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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

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...