From Overspend to Optimized: Transforming Infrastructure Costs
During our quarterly budget review, leadership highlighted a critical issue: our AWS costs were scaling faster than revenue. With our community finance platform serving nearly 2 million users in underserved communities, we needed to optimize infrastructure without compromising reliability or user experience.
For an infrastructure team, this translated to an exciting challenge: How do you identify opportunities to run infrastructure more efficiently while simultaneously improving reliability and demonstrating clear ROI?
This represented an opportunity for smart resource management and strategic thinking that would align efficiency with fiscal responsibility. The possibilities included – (1) infrastructure that operated more efficiently while maintaining reliability, (2) optimized resource allocation that could improve our unit economics, and (3) systems that performed better while costing less to operate.
At SoLo, every system decision impacts real people. When our platform is responsive, community members can access financial assistance quickly, often during emergencies when time matters most.
To deliver meaningful cost reductions without compromising user experience, we needed to first understand where our infrastructure resources were being used and where we could operate more efficiently. This wasn’t about cutting corners but it was about smart resource allocation that would maintain reliability while reducing unnecessary spending.
Our approach was methodical: identify the biggest opportunities, analyze usage patterns thoroughly, and make changes that would improve both efficiency and cost-effectiveness. The goal was to optimize our way to better financial performance, not optimize our way to reduced capabilities.
Like most growing fintech companies, our AWS bill had several significant categories, but RDS Aurora immediately stood out as our largest single expense in Cost Explorer. When you’re running a financial platform processing millions of peer-to-peer transactions, database costs add up quickly, but the magnitude surprised us.
The initial Cost Explorer analysis revealed that RDS accounted for approximately 40% of our total AWS spend, making it the obvious starting point for optimization efforts.
My approach was methodical:
Cost Explorer revealed something interesting: our Aurora instances were generating substantial I/O charges alongside the base instance costs. This wasn’t immediately obvious from our monthly summaries, but the detailed breakdown showed I/O operations as a significant cost driver.
The next question: Was this a temporary spike or a consistent pattern?
I pulled 6 months of CloudWatch metrics to establish a baseline. The data clearly showed a consistently high I/O usage across multiple Aurora instances. Our workloads were genuinely I/O-intensive, serving the real-time financial data needs of our community lending platform.
Here’s where it got interesting. AWS offers I/O-optimized Aurora instances that include I/O operations in the base price, but they cost roughly 30% more than the standard instance price.
The analysis looked like this:
Current State (Standard Aurora):
Option 1 (I/O Optimized):
Option 2 (I/O Optimized + Reserved Instances):
The math favored Option 2, but only if we could accurately predict our baseline capacity needs and execute right-sizing without impacting performance.
Rather than doubling our instance costs with I/O optimization, we chose to commit to reserved instances for our baseline capacity. This required confidence in our usage patterns, but our 6-month analysis gave us that confidence.
Our implementation approach:
The risk calculation: Reserved instances require commitment, but our community lending platform has predictable base traffic patterns with well-understood peak behaviors.
Result: $2,500/month in savings on database instances.
Our analysis revealed a classic over-provisioning pattern: 8xlarge read replicas that were underutilized.
The right-sizing process:
Why this worked: Our data showed actual resource utilization was well below provisioned capacity, even during peak lending activity. The 4xlarge instances provided adequate headroom for growth while eliminating waste.
The confidence factor: We kept detailed monitoring in place and had rollback procedures ready, but the utilization data was clear enough to make this a calculated move rather than a risky guess.
Result: Additional $2,400/month in savings.
Moving to our data pipeline analysis, our FiveTran our contract was coming up for renewal while simultaneously facing increasingly frequent reliability issues.
This created an ideal opportunity to evaluate alternatives from both cost and operational perspectives. FiveTran reliability problems had become increasingly frequent over the past 2 months, with daily connector failures affecting our business operations.
Our PostgreSQL to Snowflake data synchronization had become unreliable with frequent connector failures. These weren’t isolated incidents – we were regularly seeing sync delays of 3-4 hours during FiveTran outages, right when we needed to decide whether to renew at existing pricing levels.
The business impact was significant:
The migration decision process: We evaluated several alternatives but settled on Airflow on ECS with Astronomer for key reasons:
Implementation approach:
The cost improvement: Moving from expensive managed service costs to self-hosted infrastructure with predictable pricing and better operational control.
Result: $2,000/month in cost savings, plus reliable data pipelines that our business teams can depend on for critical decisions.
Total Monthly Savings: $9,352 ($112,224 annually)
Database Optimization:
Storage Optimization:
Pipeline and Tool Optimization:
The infrastructure optimizations we implemented created opportunities for strategic reinvestment in platform capabilities which included –
Baselines are crucial. Confident infrastructure decisions require good data. Invest the time upfront to understand your actual usage patterns and performance characteristics.
Right-sizing is an ongoing practice, not a one-time project. Build monitoring and analysis capabilities that help you continuously optimize your infrastructure as your product and user base evolve.
Document everything. Your future self (and your teammates) will thank you for clearly documenting the reasoning behind infrastructure decisions, especially when those decisions involve trade-offs between cost, performance, and operational complexity.
The approach we used can be applied to any infrastructure optimization challenge:
At SoLo Funds, we’re building technology that serves underserved communities – people who traditional financial services often overlook. We facilitate peer-to-peer lending that helps community members access financial assistance when they need it most.
Through strategic infrastructure optimization, we created a win-win situation: our platform operates more efficiently and reliably while reducing costs that can be reinvested in platform improvements and competitive pricing. Every optimization we implement serves a dual purpose – maintaining excellent service for our current community while ensuring long-term sustainability.
The cost optimizations enabled focus on user value:
When you’re building technology for people who have been underserved by traditional systems, efficiency isn’t just about cost management, it’s about maximizing every resource to serve your community more effectively and sustainably.
