Objective
The primary objective of this case study is to explore how an organization successfully optimized its AWS (Amazon Web Services) costs. The organization wanted to reduce their cloud expenses without sacrificing performance, reliability, or scalability. Key goals included:
- Reducing monthly cloud bills by 30%.
- Optimizing resource utilization.
- Implementing sustainable and automated cost management practices.
30
%
reduction in monthly AWS Cost
2
X
deeper insights of cost for services
20
%
compression of storage to reduce costs
25
%
Data Transfer Cost reduced by using CloudFront
The Challenges
- Unmonitored and Underutilized Resources:Â The organization had several idle resources running in their AWS environment. These included instances that were over-provisioned, EBS (Elastic Block Storage) volumes no longer in use, and other services running without being utilized.
- Lack of Visibility into Cost Drivers:Â AWS bills were complex and provided limited transparency on which resources were contributing the most to expenses. Understanding which services, environments, or teams were generating the most cost was a significant challenge.
- Data Transfer Costs:Â The company had several EC2 instances in multiple AWS regions and Availability Zones. Cross-region data transfer fees were higher than anticipated, which led to unexpected increases in monthly billing.
- Over-Provisioning of EC2 Instances:Â Many EC2 instances were provisioned at larger sizes than required for the workloads they were handling. This resulted in higher-than-necessary computing costs.
- Inefficient Storage Management:Â EBS volumes and S3 storage were not being properly managed. Several snapshots and outdated data were stored, contributing to high storage costs.
- On-Demand Pricing Overuse:Â The company relied heavily on On-Demand EC2 instances, leading to higher-than-necessary compute costs compared to using Reserved Instances or Spot Instances.
What did Volyo
Team do
- Automated Rightsizing of EC2 Instances: AWS’s Trusted Advisor and Cost Explorer were used to identify EC2 instances that were over-provisioned. The company then performed rightsizing exercises to match instance types and sizes with actual workloads, switching to smaller instances where appropriate.
- Reserved Instances (RIs) and Savings Plans:Â By analyzing long-term workload patterns, the organization purchased Reserved Instances for EC2 and applied Savings Plans for steady-state workloads. This reduced costs significantly for predictable workloads while maintaining flexibility for dynamic scaling.
- Auto Scaling and Spot Instances: The company implemented Auto Scaling to dynamically adjust compute resources based on real-time demand. They also leveraged Spot Instances for non-critical workloads to take advantage of AWS’s discounted compute pricing for spare capacity.
- Storage Optimization:Â The organization performed a detailed audit of EBS volumes and S3 storage. They deleted unused snapshots, compressed large datasets, and applied S3 lifecycle policies to automatically archive or delete old data. They also used Amazon S3 Intelligent-Tiering to automatically move data to cheaper storage tiers when access frequency dropped.
- Using AWS Cost Explorer and Budgets:Â To gain visibility into cost drivers, the team used AWS Cost Explorer to track spending trends and allocated costs by services, departments, and projects. AWS Budgets were set up to alert the team when costs exceeded predefined thresholds, allowing them to take immediate action.
- Cross-Region Data Transfer Optimization:Â Data transfer patterns were analyzed, and workloads were consolidated within the same regions or Availability Zones to reduce cross-region data transfer fees. For workloads that required multi-region setups, Amazon CloudFront was used to cache and distribute content, reducing data transfer costs between regions.
- Shutting Down Idle Resources:Â Tools like AWS Lambda and AWS CloudWatch were used to automate the shutdown of non-production resources outside of business hours. Development and staging environments were turned off when not in use, leading to significant savings.Â
The Results
- 30% Reduction in AWS Cloud Costs
- Improved Cost Visibility
- Efficient Resource Management
- Sustainable Cost Management
- Reduced Data Transfer Costs
- Efficient Storage Use
The AWS services that we use to reduce costs
AWS Cost Explorer
Auto Scaling
Amazon CloudFront
Amazon CloudWatch
AWS Lambda
Amazon S3 Intelligent-Tiering
