The Million-Dollar Question: How We Cut EKS Costs by 60% Without Sacrificing Performance

When the CFO of a major financial services company asked us to "make the cloud bill reasonable" while maintaining their strict performance and compliance requirements, we knew we had a challenge ahead. Their monthly AWS bill had grown from $50,000 to $800,000 in just two years as they scaled their EKS-based payment processing platform. The mandate was clear: cut costs significantly without impacting the business.

Six months later, we had reduced their monthly AWS costs by 60% while actually improving system performance and reliability. This is the story of how we did it.

The Cost Crisis: When Success Becomes Expensive

Our client, a payment processor handling 20 million transactions daily, had experienced explosive growth. Their EKS infrastructure had grown organically, with each team adding resources as needed. While this approach supported rapid business growth, it led to significant inefficiencies:

The Starting Point:

• Monthly AWS costs: $800,000
• EKS clusters: 15 (across dev, staging, prod)
• EC2 instances: 400+ (mix of on-demand and reserved)
• Average CPU utilization: 12%
• Average memory utilization: 18%
• Persistent storage: 2.5 PB (mostly unoptimized)
• Data transfer costs: $80,000/month

Business Impact:

• Infrastructure costs growing faster than revenue
• Development teams afraid to deploy due to cost concerns
• CFO questioning the cloud strategy
• Board pressure to control spending

The STAQI Approach: FinOps Meets Engineering Excellence

We developed a comprehensive cost optimization strategy based on four pillars:

1. Visibility and Accountability

2. Right-Sizing and Efficiency

3. Intelligent Scaling

4. Architectural Optimization

Let's dive into each pillar and the specific techniques we used.

Pillar 1: Visibility and Accountability

Implementing Cost Observability

The first step was understanding where money was being spent. We implemented comprehensive cost tracking:

# Cost Allocation Tags for All Resources
apiVersion: v1
kind: ConfigMap
metadata:
  name: cost-allocation-config
  namespace: kube-system
data:
  required-tags.yaml: |
    mandatory_tags:
      - cost-center
      - environment
      - application
      - team
      - business-unit
    
    auto_tagging_rules:
      - resource_type: "aws:ec2:instance"
        tags:
          managed-by: "kubernetes"
          cluster-name: "${CLUSTER_NAME}"
      - resource_type: "aws:ebs:volume"
        tags:
          managed-by: "kubernetes"
          cluster-name: "${CLUSTER_NAME}"

Custom Cost Dashboards

We built real-time cost dashboards that made spending visible to every team:

{
  "dashboard": {
    "title": "EKS Cost Optimization Dashboard",
    "panels": [
      {
        "title": "Daily Costs by Team",
        "type": "graph",
        "targets": [
          {
            "expr": "aws_billing_estimated_charges{currency=\"USD\"} by (team)",
            "legendFormat": "{{team}}"
          }
        ]
      },
      {
        "title": "Cost per Transaction",
        "type": "stat",
        "targets": [
          {
            "expr": "aws_billing_estimated_charges / payment_transactions_total",
            "legendFormat": "Cost per Transaction"
          }
        ],
        "thresholds": [
          {"color": "green", "value": 0},
          {"color": "yellow", "value": 0.02},
          {"color": "red", "value": 0.05}
        ]
      },
      {
        "title": "Resource Utilization vs Cost",
        "type": "scatter",
        "targets": [
          {
            "expr": "node_cpu_utilization",
            "refId": "A"
          },
          {
            "expr": "node_cost_per_hour",
            "refId": "B"
          }
        ]
      }
    ]
  }
}

Team-Level Cost Budgets and Alerts

# Cost Budget Alerts
apiVersion: v1
kind: ConfigMap
metadata:
  name: team-budgets
  namespace: cost-management
data:
  budgets.yaml: |
    teams:
      payments-team:
        monthly_budget: 50000
        alert_thresholds: [60, 80, 90]
        contact: "payments-team@company.com"
      
      fraud-detection:
        monthly_budget: 30000
        alert_thresholds: [70, 85, 95]
        contact: "fraud-team@company.com"
      
      api-gateway:
        monthly_budget: 25000
        alert_thresholds: [65, 80, 90]
        contact: "api-team@company.com"

Results from Visibility:

• Teams became cost-conscious overnight
• 40% reduction in dev/staging resource usage
• Identification of $120,000 in unused resources

Pillar 2: Right-Sizing and Efficiency

Intelligent Resource Right-Sizing

Most workloads were dramatically over-provisioned. We implemented automated right-sizing:

# Automated Right-Sizing Recommendations
import boto3
import pandas as pd
from datetime import datetime, timedelta

class EKSRightSizer:
    def __init__(self):
        self.cloudwatch = boto3.client('cloudwatch')
        self.eks = boto3.client('eks')
        
    def analyze_workload_patterns(self, namespace, days=30):
        """Analyze workload patterns to recommend optimal resource allocation"""
        end_time = datetime.utcnow()
        start_time = end_time - timedelta(days=days)
        
        # Get CPU and memory metrics
        cpu_metrics = self.get_metric_statistics(
            namespace=f"AWS/EKS/{namespace}",
            metric_name="pod_cpu_utilization_over_pod_limit",
            start_time=start_time,
            end_time=end_time
        )
        
        memory_metrics = self.get_metric_statistics(
            namespace=f"AWS/EKS/{namespace}",
            metric_name="pod_memory_utilization_over_pod_limit",
            start_time=start_time,
            end_time=end_time
        )
        
        # Analyze patterns
        cpu_analysis = self.analyze_resource_usage(cpu_metrics)
        memory_analysis = self.analyze_resource_usage(memory_metrics)
        
        return self.generate_recommendations(cpu_analysis, memory_analysis)
    
    def analyze_resource_usage(self, metrics):
        """Analyze resource usage patterns"""
        if not metrics:
            return None
            
        values = [point['Average'] for point in metrics]
        
        return {
            'p50': np.percentile(values, 50),
            'p95': np.percentile(values, 95),
            'p99': np.percentile(values, 99),
            'max': max(values),
            'avg': np.mean(values),
            'std': np.std(values)
        }
    
    def generate_recommendations(self, cpu_analysis, memory_analysis):
        """Generate right-sizing recommendations"""
        recommendations = []
        
        if cpu_analysis:
            # Recommend CPU based on P95 + buffer
            recommended_cpu = cpu_analysis['p95'] * 1.2
            
            if cpu_analysis['avg'] < 0.3:  # Under 30% average utilization
                recommendations.append({
                    'type': 'cpu_reduction',
                    'current_avg': cpu_analysis['avg'],
                    'recommended_limit': recommended_cpu,
                    'potential_savings': self.calculate_cpu_savings(recommended_cpu)
                })
        
        if memory_analysis:
            # Recommend memory based on P99 + buffer
            recommended_memory = memory_analysis['p99'] * 1.3
            
            if memory_analysis['avg'] < 0.4:  # Under 40% average utilization
                recommendations.append({
                    'type': 'memory_reduction',
                    'current_avg': memory_analysis['avg'],
                    'recommended_limit': recommended_memory,
                    'potential_savings': self.calculate_memory_savings(recommended_memory)
                })
        
        return recommendations

Vertical Pod Autoscaler (VPA) Implementation

# VPA for Automatic Right-Sizing
apiVersion: autoscaling.k8s.io/v1
kind: VerticalPodAutoscaler
metadata:
  name: payment-processor-vpa
  namespace: payments
spec:
  targetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: payment-processor
  updatePolicy:
    updateMode: "Auto"
  resourcePolicy:
    containerPolicies:
    - containerName: payment-processor
      maxAllowed:
        cpu: "2"
        memory: "4Gi"
      minAllowed:
        cpu: "100m"
        memory: "128Mi"
      controlledResources: ["cpu", "memory"]
      controlledValues: RequestsAndLimits

Node Pool Optimization

We restructured the entire node architecture:

# Optimized Node Groups for Different Workload Types
module "compute_optimized_nodes" {
  source = "./modules/eks-node-group"
  
  cluster_name    = var.cluster_name
  node_group_name = "compute-optimized"
  
  instance_types = ["c5.large", "c5.xlarge", "c5.2xlarge"]
  capacity_type  = "SPOT"  # 70% cost reduction for fault-tolerant workloads
  
  scaling_config = {
    desired_size = 10
    max_size     = 50
    min_size     = 5
  }
  
  labels = {
    workload-type = "compute-intensive"
    cost-profile  = "optimized"
  }
  
  taints = [{
    key    = "workload-type"
    value  = "compute-intensive"
    effect = "NO_SCHEDULE"
  }]
}

module "memory_optimized_nodes" {
  source = "./modules/eks-node-group"
  
  cluster_name    = var.cluster_name
  node_group_name = "memory-optimized"
  
  instance_types = ["r5.large", "r5.xlarge"]
  capacity_type  = "ON_DEMAND"  # For memory-sensitive workloads
  
  scaling_config = {
    desired_size = 5
    max_size     = 20
    min_size     = 2
  }
  
  labels = {
    workload-type = "memory-intensive"
    cost-profile  = "performance"
  }
}

module "general_purpose_spot" {
  source = "./modules/eks-node-group"
  
  cluster_name    = var.cluster_name
  node_group_name = "general-spot"
  
  instance_types = ["m5.large", "m5.xlarge", "m5a.large", "m5a.xlarge"]
  capacity_type  = "SPOT"
  
  scaling_config = {
    desired_size = 15
    max_size     = 100
    min_size     = 5
  }
  
  labels = {
    workload-type = "general"
    cost-profile  = "spot"
  }
}

Right-Sizing Results:

• Average CPU utilization: 12% → 65%
• Average memory utilization: 18% → 70%
• Node count reduction: 400 → 180 instances
• Monthly savings: $180,000

Pillar 3: Intelligent Scaling

Predictive Auto-Scaling

We implemented predictive scaling based on business patterns:

# Predictive Scaling Based on Transaction Patterns
import pandas as pd
import numpy as np
from sklearn.ensemble import RandomForestRegressor
from datetime import datetime, timedelta

class PredictiveScaler:
    def __init__(self):
        self.model = RandomForestRegressor(n_estimators=100, random_state=42)
        self.trained = False
        
    def train_model(self, historical_data):
        """Train the model on historical transaction and resource data"""
        # Features: hour, day_of_week, day_of_month, is_weekend, is_holiday
        features = []
        targets = []
        
        for record in historical_data:
            dt = datetime.fromisoformat(record['timestamp'])
            feature_vector = [
                dt.hour,
                dt.weekday(),
                dt.day,
                1 if dt.weekday() >= 5 else 0,  # weekend
                1 if self.is_holiday(dt) else 0,  # holiday
                record['transaction_volume'],
                record['avg_response_time']
            ]
            features.append(feature_vector)
            targets.append(record['required_replicas'])
        
        self.model.fit(features, targets)
        self.trained = True
        
    def predict_scaling_needs(self, forecast_hours=24):
        """Predict scaling needs for the next N hours"""
        if not self.trained:
            raise ValueError("Model must be trained first")
        
        predictions = []
        current_time = datetime.utcnow()
        
        for hour_offset in range(forecast_hours):
            future_time = current_time + timedelta(hours=hour_offset)
            
            # Get predicted transaction volume for this time
            predicted_volume = self.predict_transaction_volume(future_time)
            
            feature_vector = [
                future_time.hour,
                future_time.weekday(),
                future_time.day,
                1 if future_time.weekday() >= 5 else 0,
                1 if self.is_holiday(future_time) else 0,
                predicted_volume,
                100  # target response time
            ]
            
            predicted_replicas = self.model.predict([feature_vector])[0]
            
            predictions.append({
                'timestamp': future_time.isoformat(),
                'predicted_volume': predicted_volume,
                'recommended_replicas': max(1, int(predicted_replicas))
            })
        
        return predictions
    
    def generate_scaling_schedule(self, predictions):
        """Generate HPA scaling schedule"""
        schedule = []
        
        for pred in predictions:
            schedule.append({
                'time': pred['timestamp'],
                'action': 'scale',
                'replicas': pred['recommended_replicas'],
                'reason': f"Predicted volume: {pred['predicted_volume']} TPS"
            })
        
        return schedule

Custom Metrics Auto-Scaling

# HPA with Custom Business Metrics
apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: payment-processor-hpa
  namespace: payments
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: payment-processor
  minReplicas: 5
  maxReplicas: 200
  metrics:
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 70
  - type: Resource
    resource:
      name: memory
      target:
        type: Utilization
        averageUtilization: 80
  - type: Pods
    pods:
      metric:
        name: transactions_per_second_per_pod
      target:
        type: AverageValue
        averageValue: "50"
  - type: Object
    object:
      metric:
        name: queue_depth
      describedObject:
        apiVersion: v1
        kind: Service
        name: payment-queue
      target:
        type: Value
        value: "30"
  behavior:
    scaleUp:
      stabilizationWindowSeconds: 60
      policies:
      - type: Percent
        value: 100
        periodSeconds: 60
    scaleDown:
      stabilizationWindowSeconds: 300
      policies:
      - type: Percent
        value: 50
        periodSeconds: 180

Cluster Auto-Scaling Optimization

# Optimized Cluster Autoscaler Configuration
apiVersion: apps/v1
kind: Deployment
metadata:
  name: cluster-autoscaler
  namespace: kube-system
spec:
  template:
    spec:
      containers:
      - image: k8s.gcr.io/autoscaling/cluster-autoscaler:v1.21.0
        name: cluster-autoscaler
        command:
        - ./cluster-autoscaler
        - --v=4
        - --stderrthreshold=info
        - --cloud-provider=aws
        - --skip-nodes-with-local-storage=false
        - --expander=least-waste
        - --node-group-auto-discovery=asg:tag=k8s.io/cluster-autoscaler/enabled,k8s.io/cluster-autoscaler/eks-cluster-name
        - --balance-similar-node-groups
        - --scale-down-enabled=true
        - --scale-down-delay-after-add=2m
        - --scale-down-unneeded-time=3m
        - --scale-down-utilization-threshold=0.5
        - --skip-nodes-with-system-pods=false
        - --max-node-provision-time=15m

Scaling Optimization Results:

• 45% reduction in over-provisioning
• 30% faster scale-up response
• 50% reduction in idle nodes
• Monthly savings: $150,000

Pillar 4: Architectural Optimization

Storage Optimization

Storage costs were a major component. We implemented a comprehensive storage strategy:

# Optimized Storage Classes
apiVersion: storage.k8s.io/v1
kind: StorageClass
metadata:
  name: gp3-optimized
provisioner: ebs.csi.aws.com
parameters:
  type: gp3
  iops: "3000"          # Baseline IOPS for gp3
  throughput: "125"     # Baseline throughput
  encrypted: "true"
allowVolumeExpansion: true
volumeBindingMode: WaitForFirstConsumer
reclaimPolicy: Delete

---
apiVersion: storage.k8s.io/v1
kind: StorageClass
metadata:
  name: gp3-high-performance
provisioner: ebs.csi.aws.com
parameters:
  type: gp3
  iops: "16000"         # High IOPS for databases
  throughput: "1000"    # High throughput
  encrypted: "true"
allowVolumeExpansion: true
volumeBindingMode: WaitForFirstConsumer
reclaimPolicy: Delete

---
apiVersion: storage.k8s.io/v1
kind: StorageClass
metadata:
  name: efs-shared
provisioner: efs.csi.aws.com
parameters:
  provisioningMode: efs-ap
  fileSystemId: fs-0123456789abcdef0
  directoryPerms: "0755"
reclaimPolicy: Delete

Data Lifecycle Management

# Automated Data Lifecycle Management
import boto3
from datetime import datetime, timedelta

class DataLifecycleManager:
    def __init__(self):
        self.s3 = boto3.client('s3')
        self.ec2 = boto3.client('ec2')
        
    def optimize_s3_storage(self, bucket_name):
        """Implement intelligent tiering for S3 data"""
        lifecycle_config = {
            'Rules': [
                {
                    'ID': 'transaction-logs-lifecycle',
                    'Status': 'Enabled',
                    'Filter': {'Prefix': 'transaction-logs/'},
                    'Transitions': [
                        {
                            'Days': 30,
                            'StorageClass': 'STANDARD_IA'
                        },
                        {
                            'Days': 90,
                            'StorageClass': 'GLACIER'
                        },
                        {
                            'Days': 365,
                            'StorageClass': 'DEEP_ARCHIVE'
                        }
                    ]
                },
                {
                    'ID': 'application-logs-lifecycle',
                    'Status': 'Enabled',
                    'Filter': {'Prefix': 'application-logs/'},
                    'Transitions': [
                        {
                            'Days': 7,
                            'StorageClass': 'STANDARD_IA'
                        },
                        {
                            'Days': 30,
                            'StorageClass': 'GLACIER'
                        }
                    ],
                    'Expiration': {'Days': 2555}  # 7 years for compliance
                }
            ]
        }
        
        self.s3.put_bucket_lifecycle_configuration(
            Bucket=bucket_name,
            LifecycleConfiguration=lifecycle_config
        )
    
    def cleanup_unused_ebs_volumes(self):
        """Identify and clean up unused EBS volumes"""
        unused_volumes = []
        
        volumes = self.ec2.describe_volumes()['Volumes']
        
        for volume in volumes:
            if volume['State'] == 'available':  # Not attached
                create_time = volume['CreateTime']
                days_old = (datetime.now(create_time.tzinfo) - create_time).days
                
                if days_old > 7:  # Unused for more than 7 days
                    unused_volumes.append({
                        'VolumeId': volume['VolumeId'],
                        'Size': volume['Size'],
                        'Cost': volume['Size'] * 0.1 * days_old,  # Rough cost calculation
                        'DaysOld': days_old
                    })
        
        return unused_volumes

Network Optimization

# Optimized VPC and Networking Configuration
apiVersion: v1
kind: ConfigMap
metadata:
  name: vpc-optimization-config
  namespace: kube-system
data:
  network-optimization.yaml: |
    # Use VPC endpoints to reduce data transfer costs
    vpc_endpoints:
      - service: s3
        type: gateway
        route_table_ids: ["rtb-12345", "rtb-67890"]
      
      - service: dynamodb
        type: gateway
        route_table_ids: ["rtb-12345", "rtb-67890"]
      
      - service: ecr.api
        type: interface
        subnet_ids: ["subnet-12345", "subnet-67890"]
        security_group_ids: ["sg-abcdef"]
      
      - service: ecr.dkr
        type: interface
        subnet_ids: ["subnet-12345", "subnet-67890"]
        security_group_ids: ["sg-abcdef"]
    
    # NAT Gateway optimization
    nat_gateways:
      strategy: "single_az"  # Use one NAT gateway instead of one per AZ
      instance_type: "nat.large"  # Right-size based on usage

Architectural Optimization Results:

• Storage costs reduced by 70%
• Data transfer costs reduced by 80%
• Network optimization savings: $90,000/month

Advanced Cost Optimization Techniques

Spot Instance Strategy

# Mixed Instance Type Configuration
resource "aws_autoscaling_group" "eks_spot_nodes" {
  name                = "eks-spot-nodes"
  vpc_zone_identifier = var.private_subnet_ids
  target_group_arns   = []
  health_check_type   = "EC2"
  
  min_size         = 5
  max_size         = 100
  desired_capacity = 20
  
  mixed_instances_policy {
    launch_template {
      launch_template_specification {
        launch_template_id = aws_launch_template.eks_spot.id
        version           = "$Latest"
      }
      
      override {
        instance_type     = "m5.large"
        weighted_capacity = "1"
      }
      
      override {
        instance_type     = "m5.xlarge"
        weighted_capacity = "2"
      }
      
      override {
        instance_type     = "m5a.large"
        weighted_capacity = "1"
      }
      
      override {
        instance_type     = "m5a.xlarge"
        weighted_capacity = "2"
      }
    }
    
    instances_distribution {
      on_demand_allocation_strategy            = "prioritized"
      on_demand_base_capacity                  = 5
      on_demand_percentage_above_base_capacity = 20
      spot_allocation_strategy                 = "diversified"
      spot_instance_pools                      = 4
      spot_max_price                          = "0.10"
    }
  }
  
  tag {
    key                 = "kubernetes.io/cluster/eks-cluster"
    value               = "owned"
    propagate_at_launch = true
  }
}

Reserved Instance Optimization

# RI Purchase Recommendation Engine
import boto3
import pandas as pd
from datetime import datetime, timedelta

class RIOptimizer:
    def __init__(self):
        self.ce = boto3.client('ce')  # Cost Explorer
        self.ec2 = boto3.client('ec2')
        
    def analyze_ri_opportunities(self, lookback_days=90):
        """Analyze usage patterns for RI opportunities"""
        end_date = datetime.now().strftime('%Y-%m-%d')
        start_date = (datetime.now() - timedelta(days=lookback_days)).strftime('%Y-%m-%d')
        
        # Get usage data
        response = self.ce.get_dimension_values(
            TimePeriod={
                'Start': start_date,
                'End': end_date
            },
            Dimension='SERVICE',
            Context='COST_AND_USAGE',
            SearchString='Amazon Elastic Compute Cloud'
        )
        
        # Analyze instance usage patterns
        usage_data = self.get_detailed_usage(start_date, end_date)
        recommendations = self.generate_ri_recommendations(usage_data)
        
        return recommendations
    
    def generate_ri_recommendations(self, usage_data):
        """Generate RI purchase recommendations"""
        recommendations = []
        
        # Group by instance type and analyze usage
        for instance_type, data in usage_data.groupby('instance_type'):
            avg_usage = data['usage_hours'].mean()
            
            # Recommend RI if usage is consistent (>70% of time)
            if avg_usage > (24 * 0.7):  # More than 70% utilization
                hourly_rate = self.get_on_demand_rate(instance_type)
                ri_rate = self.get_ri_rate(instance_type, term='1yr', payment='partial')
                
                annual_savings = (hourly_rate - ri_rate) * 24 * 365
                
                recommendations.append({
                    'instance_type': instance_type,
                    'recommended_quantity': int(avg_usage / 24),
                    'annual_savings': annual_savings,
                    'payback_period_months': self.calculate_payback_period(instance_type),
                    'confidence_score': min(avg_usage / (24 * 0.9), 1.0)
                })
        
        return sorted(recommendations, key=lambda x: x['annual_savings'], reverse=True)

Container Image Optimization

# Optimized Multi-Stage Build
FROM node:18-alpine AS dependencies
WORKDIR /app
COPY package*.json ./
RUN npm ci --only=production && npm cache clean --force

FROM node:18-alpine AS build
WORKDIR /app
COPY package*.json ./
RUN npm ci
COPY . .
RUN npm run build

FROM node:18-alpine AS runtime
RUN addgroup -g 1001 -S nodejs
RUN adduser -S nextjs -u 1001

WORKDIR /app
COPY --from=dependencies /app/node_modules ./node_modules
COPY --from=build /app/dist ./dist
COPY --from=build /app/package.json ./package.json

USER nextjs
EXPOSE 3000
ENV NODE_ENV=production

CMD ["npm", "start"]

# Final image size: 150MB (down from 1.2GB)

The Complete Results: 60% Cost Reduction

Monthly Cost Breakdown

Before Optimization:

• EC2 instances: $450,000
• EBS storage: $120,000
• Data transfer: $80,000
• Load balancers: $40,000
• Other services: $110,000
• Total: $800,000

After Optimization:

• EC2 instances: $180,000 (60% spot, 40% RI)
• EBS storage: $36,000 (GP3, lifecycle management)
• Data transfer: $16,000 (VPC endpoints, optimization)
• Load balancers: $25,000 (rightsized ALBs)
• Other services: $63,000
• Total: $320,000

Net Savings: $480,000/month (60% reduction)

Performance Improvements

Despite the massive cost reduction, we actually improved performance:

• Response time: 250ms → 180ms (28% improvement)
• Throughput: 15,000 TPS → 22,000 TPS (47% improvement)
• Availability: 99.9% → 99.95% uptime
• Deployment speed: 15 minutes → 6 minutes

Business Impact

The cost optimization project delivered significant business value:

• Annual savings: $5.7 million
• ROI on optimization: 2,400%
• Freed up budget: Enabled expansion into 3 new markets
• Team confidence: Developers no longer afraid to innovate
• Competitive advantage: Lower transaction processing costs

Key Lessons and Best Practices

1. Start with Visibility

You can't optimize what you can't see. Implement comprehensive cost monitoring before making changes.

2. Culture Change is Critical

Technical optimization alone isn't enough. Teams need to understand and care about costs.

3. Automate Everything

Manual cost optimization doesn't scale. Build automation into your processes.

4. Performance and Cost Can Improve Together

With the right approach, cost optimization often leads to better performance.

5. Continuous Optimization

Cost optimization is not a one-time project but an ongoing discipline.

Tools and Technologies Used

Cost Monitoring and Analysis

• AWS Cost Explorer and Budgets
• Kubernetes Resource Recommender
• Kubecost for granular Kubernetes cost analysis
• Custom Prometheus metrics for real-time monitoring

Automation and Optimization

• Terraform for infrastructure as code
• Kubernetes VPA and HPA
• Custom Python scripts for analysis and automation
• AWS Spot Fleet and Auto Scaling Groups

Performance Monitoring

• Prometheus and Grafana
• AWS CloudWatch
• Application Performance Monitoring (APM)
• Custom business metrics dashboards

Future Cost Optimization Initiatives

AI-Powered Cost Optimization

We're developing AI models that can:

• Predict optimal instance types for workloads
• Automatically negotiate spot instance bids
• Optimize resource allocation based on business patterns

Serverless Migration

Moving appropriate workloads to serverless architectures:

• AWS Lambda for event-driven processing
• Amazon ECS Fargate for containerized batch jobs
• API Gateway for lightweight API endpoints

Multi-Cloud Cost Arbitrage

Evaluating opportunities to:

• Use different clouds for different workload types
• Take advantage of regional pricing differences
• Implement cross-cloud disaster recovery

Conclusion: The Strategic Value of Cost Optimization

This project proved that cost optimization is not just about saving money—it's about enabling business growth and innovation. By reducing our client's monthly AWS costs by 60% while improving performance, we:

• Freed up $5.7 million annually for business investment
• Improved system reliability and performance
• Built a culture of cost consciousness across engineering teams
• Established processes for ongoing optimization

The techniques we used are applicable to any organization running significant EKS workloads. The key is taking a systematic, data-driven approach that combines technical optimization with cultural change.

Most importantly, we demonstrated that in the cloud, performance and cost efficiency go hand in hand. When you optimize for efficiency, you often end up with better-performing, more reliable systems.

Ready to optimize your EKS costs without sacrificing performance? Contact STAQI Technologies to learn how our proven methodology can deliver similar results for your organization.