Total Cost of AI Ownership

Many enterprises now recognize AI as an essential cost item rather than a mere experiment, but they still fail to properly grasp the Unit Economics that come with adopting AI technology. This can lead to serious financial risks as AI workflows scale.

AI costs are not determined simply by Token pricing alone. A token refers to a piece of text that a model reads and generates, arising from prompts, responses, retrieved documents, agent steps, and tool calls. However, true AI TCO (Total Cost of Ownership) goes beyond token pricing to mean the entire system cost of owning, operating, securing, and scaling AI.

As AI maturity increases, costs surge sharply due to factors such as:

• More Inference and use of longer Context Windows
• Agentic Steps and loops occurring to perform complex tasks
• Increased Retrieval and tool calls to improve accuracy

Therefore, enterprises must move away from a model of renting everything and instead adopt strategic discipline in deciding which AI capabilities to own directly and which to rent. Rather than simply looking at token pricing, they need to analyze, from a whole-system perspective, how many model calls and inference steps a single user request triggers.