Cloud Infrastructure Migration: Cutting AWS Costs by 52% While Improving
Performance

The Challenge

A Series B fintech startup processing $240 million in annual transaction volume was watching their AWS bill spiral out of control. Monthly infrastructure costs had grown from $4,200 to $12,000 over 18 months, and the CTO's projections showed it would hit $22,000 per month within a year if growth continued at the same rate. The alarming part: their actual user growth had been 40% over that period, but their AWS costs had grown 185%.

The infrastructure had been built by the founding engineering team, who had prioritized speed of development over cost efficiency. The result was a patchwork of services and configurations that worked but was wildly inefficient. They were running 47 EC2 instances — most of them over-provisioned — plus 14 RDS databases, 8 Elasticache clusters, and a sprawling network of Lambda functions, SQS queues, SNS topics, and Kinesis streams that had been added for specific features and never cleaned up.

The CTO described the culture around infrastructure: "Nobody looks at the AWS bill because nobody wants to defend their usage. Every team just provisions whatever they need, and the previous month's spend becomes the baseline for next month's budget. We need someone to come in, audit everything, and fix it — because we don't have the bandwidth to do it ourselves."

Our Approach

We began with a 360-degree infrastructure audit using AWS Cost Explorer, Trusted Advisor, Compute Optimizer, and custom Python scripts that mapped every resource to its team, application, and business function. The audit revealed that 62% of their monthly AWS spend was wasted on over-provisioned, underutilized, or completely abandoned resources.

The biggest findings included:

• 24 of 47 EC2 instances had average CPU utilization below 5%
• 6 RDS instances were running at 8% or less of their capacity
• 3 Elasticache clusters serving data that was also cached in the application layer
• $1,800 per month in NAT Gateway data processing fees for traffic that could route through VPC endpoints
• 14 TB of EBS snapshots — many of them orphaned from terminated instances months earlier
• $1,200 per month in data transfer costs between AZs due to poorly architected microservice deployments
• 4 Lambda functions that hadn't been invoked in over three months

The Solution

We developed a three-phase optimization plan: right-size, consolidate, and automate.

Phase 1 – Right-sizing and resource rationalization (Weeks 1–3): We resized or downsized 34 EC2 instances, migrated 12 services to Graviton-based instances (ARM architecture) for 25% cost savings per compute unit, and consolidated 14 RDS instances into 6 using read replicas and proper sharding. We implemented auto-scaling groups with proper min/max thresholds for all production services, replacing the static instance counts that had been consuming resources during off-peak hours.

Phase 2 – Architecture optimization (Weeks 3–6): We migrated inter-service communication from public-facing endpoints to VPC-private interfaces using AWS PrivateLink, eliminating $1,800/month in NAT Gateway charges. We redesigned the data pipeline to use S3 + Athena for infrequently accessed analytics data instead of keeping it in RDS, saving $2,400/month in database costs. We consolidated 8 ElastiCache clusters into 3, configured with proper eviction policies and instance types.

Phase 3 – Automation and governance (Weeks 6–8): We implemented AWS Budgets and Cost Anomaly Detection with automated Slack alerts for any cost spikes over 10%. We wrote Terraform modules with cost-tagging requirements so that every new resource was automatically tagged with its owner, project, and cost center. We implemented scheduled start/stop scripts for non-production environments, saving $1,600/month by shutting down staging, QA, and development environments during nights and weekends.

Results

The full optimization was implemented over eight weeks:

• Monthly AWS bill reduced by 52%, from $12,000 to $5,780
• Annualized savings of $74,640
• Application response times improved by 40% due to right-sized instances and reduced network latency
• P99 latency dropped from 1,200ms to 320ms for critical API endpoints
• Environment startup/shutdown automation saved 312 hours of engineering time per year in manual provisioning
• Cost governance implemented: zero new resources can be provisioned without cost-tagging and budget approval

The fintech startup's engineering team reported that the infrastructure was actually easier to manage after the optimization — fewer instances to monitor, cleaner networking, and automated scaling that handled traffic spikes without human intervention.

"SoniNow did what we couldn't — they took a hard look at every dollar we were spending on AWS and found over $6,000 in monthly waste. The 40% performance improvement was a bonus we didn't expect. They didn't just cut costs; they made our infrastructure better." — CTO, Fintech Startup

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Is your cloud bill growing faster than your business? SoniNow's cloud infrastructure specialists can audit your AWS spend, identify waste, and implement optimizations that pay for themselves. Contact us for a free cloud cost assessment.