How ThinkSys Helped CaptainU Reduce AWS Cloud Costs by 20% and Improve Performance

Manual infrastructure resource allocation and on-demand cloud pricing can significantly drain operating margins. CaptainU faced escalating AWS cloud costs, with monthly spend reaching peak levels due to on-demand compute and database execution model limitations. These financial constraints directly impacted operational efficiency: unpredictable monthly billing, inefficient instance commitments, and underutilized commitment discount opportunities.

To transform their cloud financial management and establish cost predictability, CaptainU partnered with ThinkSys, AWS Cloud Optimization Specialists. By strategically implementing Compute Savings Plans and RDS Resetoolrved Instances across their primary infrastructure, CaptainU achieved substantial cost savings within just a few weeks.

This case study explores the implementation strategy, financial optimization journey, and tangible benefits CaptainU experienced.

About CaptainU

CaptainU provides an athletic recruiting platform that connects over 3 million student-athletes, college coaches, and event directors across North America. As a digital-first company, they rely heavily on cloud infrastructure to manage high-availability database engines, support web platforms, and handle transactional user workloads. But their cloud cost optimization process needed work.

The engineering team relied heavily on On-Demand instances across two AWS accounts. Most cloud spend came from CaptainU’s primary infrastructure, while MLQVentures supported the remaining workloads. As monthly AWS costs reached their highest level, unpredictable billing and limited commitment coverage placed growing pressure on operating margins.

Here’s a list of all the problems they were facing:

• Unoptimized On-Demand pricing across thousands of database and compute runtime hours created a massive financial bottleneck that inflated cloud operating spend.
• Previous lack of commitment coverage forced the engineering team back to expensive, undiscounted On-Demand pricing models.
• Monthly budget allocations stayed unpredictably high, limiting flexibility for allocating engineering budget to new application features.
• The finance and engineering teams struggled to catch cost spikes early and establish stable run-rate baselines across linked accounts.

Strategic Planning and Risk Mitigation

As part of the long-term optimization roadmap, ThinkSys conducted a strategic analysis of CaptainU's infrastructure path, evaluating two primary approaches to address technical debt and outdated dependencies.

The first option, Plan A (OS Upgrade), offered a 24-30 week timeline but carried significant risks of regression and downtime during the transition. The second option, Plan B (Application Refactoring/Containerization), required a more extensive 36-40 week timeline to facilitate code splitting and a full transition to modern containerized environments.

Based on this evaluation, ThinkSys explicitly recommended committing to Plan B. Although it required a longer initial investment, this path was chosen to avoid a 40-50% rework penalty associated with the short-sighted nature of the alternative OS upgrade approach.

Solution

The optimization team introduced a structured cloud financial engineering solution:

• Discount Model Selection: The team selected AWS Compute Savings Plans and RDS Reserved Instances based on key advantages. These commitment models offer exceptional cost reduction (up to 26% on RDS and 24% on compute), provide compute instance family flexibility, and seamlessly cover multi-account architectures while retaining full infrastructure performance.
• Framework & Accounting Amortization: The team created a custom billing amortization model tailored to CaptainU's specific account hierarchy. This framework incorporated financial reporting best practices and was designed to account for upfront monthly charges (such as RDS RI commitment fees) evenly across daily cost tracking.
• Usage Pattern Optimization: Through careful baseline analysis and workload profiling, the team mapped raw usage data across core services, consolidating variable compute usage into dedicated commitment tiers for Amazon Aurora MySQL RDS and Amazon EC2 instance families.
• Strategic Implementation: The initial optimization phase focused specifically on scenarios marked as highest spend: Relational Database Service (Aurora MySQL) and EC2 Compute instances. This approach ensured the most capital-intensive workloads were optimized thoroughly from the start.
• Monitoring Integration: Working closely with the engineering team, commitment tracking and daily run-rate monitoring were integrated into AWS Cost Explorer and executive reporting dashboards. This enabled real-time visibility across both development and production environments, providing immediate feedback on cloud spending.

Results and Future Opportunities

When all the changes were implemented, the difference was evident:

• Optimization Progress: Strategic coverage across key database and compute workloads delivered over ~20% in savings during the 15-day period. It also improved cost predictability and made ongoing spend easier to manage.
• Caught Major Inefficiencies Early: Daily cost monitoring picked up on baseline inefficiencies, like unreserved database workloads running 24/7 on On-Demand rates, before they accumulated further monthly overhead.
• Tax & Amortization Visibility: Structured reporting revealed recurring tax and fee spikes on commitment renewal days, allowing the finance team to standardize accounting clarity across the application lifecycle.
• Service Spend Tracking: Automated cost checks spotted underutilized resources and unoptimized auxiliary services (like ElastiCache running on On-Demand rates) that manual auditing might have missed.
• More Confidence for Engineering & Finance: With automated commitments handling baseline compute and database costs, the engineering team could focus on optimizing application performance. That made the whole team feel more confident about scaling infrastructure sustainably.
• Ongoing Financial Optimization: The final run-rate operates much more efficiently now, letting commitment plans handle repetitive baseline workloads while engineering targets additional services like ElastiCache Reserved Nodes for future savings.

Automation Implementation Steps

Step 1: Cleaned Up and Restructured Infrastructure Baseline

ThinkSys reviewed all AWS service costs, eliminated unneeded instance overhead, and isolated recurring production workloads from temporary usage spikes. This cut down on unnecessary baseline spend while still preserving full production performance.

Step 2: Selected the Right Commitment Strategy

ThinkSys looked at several commitment options and went with a combination of AWS Compute Savings Plans and RDS Reserved Instances. It was a good fit because it provides high discount rates, supports parallel multi-account coverage across CaptainU and MLQVentures, maintains instance flexibility, and requires zero architectural changes.

Step 3: Built a Custom Amortization & Accounting Model

ThinkSys also developed a tailored cost tracking framework that matches CaptainU's linked account architecture. We focused on creating reusable reporting structures and clear metric organizations. The system includes detailed daily logging, tax amortization, account settings, and tracking points for expanded future optimization.

Step 4: Prioritized High-Spend Scenarios in Phase 1

ThinkSys took a strategic approach by targeting the most budget-critical services first. We started with workloads labeled RDS Aurora MySQL and EC2 Compute. On May 16, 2026, we activated Compute Savings Plans, followed by RDS Reserved Instances on May 19, 2026. This delivered quick results and proved the strategy's value while establishing a foundation for broader coverage.

Step 5: Monitoring Cost Tracking

The ThinkSys cloud optimization team integrated commitment utilization metrics and daily cost tracking directly into the existing CI/CD pipelines. This ensures that infrastructure changes are monitored for cost impact in real-time before deployment. While there were initial adjustments regarding upfront commitment billing (such as the June 1 RDS RI fee), transparent accounting alignment across both primary and supporting accounts now ensures stable, predictable cost visibility.

Conclusion

ThinkSys successfully transformed CaptainU’s cloud infrastructure management. By shifting away from reactive on-demand billing, CaptainU now enjoys predictable, stable operating margins that allow the team to prioritize platform innovation over infrastructure firefighting. This successful implementation demonstrates the impact of ThinkSys’s expertise in achieving measurable cost reduction and operational efficiency.

Looking to achieve similar results? As your cloud optimization partner, ThinkSys helps organizations identify spending inefficiencies early, streamline financial reporting, and transform infrastructure spend into a strategic advantage. Contact ThinkSys today to learn how we can help your team optimize, scale, and work smarter.

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