Cloud Cost Optimization for a Leading Mortgage Provider
Our client, a major U.S.-based mortgage lender, faced growing AWS bills despite stable usage. Their dev environment was overprovisioned with static infrastructure and lacked autoscaling, leading to inflated RDS, EC2, and EBS costs. We partnered to right-size their architecture, implement intelligent scheduling, and deploy autoscaling — driving significant cost reductions without compromising performance.
Success Highlights
- 50% reduction in RDS service costs
- 29% savings via Reserved Instances
- 80% idle time reduction achieved through dynamic ECS autoscaling
Key Details
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Industry: Fintech / Mortgage Lending
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Geographies: US
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Platform: Multi-Account AWS Cloud Platform
Business Challenge
Our client needed to reduce AWS infrastructure costs while improving elasticity and long-term operational efficiency.
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Static Infrastructure and Rising Cloud Costs: Resources like EC2, RDS, and EBS were running 24/7, resulting in major underutilization and waste.
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Lack of Autoscaling in Key Services: The Loan Data API lacked autoscaling, leading to performance issues and inefficient resource use during peak/off-peak hours.
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Limited Visibility and Governance: No tagging policies, ownership mapping, or resource usage tracking were in place, hampering DevOps governance.
Our Solution Approach
We designed a multi-phase AWS cost optimization strategy, combining resource right-sizing, intelligent automation, and governance enhancements.
Assess Cloud Usage and Performance Metrics
We reviewed billing reports, CloudWatch metrics, and configurations to identify underutilized services and performance bottlenecks.
Scalable, Cost-Optimized AWS Environment
Designed a right-sized RDS instances, migrated to T3 burstable types, and introduced Reserved Instances for steady workloads
Deploy Autoscaling and Scheduling Logic
We implemented autoscaling for ECS, built CPU/memory-based thresholds, and automated shutdowns of idle resources outside business hours.
Establish Guardrails, Tagging, and Documentation
Implemented tagging policies, alerting mechanisms, backup routines, and communication protocols to improve transparency and control.
Technical Highlights
- RDS downsizing from db.m5.xlarge to T3 instances
- ECS autoscaling enabled via CloudWatch thresholds
- Intelligent shutdown scheduling using Lambda and AWS EventBridge
- Reserved Instances strategy for predictable workloads
- Resource tagging for cost ownership and accountability
function optimizeAWSCosts(account):
infraData = collectCloudWatchMetrics(account)
usageReport = fetchBillingReport(account)
for service in account.services:
if isUnderutilized(service):
rightSize(service)
if isIdleOutsideBusinessHours(service):
scheduleShutdown(service)
if isPredictableWorkload(service):
applyReservedInstance(service)
tagResources(account)
setAlertingGuardrails(account)
documentChanges(account)
return "Optimization Complete"Business Outcomes
Our solutions delivered tangible improvements in cloud efficiency, cost savings, and operational scalability.
RDS cost reduction through right-sizing and burstable instances
projected AWS savings via Reserved Instance adoption
idle time reduction achieved through dynamic ECS autoscaling
- Governance Improved
- Boost Long Term-Savings