AWS bills can spiral out of control fast. What starts as $5k per month at launch becomes $50k at scale, often with no corresponding increase in traffic or features. At Futureaiit, we have helped dozens of companies slash their AWS costs by 30% to 50% without sacrificing performance or reliability. Here is exactly how we did it, with specific tactics you can implement today.
The AWS Cost Problem
AWS pricing is designed to be easy to start with and expensive to scale with. The default configurations are convenient but wasteful. Reserved Instances require upfront commitment. Spot Instances require architectural changes. Most companies stick with on demand pricing and watch their bills grow.
We worked with a Series B SaaS company spending $80k per month on AWS. After a two week optimization project, we reduced their bill to $45k per month—a 44% reduction. The application ran faster, not slower. Here is how we did it.
1. Right Size Your EC2 Instances
The easiest wins come from right sizing. Most companies over provision instances "just to be safe," wasting money on unused capacity.
Analyze Actual Usage
Use CloudWatch metrics to analyze CPU, memory, and network utilization over 30 days. If your instances consistently run at 20% CPU, you are over provisioned.
Tools we use at Futureaiit:
- AWS Compute Optimizer: Free tool that recommends instance type changes based on actual usage
- CloudWatch dashboards: Visualize utilization trends over time
- Third party tools: CloudHealth, Spot.io, or Vantage for deeper analysis
Downsize Strategically
Start with non production environments. Development and staging instances can often run on t3.small instead of m5.large, saving 70% per instance.
For production, downsize during low traffic periods and monitor closely. Use auto scaling to handle traffic spikes, not permanently over provisioned instances.
Real world example: We helped a client migrate from m5.2xlarge instances (8 vCPU, $0.384/hour) to m5.xlarge (4 vCPU, $0.192/hour) after discovering their average CPU usage was 15%. This alone saved $14k per month.
2. Commit to Reserved Instances and Savings Plans
On demand pricing is convenient but expensive. Reserved Instances (RIs) and Savings Plans offer 30% to 70% discounts in exchange for commitment.
Reserved Instances
Commit to specific instance types in specific regions for 1 or 3 years. Best for predictable, steady state workloads.
Strategy:
- Analyze 6 months of usage to identify baseline capacity
- Purchase RIs to cover 70% to 80% of baseline (not 100%, to allow flexibility)
- Start with 1 year commitments, not 3 years, until you are confident in your architecture
Compute Savings Plans
More flexible than RIs. Commit to a dollar amount per hour (e.g., $10/hour) and get discounts on any EC2, Fargate, or Lambda usage.
This is better for dynamic workloads where instance types change frequently.
Impact: A client spending $30k/month on on demand EC2 switched to Savings Plans and immediately saved $10k/month (33% reduction) with zero architectural changes.
3. Use Spot Instances for Fault Tolerant Workloads
Spot Instances are spare AWS capacity sold at up to 90% discount. The catch: AWS can terminate them with 2 minutes notice.
Where Spot Works
- Batch processing: Data pipelines, ETL jobs, video transcoding
- CI/CD runners: Build and test environments
- Stateless web servers: Behind a load balancer with auto scaling
- Big data: Spark, Hadoop, EMR clusters
Where Spot Does Not Work
- Databases (unless using read replicas)
- Stateful applications without checkpointing
- Real time processing with strict SLAs
Implementation Strategy
Use Spot Instances for 50% to 70% of your auto scaling group, with on demand instances as a baseline. This gives you cost savings with reliability.
Tools like Spot.io or AWS Auto Scaling can automatically mix Spot and on demand instances, handling interruptions gracefully.
Real world example: We migrated a client's data processing pipeline to Spot Instances, reducing compute costs from $25k/month to $6k/month (76% reduction). The pipeline runs on a mix of Spot and on demand, with automatic failover.
4. Optimize S3 Storage Costs
S3 seems cheap until you store terabytes of data. Intelligent tiering and lifecycle policies can cut costs dramatically.
S3 Storage Classes
- S3 Standard: $0.023/GB for frequently accessed data
- S3 Intelligent-Tiering: Automatically moves data between tiers based on access patterns
- S3 Glacier: $0.004/GB for archival data (retrieval takes hours)
- S3 Glacier Deep Archive: $0.00099/GB for long term archives (retrieval takes 12 hours)
Lifecycle Policies
Automatically transition objects to cheaper storage classes after a certain age:
- Move logs older than 30 days to Glacier
- Delete temporary files after 7 days
- Transition infrequently accessed data to Intelligent-Tiering
Impact: A client storing 500TB of logs in S3 Standard ($11.5k/month) moved 90% to Glacier ($1.8k/month), saving $9.7k/month.
5. Eliminate Idle Resources
Zombie resources—instances, databases, load balancers running but unused—are a massive source of waste.
Common Culprits
- Orphaned EBS volumes: Volumes detached from terminated instances but still incurring charges
- Unused Elastic IPs: $0.005/hour when not attached to a running instance
- Idle RDS instances: Development databases running 24/7 when only used 8 hours/day
- Forgotten load balancers: ALBs and NLBs with no targets
Automated Cleanup
Use AWS Config rules or Lambda functions to identify and alert on idle resources. Tools like Cloud Custodian can automatically stop or delete them.
Real world example: We found $8k/month in orphaned EBS volumes and unused Elastic IPs at a client. Simple cleanup saved nearly $100k/year.
6. Optimize Data Transfer Costs
Data transfer out of AWS is expensive: $0.09/GB after the first 100GB. This adds up fast for high traffic applications.
Strategies to Reduce Transfer Costs
- Use CloudFront CDN: Cache static assets at edge locations, reducing origin requests
- Enable compression: Gzip or Brotli compression reduces transfer size by 70%+
- Optimize images: Use WebP format and responsive images to reduce payload size
- Keep traffic within AWS: Use VPC endpoints and PrivateLink to avoid internet transfer charges
Impact: A media company serving 10TB/month in video ($900/month in transfer costs) implemented CloudFront caching and reduced transfer to 2TB/month ($180/month), saving $720/month.
7. Database Optimization
RDS and DynamoDB can be surprisingly expensive if not optimized.
RDS Optimization
- Right size instances: Use Performance Insights to analyze actual load
- Use Aurora Serverless: For variable workloads, pay only for actual usage
- Enable automated backups retention: Delete old snapshots you do not need
- Use read replicas strategically: Only where needed for performance, not as a default
DynamoDB Optimization
- Switch to on demand pricing: For unpredictable workloads
- Use provisioned capacity with auto scaling: For predictable workloads
- Enable DynamoDB Accelerator (DAX): Reduce read costs by caching
How Futureaiit Can Help
At Futureaiit, we provide comprehensive AWS cost optimization services. We can help you:
- Conduct cost audits: Identify waste and optimization opportunities
- Implement Reserved Instances and Savings Plans: Maximize discounts without over committing
- Migrate to Spot Instances: Architect for fault tolerance and cost savings
- Optimize storage and data transfer: Implement lifecycle policies and CDN caching
- Automate cost management: Set up alerts, budgets, and automated cleanup
- Negotiate with AWS: For large customers, we help negotiate Enterprise Discount Programs
Our typical engagement reduces AWS costs by 30% to 50% within 30 days, with ongoing savings compounding over time.
Conclusion
AWS cost optimization is not a one time project. It is an ongoing discipline. The tactics we have outlined—right sizing, Reserved Instances, Spot Instances, storage optimization, idle resource cleanup, and data transfer reduction—are proven to deliver significant savings.
At Futureaiit, we have saved clients millions in AWS costs. The key is systematic analysis, strategic implementation, and continuous monitoring. Start with the low hanging fruit (idle resources, right sizing), then move to architectural changes (Spot Instances, storage tiering).
Ready to reduce your AWS bill? Contact Futureaiit to schedule a free AWS cost audit and discover your optimization opportunities.
Futureaiit
AI & Technology Experts