Cracking the Kubernetes Cost Code: Achieving Clarity in Shared and Unexplored Cost Mapping

Client success stories

Enhanced Kubernetes (K8s) cost management through a comprehensive solution that integrated SCAD, AWS Cost Explorer, AWS Glue, Athena, Power Athena Exporter, Amazon QuickSight, and AWS native tags. The project enabled precise unit cost mapping and resolved shared cost complexities by automating the extraction, transformation, and categorization of cost data. This solution empowered organizations to optimize cloud spending, improve resource utilization, and achieve greater financial control in their Kubernetes environments.

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Cracking the Kubernetes Cost Code: Achieving Clarity in Shared and Unexplored Cost Mapping

Challenge

Managing costs in Kubernetes environments can be challenging, particularly when dealing with shared resources and unexplored cost allocations. AWS Cost and Usage Reports (CUR) with Split Cost Allocation Data (SCAD) present caveats that make cost attribution complex: Undefined CPU/memory requests result in missing split cost allocation data. SCAD only reports requested, not actual, allocated resources. Regional accounts may lack full allocation access compared to global accounts. SCAD splits costs only for RunInstance operations, leaving many EC2-related costs unallocated.

Solution

1

Implemented a structured approach using SCAD to enhance Kubernetes cost transparency.

2

Computed unit costs for CPU and memory using AWS’s 9:1 cost ratio.

3

Allocated reserved vs. actual resource usage to reflect accurate cost distribution.

4

Determined unused capacity at the instance level to highlight optimization opportunities.

Key Solutions

Cost Calculation Framework:

Used AWS’s 9:1 CPU-memory cost ratio to derive per-unit costs. Ensured accounting for both allocated and unused resources. Demonstrated breakdowns for sample Kubernetes workloads.

Policy Enforcement & Optimization:

Developed methods to allocate unassigned EC2 costs. Encouraged defining CPU/memory requests for precise attribution. Optimized SCAD integration with AWS-native tools for improved cost visibility.

Objectives & Key Results

Objective 1: Improve Kubernetes cost transparency and allocation accuracy.

01

Achieved 80% accuracy in cost allocation through defined resource requests.

02

Reduced unallocated EC2 costs by 60%.

Objective 2: Optimize resource utilization and reduce wastage.

01

Identified and optimized unused capacity, cutting unallocated resources by 50%.

02

Implemented dashboards to monitor cost efficiency metrics.

Objective 3: Enhance Kubernetes cost governance and best practices.

01

Increased adoption of CPU/memory request definitions by 70%.

02

Standardized cost allocation policies across all projects.

Project Outcome

VirtueCloud’s SCAD-based approach significantly improved Kubernetes cost attribution, achieving 80% accuracy in cost allocation. Unallocated EC2 costs were reduced by 60%, optimizing overall cost efficiency. Governance improved with monitoring dashboards and reporting, while adoption of best practices increased with 70% of teams defining CPU/memory requests.

Future Roadmap

VirtueCloud plans to expand compliance monitoring with additional cost governance frameworks, extend cost optimization strategies across multi-cloud setups, and integrate AI-driven cost forecasting and anomaly detection. Future initiatives will also focus on linking Kubernetes cost insights with security metrics for a holistic view of resource management.