I Built an AI-Powered AWS Waste Detector (and Found $4,200 in My Own Account)

Source: Dev.to

The $4,200 Mistake

I once left a staging environment running on AWS for 6 months.

• Cost: $4,200
• Traffic: 0
• Users: 0
• Value: 0

Just… existing, racking up charges. When I finally discovered it, I felt like I’d been paying rent for an apartment I forgot I had. 🤡

Why AWS Cost Explorer Isn’t Enough

AWS Cost Explorer shows you:

• What you’re spending
• Where you’re spending it
• How spending trends over time

But it doesn’t tell you:

• Which resources are doing nothing
• Which resources you forgot about
• Which resources are over‑provisioned
• Where you’re wasting money

AI‑Powered Waste Detection for CloudWise

I shipped an AI‑powered scanner that examines your AWS account and finds common waste patterns, such as:

• RDS databases with zero connections
• EBS volumes attached to nothing
• Elastic IPs not attached to instances
• Load balancers serving zero traffic
• EC2 instances with < 5 % CPU usage
• NAT Gateways with zero traffic
• ELBs with zero requests
• t3.2xlarge instances running only cron jobs
• RDS instances with 90 %+ free storage
• EC2 instances with excessive memory
• Lambda functions with over‑allocated memory
• Cross‑AZ data transfer fees
• S3 buckets in expensive storage classes
• Old EBS snapshots
• Unused Elastic IPs

How It Works

1. Connect your AWS account (read‑only IAM role; takes ~2 minutes)
2. AI scans your resources (analyzes usage patterns, costs, configurations)
3. Get step‑by‑step recommendations (exact actions to fix each issue)

Example: Orphaned RDS Database Detected

• Resource: prod-staging-db
• Cost: $247.32 /month
• Last Connection: 187 days ago
• Status: Running but unused

Recommendation

1. Take final snapshot
2. Delete database

Estimated savings: $247.32 /month ($2,967.84 /year)
Risk Level: Low (no connections in 6 months)

Stack

• Frontend: React + TypeScript + TailwindCSS
• Backend: Python + FastAPI
• Database: PostgreSQL
• AI: OpenAI GPT‑4 for recommendation generation
• AWS Integration: Boto3 + Cost Explorer API

Key Challenges

• IAM permissions: Balancing security with functionality
• Cost attribution: Mapping resources to actual costs
• Usage pattern detection: Distinguishing idle from low‑usage
• Recommendation quality: Avoiding false positives

Waste Breakdown (Typical)

• 70 % orphaned or idle resources
• 20 % over‑provisioning
• 10 % configuration issues (cross‑AZ, storage classes)

My Own AWS Account Findings

• Total waste: $4,247 /month
• Orphaned resources: $1,847 /month (RDS, EBS, ELB)
• Idle resources: $1,200 /month (EC2, RDS)
• Over‑provisioning: $800 /month (EC2, Lambda)
• Cross‑AZ transfer: $400 /month

Try CloudWise

https://cloudcostwise.io – free to use, no credit card required. Takes about 2 minutes to connect your AWS account.

I’d love your feedback, especially from DevOps engineers and platform teams!

Coming Soon

• Per‑namespace cost tracking for Kubernetes/EKS
• Automated remediation (with approval)
• Slack/Teams alerts for cost anomalies
• Multi‑account support for organizations

Share Your Stories

Have you ever found unexpected AWS waste? Share your horror stories in the comments! 👇

P.S. That $4,200 staging environment? I could’ve bought a really nice espresso machine instead. Still thinking about it. ☕