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785b6bd | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 | 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) β 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% β 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 β Estimated savings: $7,400/month 2. Spot Instances for ML Training Move ML training to spot nodes (70% discount) β Save $16,968/month 3. Idle Resource Cleanup Terminate 18 idle Jupyter notebooks β Save $4,320/month 4. Schedule Non-Production Workloads Stop dev/staging environments nights/weekends β 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) |