Cost Drivers for AI

As AI transforms the technology landscape, it introduces new cost and investment considerations for organizations.  From the variable expenses of serverless services to the risks of on-premises infrastructure investments, AI-related costs follow traditional IT service models but come with unique challenges. 

Effectively understanding, communicating and controlling AI costs and value is essential for financial and strategic decision-making.  Mastering the following key cost drivers will help determine whether an AI initiative will deliver measurable business impact and positively affect the organization’s bottom line.

Serverless AI Services

A serverless AI service integrates directly into an application or webpage workflow, with costs based on service usage rather than underlying compute or storage expenses.   As of this writing, AWS, Azure, NVidia, and Google each offer more than a dozen such services, covering functionalities from vision recognition (is there a cat in this picture?) to video editing, transcription, document intelligence, speech recognition and translation.  As AI adoption grows, so will the diversity of these offerings, driven by increasingly creative use cases.

By reducing capital expenditures and scaling costs directly with usage, serverless AI provides an agile, cost-effective alternative to traditional AI training and hosting models. While off-the-shelf AI models offer cost savings, they lack the differentiation of custom-built solutions, which can provide a competitive edge. Conducting a cost-benefit analysis early – incorporating both business and IT perspectives – is critical for aligning AI investments with strategic goals.

AI Cost Drivers in Model Training

For ITFM / TBM analysts, understanding AI training costs is essential, as up to 80-90% of an application's total compute expenses may be incurred before it even reaches production. In some cases, this upfront investment is too high to yield a viable return on investment, driving increased reliance on serverless AI services from major cloud providers and hardware vendors like NVidia. 

For IT Finance, FinOps Practitioners and Application Architects, managing these services presents a unique challenge. Costs for out-of-the-box AI services are typically transactional (e.g., per 1k words, per image, per audio hour, etc.), while customized AI models introduce a mix of transactional and compute-based costs. Most organizations will need some level of customization to maintain a competitive advantage.  

One key challenge is that AI service consumption metrics rarely align with business value metrics (e.g., cost per sale, cost per manufactured product, or cost per customer). IT finance teams must develop cost models that translate AI cost inputs into meaningful business value metrics to enable accurate financial planning and decision-making.

Managing AI Costs and Business Value

Generative AI introduces many familiar cost management principles for ITFM practitioners. As serverless AI adoption expands, organizations must build resilient cost models that bridge the gap between granular service consumption rates and broader business value metrics. Effective management of AI costs requires balancing price versus quantity, either by reducing rates or optimizing resource consumption.

Pricing structures vary significantly between cloud providers and even between different services from the same provider, leading to large fluctuations in Generative AI spend. Understanding the business cases before launching a Generative AI initiative is crucial, as the choice of AI model – whether a small, cost-efficient model or a large, complex model requiring advanced data science – directly impacts cost, quality, and value.