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| KUBERNETES COST ALLOCATION AND CHARGEBACK REPORT | |
| Environment: Production EKS Cluster (us-east-1) | |
| Reporting Period: September 2024 | |
| EXECUTIVE SUMMARY | |
| Total cluster cost: $124,842 | |
| Allocated to teams: $108,240 (86.7%) | |
| Unallocated (shared services): $16,602 (13.3%) | |
| Top 3 cost centers: | |
| 1. Data Science Team: $42,880 (34.4%) | |
| 2. Backend Engineering: $31,240 (25.0%) | |
| 3. Frontend/Mobile: $18,420 (14.8%) | |
| Cost efficiency metrics: | |
| - CPU utilization: 42% (target: 65%) | |
| - Memory utilization: 38% (target: 60%) | |
| - Wasted resources: $34,280/month (27.5%) | |
| CLUSTER INFRASTRUCTURE COSTS | |
| Node Groups: | |
| - General Purpose (c5.2xlarge): $28,440 (18 nodes * 720 hours * $2.20/hour) | |
| - Memory Optimized (r5.2xlarge): $31,680 (20 nodes * 720 hours * $2.20/hour) | |
| - GPU (p3.2xlarge): $42,240 (14 nodes * 720 hours * $4.20/hour) | |
| Control Plane: $2,160 (3 master nodes) | |
| Load Balancers: $1,840 (8 ALBs) | |
| EBS Volumes: $8,420 (persistent storage) | |
| Data Transfer: $6,248 (inter-AZ, internet egress) | |
| Monitoring (Prometheus, Grafana): $3,814 | |
| COST ALLOCATION BY NAMESPACE | |
| namespace: data-science | |
| Total cost: $42,880 | |
| Pods: 847 | |
| CPU request: 2,840 cores | |
| Memory request: 11.2 TB | |
| GPU request: 48 GPUs | |
| Top workloads: | |
| - ml-training-job-* : $24,240 (GPU-intensive) | |
| - jupyter-notebooks-* : $8,640 (24/7 development environments) | |
| - data-pipeline-etl : $6,420 | |
| Optimization opportunities: | |
| - 18 idle Jupyter notebooks ($4,320/month waste) | |
| - Training jobs during business hours (use spot instances) → Save $12,120/month | |
| namespace: backend-api | |
| Total cost: $31,240 | |
| Pods: 1,248 | |
| CPU request: 840 cores | |
| Memory request: 3.4 TB | |
| Top workloads: | |
| - user-service : $8,420 | |
| - payment-processor : $6,880 | |
| - notification-engine : $4,240 | |
| - order-management : $3,880 | |
| Efficiency: 62% CPU utilization (good) | |
| Recommendation: Increase resource limits slightly for headroom | |
| namespace: frontend | |
| Total cost: $18,420 | |
| Pods: 624 | |
| CPU request: 420 cores | |
| Memory request: 1.2 TB | |
| Over-provisioned: 28% CPU utilization | |
| Recommendation: Reduce CPU requests by 40% → Save $7,368/month | |
| namespace: mobile-backend | |
| Total cost: $15,700 | |
| Workloads: | |
| - ios-api-gateway : $6,240 | |
| - android-api-gateway : $5,880 | |
| - push-notification-service : $3,580 | |
| CHARGEBACK BY TEAM | |
| Team: Data Science & ML | |
| September cost: $42,880 | |
| Year-to-date: $384,240 | |
| Budget: $420,000/year | |
| % of budget used: 91.5% | |
| Forecast: Over budget by $50,160 if current trend continues | |
| Team: Backend Engineering | |
| September cost: $31,240 | |
| Year-to-date: $274,800 | |
| Budget: $360,000/year | |
| % of budget used: 76.3% | |
| Status: On track | |
| Team: Frontend/Mobile | |
| September cost: $34,120 (combined) | |
| Year-to-date: $288,420 | |
| Budget: $300,000/year | |
| % of budget used: 96.1% | |
| Status: Nearly at budget | |
| Team: DevOps/Platform | |
| September cost: $16,602 (shared infrastructure) | |
| Allocated pro-rata to teams in monthly bills | |
| RESOURCE UTILIZATION ANALYSIS | |
| CPU Utilization by Team: | |
| - Data Science: 81% (efficient) | |
| - Backend: 62% (good) | |
| - Frontend: 28% (over-provisioned - needs rightsizing) | |
| - Mobile: 54% (acceptable) | |
| Memory Utilization by Team: | |
| - Data Science: 72% (good) | |
| - Backend: 48% (moderate waste) | |
| - Frontend: 22% (significant waste) | |
| - Mobile: 59% (acceptable) | |
| OPTIMIZATION RECOMMENDATIONS | |
| 1. Vertical Pod Autoscaler (VPA) | |
| Implement VPA for Frontend team → Estimated savings: $7,400/month | |
| 2. Spot Instances for ML Training | |
| Move ML training to spot nodes (70% discount) → Save $16,968/month | |
| 3. Idle Resource Cleanup | |
| Terminate 18 idle Jupyter notebooks → Save $4,320/month | |
| 4. Schedule Non-Production Workloads | |
| Stop dev/staging environments nights/weekends → Save $5,840/month | |
| Total monthly savings potential: $34,528 (27.7% reduction) | |
| CHARGEBACK INVOICE DETAILS | |
| Team: Data Science | |
| Compute: $38,240 | |
| Storage: $2,840 | |
| Network: $1,800 | |
| ------------------------- | |
| Total: $42,880 | |
| Contact: Emily Watson (emily.watson@techcorp.com) | |
| Cost center: CC-4201 | |
| Team: Backend Engineering | |
| Compute: $28,440 | |
| Storage: $1,680 | |
| Network: $1,120 | |
| ------------------------- | |
| Total: $31,240 | |
| Contact: Alex Kumar (alex.kumar@techcorp.com) | |
| Cost center: CC-4202 | |
| Billing contact for questions: finops@techcorp.com | |
| Dashboard: https://kubecost.techcorp.com (SSO login) | |