Overview
A high-growth software company faced rising Amazon Web Services (AWS) costs, rigid long-term discount instruments, and increasing operational overhead to manage cloud costs. These challenges were partially caused by a third-party FinOps platform yielding suboptimal results. Upon switching to ProsperOps, the company boosted its Effective Savings Rate from 40.4% to 46.1% and reduced Commitment Lock-in Risk from 2+ years down to <6 months. At the same time, ProsperOps automated manual processes, reducing the overhead and effort required for rate optimization, allowing their team to focus on more important priorities.
Challenge 1: Savings at the expense of higher Commitment Lock-in Risk
This company, which provides customer experience software to hundreds of organizations worldwide, has experienced rapid growth in recent years. As its AWS environment and workloads grew, across compute services and ElastiCache, so did its cloud spend. To manage rising costs, the FinOps team began using a third-party platform for rate optimization. Initially, they achieved an Effective Savings Rate (ESR) of 40.4% for AWS compute. However, savings performance was inconsistent and came at the cost of increased risk. Before switching to ProsperOps, the company’s Commitment Lock-in Risk was 2.2 years, meaning that on average, they would have to hold onto their commitments for at least 2 years.
You must measure and track both ESR and Commitment Lock-in Risk (CLR) to benchmark, monitor, and manage the success of your rate optimization efforts. The Effective Savings Rate (ESR) measures how effectively discount instruments, such as Reserved Instances or Savings Plans, are working in practice. ESR represents the realized savings rate compared to purchasing cloud services on-demand and is a key FinOps metric for gauging the ROI of cloud cost optimization efforts. Organizations, such as the global software company, that use third-party tools with immature rate optimization strategies, unknowingly trade savings for risk.
Commitment-based discounts offer lower rates than on-demand pricing, but come with inherent risk. Organizations must continue to pay for services during the committed term, even if usage changes and those services are no longer needed. For example, if usage drops due to engineering optimizations, demand fluctuations, or architectural shifts, organizations may be stuck paying for unutilized commitments. Commitment Lock-in Risk (CLR) measures the time-based risk of committing to a cloud provider for discounted rates, expressed in months. The lower the CLR, the lower the risk. World-class FinOps programs are optimizing for both ESR and CLR.
The software company’s CLR for AWS Compute averaged greater than 2 years because a significant portion of its compute spend was tied up in long-term, inflexible commitments.
When ESR gains come at the cost of long-term Commitment Lock-in Risk, are they truly optimizing or just trading savings for risk?
Challenge 2: Volatile workloads limit further ESR improvements
With millions being spent each month on AWS compute, an additional 5% improvement in ESR would mean material savings for the company, amounting to upwards of $50,000 in additional monthly savings, yielding more than $600,000 annually.
However, achieving the highest possible outcomes from rate optimization proved to be incredibly complex. The company would need to balance savings outcomes without adding further risk. This was especially challenging without automation, given the company’s highly volatile usage patterns. Covering usage too aggressively with discounts could result in incurring costs for unutilized commitments if usage declines. On the other hand, being too conservative with commitment coverage could result in paying more on-demand rates and missing valuable savings opportunities. Fully optimizing savings outcomes requires intelligent automation that operates 24/7, continuously monitoring and adjusting coverage levels to achieve a higher ESR without compromising Commitment Lock-in Risk.
Challenge 3: Operational overhead from partial automation
The company was using a third-party platform to manage AWS discount instruments that was not fully automated. Engineers and the FinOps team still had to monitor usage trends, validate recommendations, coordinate purchases, and intervene regularly to maintain coverage. This added ongoing operational overhead, diverting time and focus away from higher-value work.
While the tool helped facilitate basic execution, it still required significant human involvement, ultimately resulting in delays and suboptimal outcomes. This turned what should have been a background process into a recurring drain on team capacity.
How ProsperOps Improved ESR, Flexibility, and Operational Efficiency
To address their Commitment Lock-in Risk exposure, untapped savings potential, and growing engineering overhead, the company partnered with ProsperOps to implement Autonomous Discount Management (ADM) across AWS Compute and ElastiCache. ProsperOps replaced reactive workflows with real-time, intelligent automation—freeing teams from manual discount management and enabling smarter optimization at scale.
Key areas of impact included:
1. Reducing Commitment Lock-in Risk with smart, flexible commitments
The team was exposed to long-term financial commitments that couldn’t easily adapt if usage declined. ProsperOps rebuilt its discount structure using a blend of RIs and SPs, including flexible Convertible RIs. A blended portfolio gave their team more control and optionality, reducing CLR from over 2 years to just 6 months without compromising commitment coverage.
2. Driving incremental savings without increasing risk exposure
At their scale, unlocking more savings wasn’t about increasing coverage; it was about better timing, accuracy, and risk control. ProsperOps automates ongoing adjustments based on real-time usage and fine-tunes commitment levels without adding risk. With Global Cyclical Optimization, a feature of ProsperOps, our algorithms identified recurring usage patterns and the exact level of coverage above the trough that maximizes savings. As a result, the company increased Effective Savings Rate for AWS Compute from 40.4% to 46.1%, adding 5.7% in incremental savings, without overcommitting or straining internal capacity.
3. Eliminating manual burden and operational risk
Their FinOps team was no longer responsible for continuous tracking of usage patterns, coordinating RI purchases, or manually addressing coverage gaps. ProsperOps integrates directly with AWS via the API, executing thousands of automated commitment adjustments behind the scenes. The company no longer depended on tribal knowledge, calendar reminders, or cross-functional approvals; it simply happened. This allowed the FinOps team to redirect their focus toward higher-value initiatives, such as budgeting, forecasting, and cost allocation, without worrying about daily execution.
Before
With
Effective Savings Rate
40.4%
46.1%
Coverage
90%
98.8%
Commitment Lock-in Risk
2.2 years
6.8 months
Final Thoughts
In modern cloud environments where usage is elastic but commitments are inelastic, the true challenge isn’t just getting a good discount; it’s doing so without sacrificing flexibility. By automating commitment management, organizations can unlock higher savings, reduce financial risk, and reclaim time for their engineering teams.
If you’re relying on manual processes or semi-automated tools to manage long-term cloud commitments, it’s worth asking—how much flexibility and savings are you leaving on the table?
About ProsperOps
ProsperOps is the leading FinOps automation platform for cloud cost optimization on Amazon Web Services (AWS), Google Cloud, and Microsoft Azure. Eliminating waste and achieving cost-saving goals is challenging when cloud usage is elastic, but commitments are inelastic. Founded in 2018, ProsperOps reduces costs by synchronizing rate optimization with workload optimization, eliminating waste and boosting cross-team efficiency for FinOps. Our platform drives world-class Effective Savings Rates and mitigates Commitment Lock-In Risk for our customers.
