A B2B SaaS company headquartered in Irvine came to us with a familiar problem: their AWS bill had crept from $15,000 to $47,000 per month over 18 months, and nobody could explain why. Revenue had grown 2x. Infrastructure costs had grown 3x. The math didn't add up.
They'd tried the usual approaches — reserved instances, some right-sizing, turning off dev environments at night. It barely moved the needle. The real waste was hiding in places that required deep engineering analysis to find.
Here's how we used AI-assisted analysis and our overnight engineering pod to cut their bill by 40% in six weeks.
Week 1: The AI-Powered Audit
We started by feeding their AWS Cost Explorer data, CloudWatch metrics, and infrastructure-as-code configs into an analysis pipeline. Using Claude, we generated a comprehensive resource utilization map that would have taken a human engineer days to compile.
The AI identified three major categories of waste that accounted for 85% of the excess spend.
Oversized RDS instances. Their production database ran on a db.r6g.4xlarge ($2,800/month) but averaged 12% CPU utilization. The AI correlated CloudWatch metrics with query logs and determined that a db.r6g.xlarge ($700/month) could handle their workload with headroom.
Orphaned resources. 23 EBS volumes from terminated instances, 8 unused Elastic IPs, 4 load balancers pointing to empty target groups, and an entire staging environment that hadn't been accessed in five months. Total: $3,200/month for resources doing absolutely nothing.
Unoptimized data transfer. Their application made cross-AZ API calls for every request, doubling their data transfer costs. A simple architecture change — colocating services in the same AZ — would save $4,500/month.
Weeks 2–4: Overnight Engineering
This is where our Vietnam pod earned its keep. Every optimization was implemented, tested, and validated during overnight hours — zero disruption to the US engineering team's sprint work.
Our engineers worked through the changes methodically. Database migration to smaller instances happened over a weekend, with our overnight team monitoring performance metrics in real time and ready to roll back if latency spiked. It didn't.
The orphaned resource cleanup was straightforward but tedious — exactly the kind of work that gets deprioritized when US engineers have feature work to deliver. Our team cleared it in two nights.
The cross-AZ optimization required careful service reorganization. Our engineers mapped every service dependency, tested the new topology in staging, and executed the migration in three phases across three nights.
Weeks 5–6: Automated Guardrails
Cutting costs once is easy. Keeping them down is the hard part. We built automated guardrails using AI-assisted monitoring:
A daily cost anomaly detector that alerts when any service's spend deviates more than 15% from its 30-day average. The AI baseline adjusts automatically for known patterns like month-end processing spikes.
An automated right-sizing recommender that runs weekly, analyzes instance utilization, and creates pre-approved change requests for the US team to review each Monday morning.
A resource lifecycle policy that automatically tags unused resources after 14 days and terminates them after 30 days — with Slack notifications at each stage.
The Results
Monthly AWS spend dropped from $47,000 to $28,000 — a 40% reduction. Annualized savings: $228,000. Our engagement cost was a fraction of that.
More importantly, the automated guardrails have kept costs stable for four months. The client's infrastructure now scales linearly with their business instead of exponentially.
Cloud waste isn't a technology problem — it's an attention problem. Your US team is busy building features. Our overnight team makes sure the infrastructure stays efficient while they sleep.