How SquareOps Helped a FinTech Startup Reduce AWS Spend After a Cost Spike
- Ankush Madaan
- Case Studies
About
This case study highlights how SquareOps helped a fast-scaling technology platform optimize cloud infrastructure using proven FinOps practices and AWS best practices delivering measurable cost savings without impacting performance or reliability.
Industries
- AWS, Cloud Infrastructure, DevOps, FinOps, FinTech, High-Growth Startups, SAAS
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Client Overview
A fast-scaling FinTech startup saw AWS spend rise steadily through 2025, peaking at ~$44K in August. The platform was stable and production-ready, but cloud costs were increasing faster than expected driven by rapid scale, evolving workloads, and the absence of strong FinOps guardrails.
Like many growth-stage startups, the team faced:
- No clear cost ownership model
- Inconsistent tagging across services and environments
- No single, reliable way to connect spend to business context
SquareOps started operations in late August and ran a focused FinOps optimization program designed to:
- Deliver fast, measurable savings
- Avoid downtime or performance risk
- Preserve engineering velocity
Outcome:
Within months of engagement, AWS spend reduced from ~$44K (August) to ~$31K (November) while maintaining platform reliability.
The Baseline: What We Observed Before Optimization
When SquareOps engaged in late August, we analyzed historical AWS spend trends to understand how and why costs were growing, not just where they appeared in billing dashboards.
Using service-level spend patterns commonly surfaced in tools like AWS Cost Explorer (to help readers correlate the data), we observed the following across March–November 2025:
- Monthly AWS spend increased steadily from ~$16K in March to a peak of ~$44K in August
- The growth pattern was gradual and compounding pointing to structural inefficiencies, not a one-time spike
- Costs were scaling faster than application load and business growth, signaling missing governance and right-sizing controls
Primary Cost Drivers Identified
Our analysis showed spend concentration typical of a fast-scaling FinTech platform:
Service | What We Observed |
RDS | Largest and most consistent contributor, increasing month over month |
EC2 | Second-largest cost block, reflecting steady-state overprovisioning |
Supporting services | CloudFront, ELB, VPC (NAT + data processing), S3, CloudWatch, and EKS added incremental but compounding cost |
Aggregate Spend Pattern (Mar–Nov)
- Total AWS spend: ~$289,075
- RDS spend: ~$94,000
- EC2 spend: ~$64,400
Key Insight:
The data did not point to a single “bad month.” Instead, it revealed a cost model that would continue to compound as the startup scaled, unless ownership, right-sizing, and ongoing FinOps discipline were introduced.
The Challenge
The FinTech startup’s engineering leadership faced three common growth-stage challenges:
1. Cost Visibility Lagged Behind Scaling
Spend was spread across databases, compute, observability, and networking—making it difficult to quickly answer “what changed?” after each scale event.
2. Cost Accountability Was Unclear
Without consistent tagging and ownership mapping, teams could not reliably connect infrastructure spend to:
- Environments (prod, staging)
- Services
- Product or business owners
3. Optimization Had to Be Production-Safe
As a FinTech platform:
- Reliability and performance were non-negotiable
- Changes had to avoid downtime
- Engineering velocity could not be compromised
What SquareOps Did
Two-Phase Execution: Stabilize → Optimize
Phase 1 (Late August): Stabilize, Baseline, and Create Ownership
Cost & Usage Analysis
We decomposed AWS spend by major services:
- RDS
- EC2 / EKS
- CloudWatch
- VPC / NAT
- CloudFront
- ELB
- S3
This allowed us to distinguish:
- Persistent structural costs vs temporary spikes
- Workloads that scaled efficiently vs those that did not
Tagging & Ownership Model
We introduced a lightweight but enforceable tagging standard, covering:
- Environment
- Team
- Service
- Owner / cost center
This gave the startup immediate cost attribution without heavy process overhead.
Quick-Win Identification
Each optimization opportunity was ranked by:
- Savings potential
- Risk
- Implementation effort
Only high-impact, low-risk changes were prioritized first.
Phase 2 (September–November): Optimize the Top Cost Drivers (Without Downtime)
1. Database Optimization (RDS)
What we observed
- Steady-state overprovisioning
- Storage and IOPS allocations misaligned with actual usage
- Predictable workloads running on on-demand pricing
What we changed
- Right-sized selected instances where usage data supported change
- Optimized storage configuration
- Improved Reserved Instance / commitment coverage for stable components
Why it worked
RDS was the largest cost block; even moderate efficiency gains delivered meaningful absolute savings.
2. Compute Optimization (EC2 & EKS)
What we observed
- Compute sized for peak traffic but underutilized most of the time
- Duplicate headroom across environments
- Autoscaling tuned conservatively for safety rather than efficiency
What we changed
- Right-sized steady-state workloads
- Tuned autoscaling to preserve peak handling without paying for constant headroom
- Removed obvious “always-on” waste
Why it worked
EC2 was the second-largest driver; improved utilization directly reduced the monthly run-rate.
3. Observability Optimization (CloudWatch)
What we observed
- High-volume debug and low-value logs
- Long retention periods set by default
- Ingestion growing silently as the platform scaled
What we changed
- Reduced retention on low-value log groups
- Introduced filtering and ingestion controls
- Aligned logging with operational needs
Why it worked
CloudWatch optimizations typically produce quick wins with minimal risk, especially in scaling startups.
4. Networking & Delivery Optimization
(VPC / NAT, CloudFront, ELB)
What we observed
- High NAT Gateway processing charges
- Inefficient east–west traffic patterns
- Missed opportunities to use VPC endpoints
What we changed
- Introduced VPC endpoints where appropriate
- Reduced unnecessary NAT traversal patterns
Why it worked
Networking costs behave like a “tax”—removing inefficiencies lowered spend without touching application logic.
Safety & Guardrails
Given the startup’s growth pace and production sensitivity:
- Staged rollouts – no bulk changes
- Pre/post validation – latency, error rates, scaling behavior monitored
- Rollback-ready execution – every change had a clear revert path
No application refactors – developer velocity preserved
Results: Measured Outcomes
After SquareOps engagement began in late August, AWS spend reduced month over month:
Month | AWS Spend |
August (Peak) | ~$44K |
September | ~$38K |
October | ~$34K |
November | ~$31K |
What This Means
- ~$13K/month reduction from peak to November (~30%)
- ~$6K/month reduction immediately after engagement (~14%)
- Primary savings driven by RDS and EC2, supported by CloudWatch and networking optimizations
Beyond Savings: FinOps Built for a Scaling FinTech Startup
The startup didn’t just reduce spend, it gained durable cost control.
- Visibility: Clear service-level cost reporting
- Accountability: Teams own their infrastructure costs
- Governance: Monthly optimization cadence, anomaly alerts, and an active savings backlog
A Lightweight FinOps Playbook for FinTech Startups
- Start with service-level breakdown
- Top 2 drivers + 2 “silent” drivers (logs, NAT)
- Rank opportunities by Savings × Risk × Effort
- Do safe wins first
- Log retention, filtering, storage cleanup, commitment strategy
- Right-size with guardrails
- Validate p95 load, stage changes, keep rollback paths
- Lock it in
- Tagging, dashboards, alerts—otherwise spend creeps back
Ready to optimize your AWS costs?
SquareOps helps fast-scaling teams reduce cloud spend safely without downtime, performance impact, or slowing engineering velocity. Our production-ready FinOps approach delivers measurable savings while building long-term cost control. Let’s identify where your AWS spend can be optimized.
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