How We Slashed Our AWS Bill by 45% Using These 3 Core Service Tweaks

Running applications in the cloud with Amazon Web Services (AWS) offers incredible flexibility and scalability. However, it’s also easy to see your monthly bill climb higher than expected. We recently faced this challenge and managed to cut our AWS costs by a significant 45% by strategically tweaking our use of just three core AWS services. We’re excited to share these practical changes that you can implement too!

We focused on making smart adjustments to services that often contribute the most to AWS bills: Amazon EC2, Amazon S3, and Amazon RDS. Here’s how we did it:

1. Right-Sizing Our Amazon EC2 Instances

The Issue: We realized that many of our Amazon EC2 (virtual server) instances were over-provisioned. This means they had more computing power (CPU, memory) than they actually needed. We were paying for resources we weren’t fully utilizing.

Our Tweak: We implemented a rigorous monitoring process using AWS CloudWatch to track the CPU and memory utilization of our EC2 instances over time. We then used AWS Compute Optimizer, a free service, which analyzes your resource utilization and provides recommendations for right-sizing your instances.

The Result: By switching to smaller, more appropriately sized instances for several of our workloads, we significantly reduced our EC2 costs. For example, an instance that was consistently using only 10% of its CPU and memory was downsized to a smaller instance type that better matched its actual needs.

How to Apply This:

Enable detailed monitoring in CloudWatch for your EC2 instances.
Regularly review the recommendations provided by AWS Compute Optimizer.
Don’t be afraid to downsize! You can always scale up again if needed.
Consider using Auto Scaling groups with appropriate scaling policies to automatically adjust the number and size of your instances based on demand.

2. Optimizing Our Amazon S3 Storage

The Issue: Our Amazon S3 (object storage) usage was growing rapidly, and we weren’t always storing data in the most cost-effective way. We had a lot of data that was rarely accessed but was still being stored in the standard S3 storage class, which has higher costs for storage.

Our Tweak: We implemented S3 Lifecycle policies to automatically move data to lower-cost storage classes based on how frequently it was accessed.

S3 Standard-Infrequent Access (S3 Standard-IA): For data accessed less frequently but requiring rapid access when needed.
S3 One Zone-Infrequent Access (S3 One Zone-IA): For data accessed less frequently and can tolerate data loss in a single Availability Zone.
Amazon S3 Glacier Flexible Retrieval and Amazon S3 Glacier Deep Archive: For long-term archival with different retrieval time and cost options.

The Result: By moving older, less frequently accessed backups and logs to S3 Standard-IA and Glacier, we significantly reduced our storage costs.

How to Apply This:

Analyze your S3 data access patterns. Identify data that is rarely accessed.
Create S3 Lifecycle rules to transition data between storage classes automatically based on age.
Consider using S3 Intelligent-Tiering which automatically moves your data to the most cost-effective access tier based on changing access patterns.
Regularly review your lifecycle policies to ensure they are still aligned with your needs.

3. Leveraging Amazon RDS Reserved Instances

The Issue: We were running several persistent Amazon RDS (managed relational database service) instances for our production and development environments. These instances were running 24/7, and we were paying the on-demand rates.

Our Tweak: We analyzed our RDS usage and identified instances that were running consistently for the long term. For these instances, we purchased Amazon RDS Reserved Instances (RIs). RIs offer a significant discount (up to 75% compared to on-demand pricing) in exchange for a one-time upfront payment and a commitment to use the instance for a 1-year or 3-year term.

The Result: By purchasing RIs for our stable RDS workloads, we achieved substantial savings on our database costs.

How to Apply This:

Identify RDS instances that run consistently.
Consider the different RI payment options: All Upfront, Partial Upfront, and No Upfront, based on your budget and commitment level.
Evaluate 1-year vs. 3-year terms. Longer terms generally offer greater discounts.
Keep track of your RI utilization to ensure you are maximizing your savings.

Conclusion: Small Tweaks, Big Savings

By focusing on right-sizing our EC2 instances, optimizing our S3 storage with lifecycle policies, and leveraging RDS Reserved Instances, we were able to achieve a remarkable 45% reduction in our AWS bill. These weren’t complex architectural changes, but rather strategic adjustments to how we were using core AWS services.

We encourage you to review your own AWS usage and see where these simple tweaks can be applied. Regularly monitoring your resource utilization and taking advantage of AWS cost optimization tools can lead to significant savings and help you make the most of your cloud investment. Start small, analyze your data, and watch your AWS bill shrink!