AWS Cost Optimization (2025): 15 Proven Tactics That Saved Us $50K/Month

*By DevOps Enginer – Cost Management Series* *Published: 2025*

Cloud spending is growing faster than ever. According to Flexera's 2025 Cloud Report, 82% of companies say cloud bills are "too high", and over 40% exceed their AWS budgets every year.

But the real problem isn't AWS being expensive — it's the lack of structured, automated, and continuous cloud cost management.

In this definitive 2025 guide, we break down exactly how we reduced AWS costs by $50,000 per month, using practical, real-world strategies you can apply immediately — even if you have a small team.

This is not a theoretical list. These are battle-tested DevOps + FinOps tactics used in production.

Let's begin.

The top reasons teams overspend:

• Idle EC2, EKS, and RDS instances
• Over-provisioned environments
• Orphaned storage volumes & snapshots
• Unoptimized S3 usage
• Underutilized Load Balancers
• Legacy AMIs + data retention
• No auto-scaling
• Lack of cost visibility
• No tagging strategy
• Engineers overestimating capacity
• Forgotten dev/test environments
• No governance/policies

The good news? You can eliminate 40–60% of your AWS bill in less than 30 days.

Below are exactly the 15 tactics we used to achieve massive savings.

90% of EC2 instances are over-provisioned.

Use:

• AWS Compute Optimizer
• CloudWatch metrics
• CPU + Memory trending
• Auto-scaling policies

Downsize:

• m5 → m6g
• t3 → t4g
• c5 → c7g

Switching to Graviton2/3 alone saved us:

• 20–40% CPU cost
• 15–25% memory improvement

Orphaned storage = silent budget killer.

Steps:

1. Identify unused volumes:

aws ec2 describe-volumes --filters Name=status,Values=available

1. Delete or migrate snapshots older than 90 days
2. Move long-term backups to S3 Glacier
3. Apply automated lifecycle policies

Move unused objects automatically to:

• S3 Standard-IA
• S3 One Zone-IA
• S3 Glacier
• Glacier Deep Archive

Policy example:

{
  "Rules": [
    {
      "ID": "MoveOldData",
      "Filter": {},
      "Status": "Enabled",
      "Transitions": [
        { "Days": 30, "StorageClass": "STANDARD_IA" },
        { "Days": 90, "StorageClass": "GLACIER" }
      ]
    }
  ]
}

Aurora Serverless v2 scales "per second" and reduces costs:

• 50% cheaper for dev & staging
• 30% cheaper for inconsistent workloads
• Perfect for microservices

We migrated from: ❌ RDS MySQL → ✔ Aurora Serverless v2

Savings Plans are more flexible than Reserved Instances.

Types:

✓ Compute Savings Plans Covers EC2, Lambda, Fargate across all instance families.

✓ EC2 Instance Savings Plans Cheaper but less flexible.

✓ Machine Learning Savings Plans (2025 update) For high GPU usage.

Average savings: 30–60%

Ideal for:

• Batch processing
• CI/CD runners
• Stateless apps
• Kubernetes worker nodes
• Data ingestion

EKS + Spot = enormous savings.

Use tools:

• Karpenter
• Cluster Autoscaler
• Spot.io

Graviton3/3E now supports:

• EC2
• RDS
• ECS
• EKS
• Lambda (2025 update: ARM-native runtimes)
• Kafka MSK
• ElastiCache

Graviton = up to 40% cost reduction with better performance.

Use automation to stop:

• EC2
• RDS
• EKS node groups
• NAT Gateways

Schedule examples:

• Stop at 7 PM
• Start at 9 AM
• Off during weekends

Tooling:

• EventBridge + Lambda
• Instance Scheduler
• Terraform scripts

Most companies waste 40% on idle non-prod environments.

NAT Gateways are extremely expensive.

Ways to cut costs:

• Use NAT instance instead
• Route heavy outbound traffic through VPC endpoints
• Consolidate NAT gateways
• Use regional endpoints for S3/DynamoDB

Settings:

• Enable compression
• Use cache policies
• Add origin shield for heavy traffic
• Move static assets to S3
• Use Lambda@Edge to rewrite caching logic

Use:

aws elbv2 describe-load-balancers

Delete:

• Unused ALBs
• Old Classic ELBs
• NLBs from dead services

Also remove:

• Idle target groups
• Unused listeners

Use:

• Karpenter
• Vertical Pod Autoscaler
• Cluster Autoscaler
• Node expiration policies

Reduce:

• Over-provisioned node pools
• On-demand nodes
• Old GPU nodes

AWS Compute Optimizer scans:

• Lambda
• EC2
• EBS
• Auto-scaling
• ECS services

And recommends:

• Right-sizing
• Graviton migration
• Deallocation

We implemented 60% of recommendations.

Tags:

• Environment=Prod/Dev
• Owner=Team
• Project=AppName
• CostCenter=XYZ
• Criticality=High

Without mandatory tagging:

• You cannot identify waste
• You cannot enforce governance

Use AWS Tag Policies.

Tools:

• AWS Cost Explorer
• Kubecost (for EKS)
• CloudZero
• FinOut
• Datadog Cloud Cost Mgmt
• CloudHealth

Real-time alerts for:

• Spikes
• Overuse
• Un-tagged resources
• Underutilized services

This prevents large & unexpected overages.

• Automate everything
• Standardize tags
• Use AI-driven anomaly detection
• Schedule idle shutdowns
• Use FinOps dashboards
• Enforce policies with OPA/Kyverno
• Build cost into sprint process
• Train developers (cost = architecture)

Cloud cost is now a DevOps responsibility, not just finance.

AWS costs can be reduced dramatically — if you follow a consistent, automated, and engineering-first FinOps strategy.

Using the 15 tactics above, we saved:

➡ $50,000 per month ➡ $600,000 per year ➡ Without reducing performance ➡ Without removing essential services

You can apply these same steps to your AWS environment today.

Start with the quick wins: delete orphaned resources, right-size EC2 instances, and implement auto-shutdown schedules. Then move to deeper optimizations like Savings Plans, Graviton migrations, and continuous FinOps monitoring.

Remember: every dollar saved on infrastructure is a dollar you can invest in innovation, talent, or better customer experiences.

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*Want to optimize your AWS costs? Let's connect.*