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7The easy cloud cost savings are getting harder to find. That is one of the key observations in the FinOps Foundation’s 2026 State of FinOps report. Practitioners report that many of the obvious sources of cloud waste have already been addressed. What remains is a high volume of smaller cloud optimization opportunities that can take significantly more effort to capture.
As one FinOps practitioner put it, “We have hit the ‘big rocks’ of waste and now face a high volume of smaller opportunities that require more effort to capture.”
Early in a cloud optimization program, the savings are often easier to spot. Teams can remove idle resources, clean up unused capacity, shut down abandoned environments, and address straightforward cloud cost optimization opportunities across their infrastructure.
The opportunities left behind are different. They are smaller, spread across workloads, and often require someone to investigate whether a change is actually worth making.
The question, then, is no longer simply:
Can we save money here?
It is also:
How much effort will it take to capture that saving?
A recommendation may show a potential saving, but realizing it can still require engineering time to understand the workload, make the change, test it, and confirm that the expected saving was delivered.
When the potential saving is small, that effort matters. FinOps teams need to consider not just the size of an opportunity, but the cost of pursuing it.
The economics of chasing smaller opportunities
Cloud cost optimization tools can identify underused resources and surface potential savings. Turning those recommendations into realized savings is harder.
Usage optimization often requires engineering involvement. Engineers need to understand how a workload behaves before changing its size, configuration, or capacity. This is particularly relevant when rightsizing resources or changing workload capacity. A recommendation that looks straightforward on a cost dashboard may need more investigation before anyone is comfortable acting on it.
The FinOps Foundation's Usage Optimization guidance highlights this trade-off: teams must weigh expected savings against the effort, risk, and disruption of implementing an optimization.
That trade-off becomes more significant as potential savings shrink.
FinOps teams also have limited capacity. The 2026 FinOps report notes that organizations spending more than $100 million a year on cloud have an average of around 8 to 10 FinOps practitioners, alongside contractors. A team of that size cannot manually work through every small cloud optimization opportunity across a large cloud environment.
Not every recommendation deserves an engineer’s time. We need to ask whether engineering time is actually justified, and where FinOps can capture the savings without pulling engineers into another optimization task.
Usage optimization and rate optimization are not the same thing
Usage optimization changes how much infrastructure a workload uses. It includes rightsizing resources, removing idle capacity, improving utilization, and changing workloads so they consume resources more efficiently. Because these changes can affect running applications, they often require engineering context, testing, and deployment.
Rate optimization addresses a different part of the cost equation: what the organization pays for the resources it uses. This includes mechanisms such as Savings Plans, Reserved Instances, Committed Use Discounts, and other pricing and commitment options.
The FinOps Foundation defines Rate Optimization as managing the rates an organization pays for its resources. Its guidance also emphasizes centralized management of commitment discounts, regular analysis of coverage and utilization, and increasing automation as organizations mature.
The distinction is simple:
Usage optimization reduces the amount of infrastructure an organization needs. Rate optimization reduces the rate paid for the infrastructure it continues to use.
That distinction becomes important when the remaining cloud optimization opportunities are smaller and increasingly dependent on engineering time. Cloud rate optimization offers a different path altogether by reducing the rate paid for existing usage without requiring a change to the application itself.
Why automated cloud rate optimization matters
Rate optimization has traditionally involved reviewing historical usage, estimating future demand, deciding what to commit to, and monitoring whether those commitments continue to match actual usage.
Cloud usage also changes over time. A commitment that made sense when it was purchased may no longer fit as workloads, demand, or infrastructure requirements change.
Rate optimization therefore becomes an ongoing activity rather than a one-time purchasing decision.
The FinOps Foundation's Rate Optimization guidance describes a mature approach in which commitment portfolios are continuously managed against actual usage, purchase cycles become more frequent, and automation responds when demand patterns change.
For a small FinOps team, repeatedly reviewing usage and commitment data can become a significant operational task. Automated commitment management can handle more of that monitoring and help identify when coverage, utilization, or commitment decisions need revisiting.
The decisions will still require judgment. FinOps teams need to decide how much to commit, what level of flexibility is appropriate, and how future usage changes could affect those decisions.
Automation simply reduces the repetitive work around those decisions, leaving more time for the parts that require context.
Shrink first, then commit
Automating cloud rate optimization does not mean skipping usage optimization.
Commitments should reflect the infrastructure an organization actually expects to use. If the underlying workload is oversized or likely to change, committing before understanding that usage can create a different problem: paying for discounted capacity that is no longer needed. This is why a “shrink first, then commit” approach can be useful, particularly when rightsizing workloads before committing to Savings Plans or Reserved Instances.
The AWS State of Cost Efficiency Report provides evidence for the relationship between rightsizing and commitments. AWS analyzed more than 71,000 opted-in customer accounts and found that customers combining rightsizing with Savings Plans performed better on its Cost Efficiency measure than customers relying on Savings Plans alone.
The same analysis found that customers with 95% to 100% Savings Plan coverage had 65% to 80% less visible non-Savings Plan optimization opportunity than customers with 0% to 25% coverage.
That does not necessarily mean the underlying cloud optimization opportunities had disappeared. High commitment coverage can make some opportunities less visible in the metric.
The AWS findings reinforce the need to look at both sides. Rightsizing reduces the infrastructure an organization needs; commitments reduce the rate paid for infrastructure that remains.
Organizations also do not need to wait until every workload is perfectly optimized before considering rate optimization. The practical question is where engineering effort creates enough value to justify the work and where rate management can be handled more efficiently.
A different way to think about cloud optimization
The “big rocks” of cloud waste are not necessarily gone everywhere. But the FinOps Foundation's 2026 report shows that many practitioners are reaching a point where the obvious opportunities have already been addressed and the remaining ones require more effort to capture.
That makes the economics of cloud cost optimization itself part of the problem.
A savings opportunity is not valuable simply because a tool can identify it. Teams also need to consider the work required to realize that saving.
Not every saving needs to become an engineering ticket. Some changes will still require engineering context, while others FinOps can handle. Rate optimization is one area where more of that work can be centralized and automated.
That means being more selective about where people spend their time. Some savings will justify engineering effort. Others can be managed by FinOps, and rate optimization is one area where more of that work can be automated.
The next stage of FinOps may therefore be less about finding more recommendations and more about deciding which ones are worth acting on. When the big rocks are gone, the question becomes whether the effort required to capture the smaller savings from cloud optimization is justified and where automation can make that equation work.
To make cloud commitment management simpler, CloudKeeper Commit automates commitment optimization to help businesses maximize savings without the constant manual effort. Explore CloudKeeper Commit to see how it can help you get more from your cloud spend.
Frequently Asked Questions
Q1: What should businesses focus on after addressing the biggest sources of cloud waste?
Once teams address idle resources and obvious inefficiencies, they should look for smaller savings opportunities across their cloud environment. The key is to weigh the potential savings against the time and effort needed to achieve them.
Q2. What is the difference between usage optimization and rate optimization?
Usage optimization focuses on how much infrastructure a workload uses, while rate optimization focuses on how much the business pays for it. Rightsizing a resource is an example of usage optimization; using AWS Savings Plans or Reserved Instances to lower costs is rate optimization.
Q3. How can automation make cloud cost optimization easier?
Tracking cloud usage and reviewing commitment coverage manually can take up a lot of time, especially across large environments. Automation helps teams monitor changes, identify opportunities, and manage commitments without having to review everything manually.
Q4. How does CloudKeeper Commit help businesses reduce cloud costs?
CloudKeeper Commit helps businesses manage AWS commitments and optimize savings through automated commitment management and continuous rebalancing. This helps keep commitments aligned with changing cloud usage while reducing the need for manual intervention.
Q5. Why should businesses rightsize workloads before committing to cloud discounts?
If a workload is using more resources than it needs, committing to its current level of usage could lock the business into unnecessary spend. Rightsizing first gives teams a clearer picture of their actual requirements before they decide how much usage to commit to.
