While enterprise AI spending remains relatively modest today, the potential for overspending is significant. Most organizations are still experimenting, with only a few production-ready use cases. But that’s about to change. Over the next two to three years, AI investment is expected to grow exponentially as enterprises scale their efforts to operationalize AI.
One major cost driver is the shift to large-scale generative AI (genAI) models, which require up to 100 times more compute than traditional AI models. And compute is just one lever. GenAI costs span both traditional infrastructure — like data, databases, storage, and networking — and AI-specific workloads such as model selection, token usage, training, and inferencing.
These new cost levers add complexity, but they’re only part of the equation.
GenAI Isn’t Traditional Software
Developing genAI and agentic AI systems is fundamentally different from traditional software development. These systems are probabilistic — meaning outputs can vary even with the same input. In black-box AI services, pricing structures can change without notice or transparency. Margins are dynamic and unpredictable, making cost management — and forecasting — especially challenging.
Still, every AI use case includes standard levers that can be tuned to optimize spend and manage the delicate balance between cost, performance, and risk.
Understanding AI Cost Categories
AI costs generally fall into two categories:
Direct costs. These include models, data, and infrastructure — the core technologies needed to build and run AI solutions.
Operational costs. These cover the overhead of running AI at scale, such as governance, business transformation, and skills development.
Each category involves trade-offs. Here are a few key levers for consideration:
Choosing the right model is the quickest way to balance performance and cost. Mature organizations regularly evaluate and swap models, as model quantity and processing profiles can significantly impact expenses.
Data is often the largest cost driver, with AI workloads doubling storage needs. Agentic systems generate vast logs and metadata. Optimize by using efficient formats, compression, tiered storage, and eliminating redundant or abandoned data.
Infrastructure choices affect both costs and performance. Cloud offers flexibility and access to GPUs but comes with less predictable costs, and on-premises provides predictability but high up-front investment. Workload placement should also factor in latency, performance, and data sovereignty.
The Bottom Line
As genAI adoption scales, so will costs — often exponentially. GenAI introduces new cost levers and operational complexities that differ fundamentally from traditional software. Staying ahead requires continuous fine-tuning of your AI cost levers: models, data, infrastructure, and operations.
Want to learn more? Check out our report, AI Cost Optimization: The Why, What, And How.
Need tailored guidance? Speak with our analysts: Michele Goetz (AI/data), Tracy Woo (FinOps), or Charlie Dai (AI cloud).
