Customer Overview
PayFlow is a payment processing platform serving small and medium businesses across North America. Their platform handles payment acceptance, invoicing, and financial reporting for over 15,000 merchants.
In 2023, PayFlow experienced explosive growth: transaction volume increased 10x, from 2 million to 20 million monthly transactions. For most companies, that would mean a proportional increase in infrastructure costs. PayFlow achieved something different.
The Challenge
Hypergrowth Reality
Growing 10x in a year creates unique challenges:
Scaling pressure: Infrastructure decisions needed to happen fast. There was no time for extensive architecture reviews.
Investor expectations: Series B investors expected efficient unit economics. Cost per transaction was a key metric.
Reliability requirements: Payment processing requires 99.99% uptime. Cutting costs cannot compromise reliability.
Compliance constraints: PCI-DSS compliance added constraints to infrastructure changes.
The Cost Trajectory Problem
When PayFlow engaged with us, their projections showed a concerning trend:
| Metric | Current | 12-Month Projection (Before) |
|---|---|---|
| Monthly transactions | 2M | 20M |
| Monthly AWS spend | $45,000 | $450,000+ |
| Cost per transaction | $0.0225 | $0.0225 |
Linear cost scaling would consume their Series B runway faster than projected. They needed costs to grow sublinearly with volume.
"Our investors were clear: if we couldn't demonstrate improving unit economics, the next funding round would be challenging. We needed to prove that growth meant efficiency, not just bigger bills."
— CEO, PayFlow
Initial Architecture
PayFlow's original architecture was typical of fast-growing startups:
- Monolithic application on oversized EC2 instances
- RDS MySQL scaled vertically (bigger instances)
- S3 for document storage
- CloudFront for merchant portal
- Basic Auto Scaling with aggressive thresholds
Nothing was fundamentally wrong, but nothing was optimized for scale.
The Solution
FinOps Foundation
Before optimizing, we established FinOps practices:
- Cost allocation: Tagged all resources by team, environment, and purpose
- Unit economics tracking: Defined cost-per-transaction as the key metric
- Accountability: Each team received a cost dashboard
- Forecasting: Built models linking transaction volume to expected costs
These foundations made optimization measurable and sustainable.
Architecture Evolution
Rather than fighting growth, we embraced it with architecture changes:
Compute Optimization
Before: Large EC2 instances with low average utilization, sized for peak
After:
- Auto Scaling with predictive policies based on transaction patterns
- Graviton3 instances for 40% better price/performance
- Spot instances for batch processing (80% discount)
- Container migration for stateless services (better density)
Impact: 55% reduction in compute cost per transaction
Database Strategy
Before: Single large RDS instance, scaled vertically with each growth milestone
After:
- Read replicas for reporting queries
- ElastiCache for hot data (session, merchant lookup)
- RDS Proxy for connection pooling
- Aurora Serverless for variable workloads
Impact: 70% reduction in database cost per transaction
Storage and Data Transfer
Before: Standard S3 storage class for everything, CloudFront with default settings
After:
- S3 Intelligent-Tiering for automatic optimization
- CloudFront with aggressive caching policies
- VPC Endpoints for inter-service communication (eliminated data transfer costs)
Impact: 45% reduction in storage and data transfer costs
Commitment Strategy
With architecture optimized, we locked in savings:
| Commitment Type | Coverage | Discount |
|---|---|---|
| Compute Savings Plans | 70% baseline | 36% |
| Reserved Instances (RDS) | 100% primary | 42% |
| Spot for batch | 100% of batch | 80% |
| On-demand | Burst capacity | 0% |
Blended discount: 38% across all compute
Continuous Optimization
Growth doesn't stop, so neither does optimization:
- Anomaly detection: CloudBolt alerts on unexpected cost increases
- Weekly reviews: Engineering leads review cost changes
- Quarterly optimization: Deep dives on major cost categories
- Architectural planning: Cost implications included in technical decisions
The Results
The Numbers
| Metric | Before | After 12 Months | Change |
|---|---|---|---|
| Monthly transactions | 2M | 20M | +900% |
| Monthly AWS spend | $45,000 | $157,500 | +250% |
| Cost per transaction | $0.0225 | $0.0079 | -65% |
| Annual run rate | $540,000 | $1,890,000 | +250% |
Without optimization, AWS spend would have been $4.5M+ annually
Actual spend: $1.89M annually
Avoided costs: $2.6M in the first year alone
Unit Economics Transformation
The cost per transaction improvement told the story investors wanted to hear:
| Month | Transactions | AWS Spend | Cost/Transaction |
|---|---|---|---|
| Month 1 | 2M | $45,000 | $0.0225 |
| Month 4 | 5M | $67,000 | $0.0134 |
| Month 8 | 12M | $108,000 | $0.0090 |
| Month 12 | 20M | $157,500 | $0.0079 |
Costs grew 3.5x while transactions grew 10x—exactly the efficiency investors wanted.
Beyond Cost Savings
Improved reliability: Architecture changes actually improved system resilience
- Database read replicas provide failover
- Container orchestration enables faster recovery
- Auto Scaling responds faster to demand spikes
Better performance: Caching and read replicas reduced latency
- API response time: -40%
- Merchant portal load time: -55%
Engineering efficiency: Less time fighting infrastructure
- Fewer scaling incidents
- More time building features
- Better cost awareness across teams
Customer Perspective
"The ROI was immediate and obvious. We paid for a year of Sentasity in the first month of savings. More importantly, we proved to our investors that we could scale efficiently. That confidence was worth more than the dollar savings."
— CFO, PayFlow
"What I appreciated was the partnership approach. They didn't just hand us a list of recommendations and walk away. They worked with our team to implement changes safely and taught us how to maintain optimization going forward."
— VP of Engineering, PayFlow
Lessons for High-Growth Companies
What Enabled Success
- Early investment in FinOps: Tracking costs from the start made optimization measurable
- Architecture flexibility: Willingness to evolve architecture, not just resize instances
- Executive support: CEO made efficiency a company priority
- Engineering culture: Teams took ownership of their costs
Recommendations
For companies expecting rapid growth:
- Establish unit economics early: Define your cost metric (per transaction, per user, etc.)
- Build for elasticity: Design for horizontal scaling, not vertical
- Automate everything: Manual processes don't scale
- Optimize continuously: Don't wait until costs are painful
Planning for Growth?
Whether you're pre-growth or mid-scale, establishing FinOps practices early pays dividends. Our scanner and CloudBolt platform provide the visibility you need to scale efficiently.
Start your free scan to establish your cost baseline, or learn about Managed Billing for ongoing optimization support.
