Anodot’s Deep Kubernetes Visibility for FinOps

Gain a thorough comprehension of your Kubernetes application expenses. Analyze clusters and nodes to identify overprovisioned Kubernetes pods in terms of memory or CPU usage and optimize their resources accordingly

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Get deep Kubernetes visibility

No other cloud cost management platform provides granular insight into your Kubernetes deployments like Anodot. Track your spending and usage across clusters with detailed reports and dashboards.

With Anodot’s powerful algorithms and multidimensional filters, you can analyze your performance in depth and identify underutilization at the node and pod level.

Anodot provides a tool for FinOps teams to optimize multicloud and K8s ROI, detect cost anomalies, and improve Kubernetes expenses.

Allocate K8s costs accurately

Allocating Kubernetes costs isn’t an easy task. Most Kubernetes clusters are shared services with applications run by any number of teams. This means there’s no direct cost of a specific container. Additionally, FinOps teams must break down costs by compute, storage, data transfer, shared cluster costs, waste, and choose between request, limit or actual usage allocation models.

With Anodot, FinOps teams can accurately allocate K8s spend including shared cluster costs and waste; analyze their cost and usage by cost centers; and perform full allocation across K8s and traditional workloads.

Optimize your Kubernetes environment

Once you have comprehensive K8s visibility and can allocate your costs accurately, your next priority is to continuously look for ways to optimize your Kubernetes clusters, nodes and pods. Many parameters impact a node’s price including the operating system, processor vendor, processor architecture, instance generation, CPU and memory capacity and ratio, and the pricing model.

Anodot provides a shared source of cost visibility and cost optimization recommendations, making continuous improvement a scalable task for multi-stakeholder teams. With Anodot, FinOps teams can fine-tune Kubernetes resource allocation — allocating the correct amount of resources per cluster, namespace/label, node, pod, and container.