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What's the State of AI Costs in 2025?

Bill Buckley
CloudZero

Artificial intelligence (AI) is radically shifting how organizations operate and provide value, running the gamut from intelligent automation to machine learning at scale. It's become a competitive necessity, and organizations are eager to benefit from AI's efficiency boost and innovation possibilities.

Yet, while companies adopt AI at a record pace, they also face the challenge of finding a smart and scalable way to manage its rapidly growing costs. This requires balancing the massive possibilities inherent in AI with the need to control cloud costs, aim for long-term profitability and optimize spending.

CloudZero's State of AI Costs in 2025 report examines how (and how much) companies are investing in AI — and whether they can confidently calculate the return on that investment (ROI). The report reveals a dynamic, volatile situation in which AI's budgets and momentum are growing quickly, as are organizations' expectations about its value. Yet these same companies are experiencing limited AI governance, misalignment and difficulty determining AI ROI. Now that cloud-based AI tools claim the biggest slice of the budget pie, attribution and cost visibility are crucial. Without them, organizations face the risk of unpredictable and unsustainable AI spend.

Some of the key takeaways our report found included:

  • AI spending is skyrocketing – This year, average monthly AI budgets will increase by 36%. That signals a big pivot toward larger, more complex AI initiatives.
  • Companies are struggling to evaluate AI ROI – Only 51% of companies definitively said they felt confident calculating the ROI of AI initiatives, largely due to an increasing visibility gap. Concurrently, cloud-based tools are dominant, making cloud cost visibility and attribution essential for optimizing AI ROI.
  • Unclear profitability remains a challenge – The most popular AI tools are designed for scalability, automation, and cloud deployment, yet their profitability remains unclear without effective cost tracking.

AI Spending on the Rise

Last year, organizations spent an average of $62,964 per month on AI. The 2025 report shows this amount will increase by 36% to $85,521. It's also noteworthy that the portion of companies planning to invest over $100,000 per month in AI tools will double — 40% this year compared to just 20% last year.

This significant spending uptick suggests that companies are increasing their AI projects to reap the benefits they promise. However, as their spend rises, businesses must ask this essential question: How confident are we about the ROI we're getting from our AI initiatives?

Prioritizing AI Explainability

This year's report revealed that 44% of respondents plan to invest in improving AI explainability. Their goals are to increase accountability and transparency in AI systems as well as to clarify how decisions are made so that AI models are more understandable to users. Juxtaposed with uncertainty around ROI, this statistic signals further disparity between organizations' usage of AI and accurate understanding of it.

In addition to explainability, businesses will prioritize AI robustness and security (41%), computing and cloud resources (39%), and improving customer experience (39%) this year. These priorities suggest a pivot toward AI deployments that are more scalable, transparent, and responsible.

Why Is It So Hard to Measure ROI?

Why is measuring AI's ROI still so hard for many businesses? The main reasons are:

  • Cloud expenses, maintenance, and other hidden costs
  • Difficulty separating the impact of AI from other business factors
  • Difficulty attributing AI costs to the right sources

Consequently, 49% of companies do not believe strongly in their AI ROI tracking. This speaks to a need for a stronger and more consistent cost attribution and tracking approach.

ROI Confidence Comes from Cost Optimization Tools

Of the companies that use third-party platforms, over 90% reported high awareness of AI-driven revenue. That awareness empowers them to confidently compare revenue and cost, leading to very reliable ROI calculations.

Conversely, companies that don't have a formal cost-tracking system have much less confidence that they can correctly determine the ROI of their AI initiatives. 41% of participants said they only "somewhat agree" about their ability to do this. These responses underscore the importance of implementing a formal and reliable cost-tracking system to evaluate ROI accurately.

Pairing AI Innovation with Cost Intelligence

Even the best-planned AI projects can become unexpectedly expensive if organizations lack effective cost governance. This report highlights the need for companies to not merely track AI spend but optimize it via real-time visibility, cost attribution, and useful insights. Cloud-based AI tools account for almost two-thirds of AI budgets, so cloud cost optimization is essential if companies want to stop overspending.

Cost is more than a metric; it's the most strategic measure of whether AI growth is sustainable. As companies implement better cost management practices and tools, they will be able to scale AI in a fiscally responsible way, confidently measure ROI, and prevent financial waste.

Methodology: This report is based on a survey conducted by CloudZero of 500 US software engineers, senior managers and above in organizations with 250 to 10,000 employees. The survey was conducted in March 2025. 

Bill Buckley is SVP of Engineering at CloudZero

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What's the State of AI Costs in 2025?

Bill Buckley
CloudZero

Artificial intelligence (AI) is radically shifting how organizations operate and provide value, running the gamut from intelligent automation to machine learning at scale. It's become a competitive necessity, and organizations are eager to benefit from AI's efficiency boost and innovation possibilities.

Yet, while companies adopt AI at a record pace, they also face the challenge of finding a smart and scalable way to manage its rapidly growing costs. This requires balancing the massive possibilities inherent in AI with the need to control cloud costs, aim for long-term profitability and optimize spending.

CloudZero's State of AI Costs in 2025 report examines how (and how much) companies are investing in AI — and whether they can confidently calculate the return on that investment (ROI). The report reveals a dynamic, volatile situation in which AI's budgets and momentum are growing quickly, as are organizations' expectations about its value. Yet these same companies are experiencing limited AI governance, misalignment and difficulty determining AI ROI. Now that cloud-based AI tools claim the biggest slice of the budget pie, attribution and cost visibility are crucial. Without them, organizations face the risk of unpredictable and unsustainable AI spend.

Some of the key takeaways our report found included:

  • AI spending is skyrocketing – This year, average monthly AI budgets will increase by 36%. That signals a big pivot toward larger, more complex AI initiatives.
  • Companies are struggling to evaluate AI ROI – Only 51% of companies definitively said they felt confident calculating the ROI of AI initiatives, largely due to an increasing visibility gap. Concurrently, cloud-based tools are dominant, making cloud cost visibility and attribution essential for optimizing AI ROI.
  • Unclear profitability remains a challenge – The most popular AI tools are designed for scalability, automation, and cloud deployment, yet their profitability remains unclear without effective cost tracking.

AI Spending on the Rise

Last year, organizations spent an average of $62,964 per month on AI. The 2025 report shows this amount will increase by 36% to $85,521. It's also noteworthy that the portion of companies planning to invest over $100,000 per month in AI tools will double — 40% this year compared to just 20% last year.

This significant spending uptick suggests that companies are increasing their AI projects to reap the benefits they promise. However, as their spend rises, businesses must ask this essential question: How confident are we about the ROI we're getting from our AI initiatives?

Prioritizing AI Explainability

This year's report revealed that 44% of respondents plan to invest in improving AI explainability. Their goals are to increase accountability and transparency in AI systems as well as to clarify how decisions are made so that AI models are more understandable to users. Juxtaposed with uncertainty around ROI, this statistic signals further disparity between organizations' usage of AI and accurate understanding of it.

In addition to explainability, businesses will prioritize AI robustness and security (41%), computing and cloud resources (39%), and improving customer experience (39%) this year. These priorities suggest a pivot toward AI deployments that are more scalable, transparent, and responsible.

Why Is It So Hard to Measure ROI?

Why is measuring AI's ROI still so hard for many businesses? The main reasons are:

  • Cloud expenses, maintenance, and other hidden costs
  • Difficulty separating the impact of AI from other business factors
  • Difficulty attributing AI costs to the right sources

Consequently, 49% of companies do not believe strongly in their AI ROI tracking. This speaks to a need for a stronger and more consistent cost attribution and tracking approach.

ROI Confidence Comes from Cost Optimization Tools

Of the companies that use third-party platforms, over 90% reported high awareness of AI-driven revenue. That awareness empowers them to confidently compare revenue and cost, leading to very reliable ROI calculations.

Conversely, companies that don't have a formal cost-tracking system have much less confidence that they can correctly determine the ROI of their AI initiatives. 41% of participants said they only "somewhat agree" about their ability to do this. These responses underscore the importance of implementing a formal and reliable cost-tracking system to evaluate ROI accurately.

Pairing AI Innovation with Cost Intelligence

Even the best-planned AI projects can become unexpectedly expensive if organizations lack effective cost governance. This report highlights the need for companies to not merely track AI spend but optimize it via real-time visibility, cost attribution, and useful insights. Cloud-based AI tools account for almost two-thirds of AI budgets, so cloud cost optimization is essential if companies want to stop overspending.

Cost is more than a metric; it's the most strategic measure of whether AI growth is sustainable. As companies implement better cost management practices and tools, they will be able to scale AI in a fiscally responsible way, confidently measure ROI, and prevent financial waste.

Methodology: This report is based on a survey conducted by CloudZero of 500 US software engineers, senior managers and above in organizations with 250 to 10,000 employees. The survey was conducted in March 2025. 

Bill Buckley is SVP of Engineering at CloudZero

Hot Topics

The Latest

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.