AI Deployment Costs: What Enterprises Need to Know

According to recent PYMNTS Intelligence datathe cost of deploying AI is the second biggest drawback of generative AI adoption, with 46.7% citing it as a concern, following only integration complexity.

On paper, the cost of using today’s generative models is falling based on what AI companies are charging.

For example, OpenAI’s GPT-4 with an 8K context window had cost $30 per million input tokens and $60 per million output tokens as of early 2023. This year, GPT-4 Turbo, which is more powerful, nonetheless costs 50% to 67% less: $10 per million input tokens and $30 for the output.

According to Stanford’s 2025 Artificial Intelligence Index reportas AI models become more capable and smaller,the costs for applying them in use cases – inference – “have fallen anywhere from nine to 900 times per year,” the report said.

When it comes to infrastructure, costs have declined by 30% annually, while energy efficiency has improved by 40% each year, according to the Stanford report. Moreover, open-weight models that are free to use are closing the gap with closed models in performance.

But these headline numbers tell only part of the story.

Although the cost of the models has dropped since 2022, the overall cost of ownership “has been resistant to declines,” said Muath Juadyfounder of SearchQ.AI. “The real expenses lie in the hidden infrastructure, including data engineering teams, security compliance, constant model monitoring, and integration architects necessary to connect AI with existing systems.”

For every dollar spent on AI models, businesses are spending five to $10 to make the models “production-ready and enterprise-compliant,” Juady told PYMNTS. “The integration challenges tend to be more expensive than the technology itself and require substantial investment in change management and process redesign, which many organizations underestimate.”

Moreover, the cost of AI deployment “is not a one-time expense but an ongoing operational commitment,” Juady added.

So why is AI adoption soaring? Juady said, “businesses that are successfully adopting AI are not waiting for costs to drop further; they are identifying specific use cases where even current costs can provide a measurable ROI.”

Read also: High Impact,Big reward: Meet the GenAI-Focused CFO

Self-Hosting Can Lower Costs

For many enterprises,early decisions,such as whether to self-host,use the cloud or use third-party infrastructure,can dictate as much as 40% of AI expenses,said Pavel Bantsevichproject manager and solutions advisor at Pynest. Cloud-based hosting may be ideal for prototypes, but costs can spike as workloads scale.

Bantsevich said he worked with a U.S. construction company that’s been in business for a century to develop an AI predictive analytics tool and hosted it in the cloud. Infrastructure costs came to under $200 a month. But once it went live and people started using it, costs soared to around $10,000 a month. Switching to self-hosting using Meta’s open-source Llama model rather of the cloud lowered the cost to about $7,000 a month and has remained under control.

In another case, a European retailer client of Bantsevich’s with more than 50,000 employees wanted to implement a computer vision module for self-checkout machines. But the company didn’t want to use the cloud. It self-hosted rather using a small Llama AI model that performed well. Costs came to less than $10 a month per machine. “If a cloud solution had been selected, the numbers would have gone sky high,” he said.

Bantsevich believes that costs will continue to decline as datasets are more readily available today and cloud providers also have cut rates to retain customers. “It is indeed likely we shall see AI costs be similar to electricity bills in the near future,” he predicted.

Meanwhile, Bill Chief Financial Officer Rohini Jain advised businesses to take advantage of AI that is already embedded in the platforms they use, such as those for invoicing, payments or forecasting, rather than adding standalone tools with “uncertain” pricing.”Integrated solutions typically offer better ROI and more predictable costs, such as subscription pricing,” she said.

Fergal GlynnCMO and AI security advocate of Mindgard, said deploying AI can cost as little as $10,000 for basic projects, while large-scale enterprise systems can run into millions of dollars.Most companies spend between $50,000 and $500,000 for practical use cases like analytics tools or chatbots; smaller firms often pay less by using off-the-shelf AI.

Nicole Dinicolaglobal vice president of marketing at Smartcat, told PYMNTS that adopting AI doesn’t have to be “all or nothing.”

“Many platforms, including free or low-cost options, make it easy for organizations to start small and scale their adoption over time,” DiNicola said. “Unlike legacy SaaS, which frequently enough requires lengthy onboarding, upfront costs, and full-scale deployment to show value, AI can deliver meaningful impact without being fully integrated institution-wide.”

DiNicola pointed to teams embedding AI into workflows and already gaining efficiencies and cost savings. “AI tends to compound in value, but even small-scale adoption can drive clear and measurable improvements.”

A worse outcome would be letting the cost and complexity of AI scare a business into avoiding AI deployment in the first place.

“Inaction is often the more expensive path, even if it’s less obvious upfront,” DiNicola added. “While that delay might feel safe, early adopters are already building momentum, improving processes, learning faster, and expanding their competitive advantage.”

Read more:

How to Choose Between Deploying an AI Chatbot or Agent

Small Business, Big AI: How SMBs Are Leveling the Playing Field With Enterprise Giants

AI in Accounting Services May Level Playing Field for Small Businesses

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## The Hidden Costs of AI: Why Deployment Expenses Are Soaring

The integration of artificial intelligence (AI) into business operations is accelerating, but many enterprises are discovering that the true cost of deploying AI at scale extends far beyond the advertised per-token rates. While the cost of AI models themselves may be decreasing, the overall expenses associated with AI deployment remain stubbornly high.

According to recent PYMNTS Intelligence data, the cost of deploying AI is the second-biggest obstacle to generative AI adoption, with 46.7% of businesses citing it as a major concern, second only to integration complexity.

While the cost of using generative AI models appears to be falling, these figures don’t tell the whole story. For example, OpenAI’s GPT-4 Turbo costs significantly less than its predecessor, GPT-4.Similarly, a 2025 Stanford Artificial Intelligence Index report notes that the costs for applying AI models in various use cases have fallen dramatically. Infrastructure costs have also declined, and energy efficiency has improved.

however, Muath Juady, founder of SearchQ.AI, points out that the overall cost of AI ownership “has been resistant to declines.” He argues that the real expenses lie in the often-overlooked infrastructure, including data engineering teams, security compliance, model monitoring, and integration architects. Juady told PYMNTS that for every dollar spent on AI models, businesses spend five to $10 to make them “production-ready and enterprise-compliant.” He emphasized that integration challenges are frequently enough more expensive than the technology itself and require substantial investments in change management and process redesign.

Juady also noted that AI deployment is not a one-time expense but an ongoing operational commitment. Despite these costs, AI adoption continues to rise because businesses are identifying specific use cases where even current costs can provide a measurable return on investment (ROI).

### Self-Hosting Can Lower Costs

Pavel Bantsevich, project manager and solutions advisor at Pynest, suggests that early decisions about infrastructure, such as whether to self-host, use the cloud, or rely on third-party infrastructure, can dictate as much as 40% of AI expenses. While cloud-based hosting might potentially be suitable for prototypes, costs can escalate as workloads scale.

Bantsevich shared an example of a U.S. construction company that initially hosted an AI predictive analytics tool in the cloud, with infrastructure costs soaring from under $200 a month to around $10,000 once it went live. Switching to self-hosting using Meta’s open-source llama model reduced the cost to approximately $7,000 a month. In another case, a European retailer with over 50,000 employees implemented a computer vision module for self-checkout machines using self-hosting and a small Llama AI model, resulting in costs of less than $10 a month per machine.

Bantsevich believes that AI costs will continue to decline as datasets become more readily available and cloud providers cut rates. He predicts that AI costs may eventually resemble electricity bills.

Rohini Jain, CFO of Bill, advises businesses to leverage AI already embedded in existing platforms for invoicing, payments, or forecasting, rather than adding standalone tools with uncertain pricing. She suggests that integrated solutions typically offer better ROI and more predictable costs.

Fergal Glynn, CMO and AI security advocate of Mindgard, estimates that deploying AI can range from $10,000 for basic projects to millions of dollars for large-scale enterprise systems. He notes that most companies spend between $50,000 and $500,000 for practical use cases like analytics tools or chatbots, while smaller firms often pay less by using off-the-shelf AI.

Nicole Dinicola, global vice president of marketing at Smartcat, suggests that AI adoption doesn’t have to be an “all or nothing” approach. She points out that many platforms offer free or low-cost options that allow organizations to start small and scale their adoption over time. Dinicola emphasizes that even small-scale AI adoption can drive clear and measurable improvements.

dinicola warns that avoiding AI deployment altogether due to cost concerns can be a worse outcome. She argues that early adopters are already building momentum, improving processes, and expanding their competitive advantage.
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