💰 Cost Optimization with AWS Compute Optimizer.

“You can’t optimize what you don’t measure.” — This quote holds especially true in the cloud.
If you're running workloads on AWS, cost optimization should never be a one-time effort. AWS offers a wide array of services to help manage your spending, and one of the most powerful — yet underutilized — tools is AWS Compute Optimizer.

In this blog, I’ll walk you through what Compute Optimizer is, how it works, and how I used it in a real-world project to save money without compromising performance.

🔍 What is AWS Compute Optimizer?
AWS Compute Optimizer is a service that uses machine learning to analyze your workload configurations and utilization metrics. Based on this data, it provides right-sizing recommendations for:

EC2 instances
EBS volumes
Lambda functions
Auto Scaling groups
Instead of over-provisioning by default or relying solely on manual metrics, you can use Compute Optimizer to guide your sizing decisions with data-driven insights.

⚙️ How Compute Optimizer Works
Data Collection:
Collects up to 14 days of historical metrics from CloudWatch.
Monitors CPU, memory, disk I/O, and network activity.
Analysis & Modeling:
Uses machine learning models trained on millions of AWS workloads.
Evaluates whether a resource is under-provisioned, over-provisioned, or optimized.
Recommendation:
Suggests better instance types or volume types.
Projects expected performance and cost for each recommendation.
Categorizes into:
Not optimized
Optimized
Insufficient data
🛠️ Enabling Compute Optimizer
To start using Compute Optimizer:

Go to the AWS Console.
Enable it in your target regions.
Optionally enable Enhanced Infrastructure Metrics for deeper insights (e.g., memory data for EC2).
You’ll begin to see recommendations within 12–24 hours.

🧪 Real-World Use Case: Cost Cutting with EC2
Let me walk you through a scenario from one of my Java backend applications.

We had 6 EC2 instances of type t3.large in a production environment. These handled various microservices with relatively low average usage but had burst workloads occasionally.

Compute Optimizer Analysis:

CPU utilization: <10% average
Network I/O: <5 MB/s
Memory (after enabling enhanced metrics): ~35% used
Recommendations:

Switch to t3.medium
Enable T3 Unlimited for burst scenarios
📉 What We Did
We reviewed the recommendations, validated CloudWatch metrics, and decided to switch 3 of the 6 instances to t3.medium.

We used this migration strategy:

Create AMI backups
Update Auto Scaling Group launch templates
Monitor via CloudWatch and logs
Use SSM for post-launch validation
💡 The Result
After two weeks of monitoring:

No increase in latency
No service degradation
Saved $50/month per instance
Projected annual savings: $1800+
We later migrated the remaining 3 nodes, leading to a total monthly cost reduction of ~$300.

🛑 Things to Keep in Mind
Before applying Compute Optimizer recommendations blindly, consider:

Is this instance intentionally over-provisioned? (e.g., for redundancy or DR)
Is it serving memory-heavy or burst workloads?
Is it running a containerized workload with memory/cpu reservation?
What’s your availability SLA?
Recommendations are starting points — always validate against application performance and business requirements.

📦 What About EBS and Lambda?
EBS

We had a general-purpose gp2 volume attached to our dev environment with almost no IO requirements.

Compute Optimizer suggested sc1 (cold HDD), and we tested it for archive-type workloads.

✅ Result: No functional issues, 60% storage cost saved.

Lambda

In a different project, our Lambda functions had 1024MB memory allocated by default. Compute Optimizer showed average usage around 200MB.

We lowered to 512MB, which also reduced the timeout rate and improved cold-start behavior.

📊 How to Track the Savings
Once you apply changes:

Use AWS Cost Explorer to monitor actual savings
Use AWS Budgets to track against thresholds
Tag optimized resources with something like optimization: compute for cost analysis
📎 Tips to Integrate into Your Workflow
Include Compute Optimizer reviews as part of your monthly cloud hygiene check
Automate snapshotting EC2 & EBS before resizing with scripts
Store recommendations as JSON and review diffs over time
Build a custom dashboard using the Compute Optimizer API
📈 Going Beyond: Combine With Trusted Advisor & Cost Explorer
While Compute Optimizer is focused on resource rightsizing, pairing it with:

AWS Trusted Advisor → checks for idle load balancers, unused IPs, etc.
Cost Explorer → visualizes usage trends and service spikes
… gives you a complete picture of cost optimization.

🔚 Final Thoughts
AWS Compute Optimizer is one of the easiest ways to cut cloud bills without rewriting your infrastructure.

🎯 It's free, data-backed, and supports multiple AWS services.

In my experience, implementing even 10–15% of the recommendations led to significant savings — without a single service interruption.