuck pods and intelligently groups them onto dedicated nodes. This frees up the rest of the cluster, allowing autoscalers to operate effectively and scale down. Before activation, Komodor provides simulations of potential savings, and for activated clusters, it shows CPU and memory usage before and after Komodor's bin packing and the resulting dollar savings. Komodor enhances existing autoscalers rather than replacing them, unlocking up to 40% in additional savings. In summary, Komodor is a Kubernetes management platform rooted in reliability, ensuring every cost recommendation considers cluster health and performance, helping users confidently achieve cost efficiency while reducing operational burden.