Kubernetes cost at every object level, not just the pod.
DigiUsher resolves Kubernetes cost at node, pod, cluster, daemonset, replicaset, deployment and namespace level, across EKS, AKS, GKE, OpenShift and on-premise clusters in one view.
Rightsizing then runs across five dimensions, GPU, CPU, memory, storage and network, because CPU-only rightsizing optimizes the cheapest resource on an AI-era node.
Managed and self-managed clusters land in one view via native cloud mechanisms and label ingestion, cloud and on-premise together, so cost per service is comparable across EKS, AKS, GKE, OpenShift and self-managed Kubernetes alike.
Waste scenarios
Where the money actually goes.
Each scenario carries severity, saving and evidence, and becomes a pull request against the repository that owns the resource, applied only after human approval.
On a node carrying accelerators, CPU is rarely where the money is.
Rightsizing that only reads CPU requests optimizes the cheapest dimension
and reports success while the expensive four go untouched.
01
GPU rightsizing
MIG partition matching, time-slicing candidates and bin-packing improvements. An inference job holding a full accelerator where a partition would serve is among the most expensive routine mistakes in AI infrastructure.
02
CPU and memory requests
Requests versus limits versus observed usage, with VPA, HPA, KEDA and Karpenter behavior taken into account. Over-requesting is the mechanism by which cluster utilization quietly falls below 30%.
03
Storage
Orphaned persistent volumes, snapshot sprawl, over-provisioned IOPS classes and volumes retained after their workload was deleted.
04
Network
Cross-AZ traffic between pods that scheduling could co-locate, idle load balancers, NAT gateway egress and service-mesh overhead.
05
Idle and headroom
Reserved-but-unused capacity reported as its own attributable line rather than distributed silently across tenants. Headroom nobody owns is headroom nobody reduces.
Signature value metric
Cost per service
Efficiency outliers visible across the service catalog; cost per request for platform targets. Because every domain shares one schema, this metric composes with the others into an AI-inclusive cost per customer: one formula, one auditable lineage.
Compose
Across domains
Cost per customer can include this domain's cost alongside cloud, AI
tokens, data credits, Kubernetes pods and SaaS seats. Point tools each
compute a fraction; one schema computes the whole number.
Verify
Savings that landed
Every optimization moves identified → applied → verified-realized, where
the third state means subsequent billing data confirms the reduction.
Most tools report the first and let you assume the third.
Ask
Conversationally
The full data model is exposed over the Model Context Protocol, so
Claude, Copilot, Gemini or an in-house LLM can answer questions about
this domain under the same role-based access control as the dashboards.
Frequently asked questions
Direct answers on Kubernetes cost optimization.
At what level does DigiUsher measure Kubernetes cost?
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Node, pod, cluster, daemonset, replicaset, deployment and namespace level, via native mechanisms such as EKS Split Cost Allocation and AKS cost allocation, plus label ingestion for OpenShift and on-premise clusters.
Which resources does Kubernetes rightsizing cover?
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Five dimensions: GPU (MIG partition matching, time-slicing, bin-packing), CPU, memory, storage and network. In an AI-era cluster CPU is frequently the cheapest resource on the node, so single-dimension rightsizing optimizes the thing that matters least.
Do we still need Kubecost or OpenCost?
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No. DigiUsher ingests Kubernetes cost natively and joins it to the rest of the estate, which cluster-local tools cannot do: a service whose cost is half EKS pods and half Snowflake credits has no answer inside a Kubernetes-only tool. Teams already running OpenCost can keep it as a cluster-local signal.
How is idle cluster capacity handled?
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As its own attributable line rather than overhead spread silently across tenants. Reserved-but-unused capacity, such as cluster headroom, over-requested pods and unattached volumes, is reported so that someone owns the decision to keep or release it.