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Cloud waste did not demonstrably “come back” across the market: HashiCorp’s surveys found that the share of respondents reporting waste fell from 96% in 2023 to 91% in 2024. That is the share of organizations reporting some waste, not the share of cloud dollars wasted. What changed is the shape of the problem. Many teams have addressed obvious inefficiencies, leaving smaller, harder-to-capture opportunities, while FinOps now has to manage a wider range of technology spending and business priorities.
What “the market split” means
There is no established market-wide measure in the cited sources showing that the proportion or dollar value of cloud waste rose after declining. The phrase “waste came back” is better understood as a description of what teams encounter: waste remains common, but the next savings opportunity is often less obvious than the first.
That creates a practical split in how organizations experience optimization. Teams still building basic cost visibility may find substantial opportunities in idle resources and oversizing. More mature teams may already have tackled those “big rocks” and face a larger number of smaller changes, each requiring analysis, coordination, or workload trade-offs. The evidence supports this difference in maturity and remaining opportunity—not a precise segmentation of companies or a quantified divide between market groups.
There is also a change in scope. FinOps is moving beyond cloud infrastructure costs to include areas such as AI, SaaS, licensing, private cloud, and data centers. That makes cost optimization less about cutting a single bill and more about allocating costs, setting policy, forecasting, and judging spend against business value.
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What the reported waste figures do—and do not—show
HashiCorp’s 2024 State of Cloud Strategy survey, conducted with Forrester Consulting, found that 91% of respondents said their organization experienced cloud waste, compared with 96% in the 2023 survey. These are survey responses about whether an organization experienced waste; they do not say what percentage of cloud spending was wasted, how much money was lost, or whether waste increased for any particular company.
Respondents commonly identified several contributing factors:
| Reported contributor | Share of HashiCorp 2024 survey respondents | What it can mean in practice |
|---|---|---|
| Lack of needed skills | 41% | Teams may lack the capacity or expertise to identify, prioritize, and safely implement cost changes. |
| Overprovisioning | 40% | Resources may be sized above a workload’s actual needs, sometimes to preserve performance or accommodate uncertain demand. |
| Idle or underused resources | 35% | Resources can continue to incur charges despite being unused or serving less demand than expected. |
The percentages describe causes named in the survey, not a universal breakdown of wasted dollars. A team should treat them as useful places to investigate, not as a diagnosis of its own environment.
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How FinOps priorities changed from 2024 to 2026
FinOps Foundation reports show a shift in practitioner priorities, but they do not form one continuous measurement of cloud waste. The surveys use different respondent groups and questions, so their findings should be read as snapshots of what practitioners prioritized.
| Report | Reported emphasis | How to interpret it |
|---|---|---|
| 2024 | Reducing waste became the leading practitioner priority for the first time; managing commitment-based discounts also rose. The survey involved 1,245 respondents and reported average annual company cloud spend of $44 million. | A snapshot of priorities among that survey’s respondents, not an estimate of waste across all cloud users. |
| 2025 | Workload optimization and waste reduction led current priorities; 50% of practitioner respondents said optimization remained a priority. For the following 12 months, governance and policy ranked first in future priorities, with workload optimization second. The report describes respondents responsible for more than $69 billion in cloud spend. | Optimization remained important while respondents looked toward stronger governance. The respondent group was large-spending practitioners, not a census of every cloud customer. |
| 2026 | Optimization was described as “table stakes,” with more emphasis on value capabilities, governance, earlier decisions, and a broader FinOps remit. Practitioners also reported diminishing returns from traditional optimization. | A shift in the discipline’s focus does not establish that organizations have stopped optimizing or that waste has risen market-wide. |
The FinOps Foundation’s 2026 report captures the experience of teams that have moved beyond the largest savings opportunities: “We have hit the ‘big rocks’ of waste and now face a high volume of smaller opportunities that require more effort to capture.” The statement is attributed to an unnamed practitioner. It describes a real optimization challenge without proving that the overall amount of waste has increased.
Why savings get harder after the obvious fixes
The remaining opportunities are smaller or less certain
Once a team has removed clearly idle resources or corrected conspicuous overprovisioning, additional changes may affect a narrower set of workloads. Finding them can require more detailed usage data and involvement from the people who understand application behavior. The savings may be less certain, while the work to validate a change remains significant.
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Cost changes can create operational trade-offs
A lower bill is not automatically a better outcome. Rightsizing, changing a service, or altering a commitment can affect performance, resilience, flexibility, or engineering effort. Teams need to assess the workload and its business purpose before choosing a cost target; otherwise, an optimization can shift costs or create a larger operational problem.
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Skills and ownership matter
HashiCorp’s 2024 survey identified skills gaps as the most commonly reported contributor among the listed causes. Cost data alone does not assign responsibility or make a change safe. Finance, engineering, product, and platform teams may need shared definitions, ownership, and a process for turning a recommendation into a reviewed change.
FinOps now covers more than cloud infrastructure
In the FinOps Foundation’s 2026 survey, 98% of respondents said they manage AI spend, compared with 63% in 2025 and 31% in 2024. The same report said 90% manage SaaS or plan to, 64% manage licensing, 57% manage private cloud, and 48% manage data center. These are findings from that report’s respondent group, not universal adoption rates. They illustrate how a team’s remit can expand even as traditional cloud optimization matures.
How to find savings after the easy wins
- Establish a usable baseline. Agree on which costs are in scope, how they are allocated to teams or services, and the period against which changes will be assessed. Separate changes in usage, pricing, and workload demand so a bill movement is not mistaken for an optimization result.
- Look for workload-specific opportunities. Review utilization and resource lifecycles across compute, storage, databases, networking, and operational services. The FinOps Foundation’s Usage Optimization Opportunities Library includes examples spanning AWS, Azure, and Google Cloud, with filters for savings potential, service category, effort, and risk. Its examples include aged Azure snapshots and unused AMI snapshots; the library page was last updated June 30, 2025.
- Compare value, effort, and risk together. Estimate potential savings, but also identify engineering effort, operational risk, reversibility, and who must approve or implement the change. A small saving with low risk may be a better near-term choice than a larger estimate that requires extensive testing or threatens a critical workload.
- Validate before and after implementation. Check that the proposed change addresses actual demand, define how success will be measured, and monitor the workload after rollout. Where possible, make changes in a way that can be reversed if performance or reliability deteriorates.
- Turn recurring work into policy. When the same issue appears repeatedly, consider guardrails, review processes, or automation that prevent it from returning. The FinOps Foundation’s 2025 report placed governance and policy at the top of practitioners’ future priorities, reflecting the need to make good cost decisions part of routine operations.
How to compare optimization opportunities and tools
Use a consistent set of questions whether an opportunity comes from a cloud provider’s native service, a FinOps platform, or a team’s own analysis. The FinOps Foundation’s opportunity library offers a practical starting point for comparing individual actions.
| Dimension | Questions to ask |
|---|---|
| Provider and coverage | Does it apply to AWS, Azure, Google Cloud, or the specific services and accounts in scope? |
| Service and workload fit | Is the recommendation about compute, storage, databases, networking, CloudOps, or another area? Does it fit the workload’s demand and business role? |
| Potential savings | How is the estimate calculated, and does it reflect actual usage and applicable pricing rather than an unverified headline estimate? |
| Effort and risk | What engineering work, testing, approvals, and reliability trade-offs are involved? Can the change be reversed? |
| Governance and allocation | Can the approach help assign costs to owners, enforce policies, and make decisions early enough to influence spend? |
| Measurement and explainability | Can teams understand why a recommendation or score changed, and does the measure connect to workload outcomes and business value? |
For software approaches, also assess cost-data normalization, anomaly detection, forecasting, integrations, and implementation effort. Native provider tools may offer service-specific context; broader platforms may help unify views across providers or technology categories. The right choice depends on the organization’s coverage and workflows, and the cited evidence does not establish a universally best product.
What AWS’s Cost Efficiency score can tell you
AWS provides one example of a provider-specific way to track optimization. AWS introduced its Cost Efficiency metric in Cost Optimization Hub in November 2025. AWS defines it as a daily score from 0 to 100% representing the share of optimizable spend that is already well optimized. The metric combines workload optimization, such as rightsizing and idle cleanup, with rate optimization, such as Savings Plans and Reserved Instances.
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In its June 9, 2026 State of Cost Efficiency report, AWS said that, as of May 2026, its customer median score was 83 and its mean was 79. AWS also reported a 52-percentage-point score spread among smaller customers, compared with a 35-point spread among larger customers, whose scores were more tightly clustered. These are AWS customer findings based on an AWS-defined measure; they are not a cross-cloud benchmark or a measure of the percentage of all cloud spend wasted.
AWS has also noted that agreement on an efficiency measure can be difficult because engineering, finance, product, and leadership may value different outcomes. Optimizing a single score can undermine other work. Treat any metric as a management aid: agree on how it relates to workload needs and business outcomes, and pair it with checks for performance, resilience, and cost allocation.
Why the market story is not “waste is rising again”
The available evidence points to persistent reported waste, changing practitioner priorities, and more demanding optimization work—not a demonstrated rebound in market-wide waste. HashiCorp’s reported prevalence figure declined between its 2023 and 2024 surveys, while FinOps Foundation reports describe workload optimization as a continuing priority and practitioners in 2026 as facing diminishing returns after addressing the largest opportunities.
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1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsThose findings can coexist. A high share of organizations may still report some waste even if the most obvious waste is being addressed; meanwhile, teams that have matured may need more effort to realize smaller gains. At the same time, expanding FinOps to AI and other technology spend means that the optimization task is broader than cloud infrastructure alone. For readers evaluating their own organization, the useful question is not whether waste has “returned” everywhere, but which costs are attributable, which workloads can safely change, and whether the expected business value justifies the effort.
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