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Cloud waste did not demonstrably “come back” across the market. The available figures show that waste remains widely reported, while optimization is getting harder as teams move past obvious savings and FinOps expands into AI and other technology costs. The split is better understood as a difference in maturity, cost ownership, and remaining opportunity—not as a proven rise in the share of cloud spending wasted.
Did cloud waste really come back?
No market-wide rebound is established by the figures available here. HashiCorp’s 2024 State of Cloud Strategy survey, conducted with Forrester Consulting, found that 91% of respondents said their organizations experienced cloud waste, down from 96% in the 2023 survey. Those numbers measure the share of respondents reporting that their organization had waste; they do not measure the percentage or dollar amount of cloud spend wasted.
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The annual findings also measure different things. FinOps Foundation reports describe practitioners’ priorities and areas of responsibility, while AWS’s Cost Efficiency score is a provider-defined measure of optimization among AWS customers. They cannot be combined into a single time series showing that waste fell and then rose.
Why do teams still find waste after optimization?
The first savings are easier to see
Teams often begin with visible opportunities such as idle resources and overprovisioned workloads. HashiCorp’s 2024 survey respondents commonly cited a lack of needed skills (41%), overprovisioning (40%), and idle or underused resources (35%) as contributors to waste. These are reported contributing factors, not a universal breakdown of waste or a measurement of dollars lost.
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Once the conspicuous opportunities have been addressed, further savings may involve many smaller changes that take more analysis and coordination. The FinOps Foundation’s 2026 report describes practitioners facing diminishing returns from traditional optimization and quotes an unnamed practitioner: “We have hit the ‘big rocks’ of waste and now face a high volume of smaller opportunities that require more effort to capture.” That supports a harder-savings explanation, not evidence of a market-wide rebound.
Optimization is becoming an operating discipline
Finding an underused resource is only the start. A team still needs to determine whether it can safely resize, schedule, consolidate, or remove it, and whether doing so affects reliability, performance, or delivery plans. As the easiest fixes are exhausted, cost decisions depend more on workload context, engineering capacity, and clear ownership than on a list of potential cuts.
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What changed in FinOps priorities from 2024 to 2026?
| Report | What it says | How to read it |
|---|---|---|
| FinOps Foundation, 2024 | Reducing waste became the leading practitioner priority for the first time; managing commitment-based discounts also rose. The survey had 1,245 respondents and reported average annual company cloud spend of $44 million. | A snapshot of respondents’ priorities and spending context, not a measure of waste prevalence. |
| FinOps Foundation, 2025 | Workload optimization and waste reduction remained the leading current priority for 50% of practitioner respondents. Governance and policy ranked first among priorities for the following 12 months, with workload optimization second. The report describes respondents responsible for more than $69 billion in cloud spend. | A survey of large cloud spenders, not a census of all cloud customers. |
| FinOps Foundation, 2026 | Optimization remained important as the discipline broadened toward value management, governance, and earlier decisions. The report says 98% of respondents managed AI spend, compared with 63% in 2025 and 31% in 2024; it also reports 90% managing SaaS or planning to, 64% licensing, 57% private cloud, and 48% data center. | Survey findings about the respondents’ remit, not universal adoption rates or a continuous measure of waste. |
The reports indicate a widening job: teams are still expected to optimize workloads, but they are also being asked to govern spending, allocate costs, forecast, and connect technology decisions to business value. Because the surveys ask different questions of different respondent groups, the sequence describes changing priorities rather than proving that one change caused another.
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It is a useful way to describe different starting points, not a formally quantified market segment. Teams that have not yet established ownership or basic visibility may still have substantial, relatively obvious work. More mature teams may have addressed those “big rocks” and be spending more effort on smaller, workload-specific opportunities, policy, forecasting, and value measurement. A company can also have both conditions at once across different providers, services, or business units.
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- Less mature cost ownership: Cloud bills are visible, but costs may not be consistently allocated to the teams or products that can change them.
- Basic optimization underway: Teams are identifying idle, underused, and oversized resources, but need repeatable processes to implement changes safely.
- Broader FinOps scope: Cloud optimization is joined by governance and management of AI, SaaS, licensing, private cloud, or data-center spending.
These are practical patterns for interpreting the reports, not categories assigned to specific companies by the surveys.
How should teams find savings after the easy wins?
Prioritize opportunities by workload fit and business impact, not by a savings estimate alone. The FinOps Foundation’s Usage Optimization Opportunities Library, last updated June 30, 2025, includes examples across AWS, Azure, and Google Cloud, with filters for savings potential, service category, effort, and risk. Listed areas include CloudOps, compute, database, storage, and network; examples include aged Azure snapshots and unused AMI snapshots.
- Establish ownership and context. Identify the team, product, or workload responsible for the spend, and understand its performance and availability needs.
- Identify a specific opportunity. Look for a concrete action—such as investigating an idle or underused resource—rather than treating a broad cost category as a mandate to cut.
- Estimate the trade-off. Compare potential savings with implementation effort, operational risk, and the possibility that the change could affect reliability or delivery.
- Make and verify the change. Agree on who approves and implements it, then check both the cost result and workload behavior against the intended outcome.
- Turn repeatable wins into policy. Where a decision is safe and consistent, use governance or automation to prevent the same kind of waste from recurring.
This sequence helps distinguish an attractive estimate from a worthwhile change: a high projected saving is not useful if the workload cannot tolerate the action or no team owns the follow-through.
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How should teams compare native tools and FinOps platforms?
There is no single tool choice established by these findings. Compare an approach against the work your organization needs to do, including whether it can cover the relevant providers and services, normalize and allocate cost data, support workload and rate optimization, and connect findings to governance and business outcomes.
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| Comparison area | Question to ask |
|---|---|
| Coverage and data | Does it cover the providers and technology categories in scope, and can teams reconcile and allocate costs consistently? |
| Opportunity support | Can it help with both workload changes, such as rightsizing and idle cleanup, and rate choices, such as commitment-based discounts? |
| Governance and planning | Does it support policy workflows, forecasting, anomaly detection, and clear ownership of actions? |
| Explainability and effort | Can teams understand why an action is recommended, what assumptions it uses, and what it will take to implement? |
| Business fit | Can results be judged alongside workload reliability, performance, and business value rather than a spend-reduction target alone? |
AWS offers one provider-specific example. In November 2025, AWS introduced a Cost Efficiency metric in Cost Optimization Hub to track efficiency over time. AWS says organizational agreement on measurement can be difficult because engineering, finance, product, and leadership may prefer different metrics; it also cautions that improving one metric can undermine other optimization work. AWS’s announcement quotes an unnamed customer saying it took more than a year after building an internal efficiency metric to obtain organizational buy-in.
What does AWS’s Cost Efficiency score measure?
AWS’s June 9, 2026 Cost Efficiency report defines the score as a daily value from 0% to 100% representing the percentage of optimizable spend that is already well optimized. It combines workload optimization, including rightsizing and idle cleanup, with rate optimization, including Savings Plans and Reserved Instances.
For May 2026, AWS reported a median customer score of 83 and a mean of 79. It also reported a 52-percentage-point score spread among smaller customers, compared with 35 points among larger customers, whose scores were more tightly clustered. These figures describe AWS customer data using AWS’s own metric; they are not a multi-cloud benchmark or a measure of how much cloud spending the market wastes.
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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsThe score can help an AWS customer track a provider-defined measure over time, but it should not stand in for workload health or business outcomes. A team choosing any single optimization metric should make clear what it captures and what it leaves out.
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