AI At Scale Changes The Infrastructure Equation

Bob Venero is the CEO & Founder of Future Tech Enterprise, Inc., an award-winning, global IT solutions provider.

As artificial intelligence (AI) has largely gone from experimentation to everyday business use, cost has become a central topic. Companies are identifying use cases that work and beginning to think about what it will take to scale them across their organizations.

That’s when the economics can start to look very different.

A company may begin with a handful of people using a cloud-based model and see a relatively manageable expense. As more employees begin using it, more use cases move into production and AI becomes part of day-to-day operations, token consumption grows along with it.

That growth raises important questions about the architecture supporting AI. Where models run, where data resides and how frequently those resources are accessed can have a significant impact on cost. They can also affect speed and availability, along with how easily an organization can scale the use cases that are creating real business value.

For companies planning to expand their use of AI, these are decisions worth making early.

Token Costs Add Up Quickly

Tokenization is a big part of the conversation right now. Every time an organization uses a cloud-based AI model, tokens are being consumed, and those costs become much more significant as usage grows.

For companies planning to use AI broadly across the enterprise, this deserves attention from the beginning. The cost of the infrastructure required to run AI on-prem can be a fraction of the token expense associated with running large volumes of AI workloads in the cloud. At enough scale, those ongoing costs can affect how much AI the organization can realistically use and how quickly it can expand successful applications.