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

Related Links

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The Real Enterprise AI Risk Isn't Hallucinations

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The Corporate Network Is Fading - Here's What Replaces It

For decades, enterprise networks were designed around a simple assumption: work happened inside the office. Applications lived in centralized data centers, employees connected through internal infrastructure, and security focused on protecting the perimeter that surrounded everything ... But the way organizations operate today bears little resemblance to that environment. Cloud platforms host critical applications, employees connect from homes and airports as often as they do from offices, and partners collaborate through shared systems that exist far beyond corporate walls. In short, the corporate network no longer resembles the environment it was designed to protect ...

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Enterprises operate different combinations of workloads across cloud, hybrid and multicloud environments. For business-critical workloads, teams need to consider monitoring and observability early so they can detect health issues, investigate failures, and understand operational impact. Organizations place workloads on cloud platforms based on a combination of technical requirements, economics, existing dependencies, organizational standards, and business priorities. Their monitoring priorities therefore depend on what they operate and where those systems run. Those priorities will not look the same for every organization ...

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