Why predictability, not lower token prices, is the key to enterprise AI ROI
Enterprises cannot afford to wait for certainty with AI; those that do will fall behind. But exploring a new technology should never mean losing control of cost and risk. A well-established principle of entrepreneurship makes this clear: act within an affordable loss, committing only what you can afford to lose while you learn.
Apply this to AI. ROI is return divided by investment. If the investment cannot be forecast with confidence, neither can the ROI.
The token: AI’s unit of currency
A token is the basic unit of text a language model reads and writes, roughly four characters or three-quarters of an English word (other languages often need more). It has also become the currency of GenAI: cloud providers charge per million tokens, with output tokens typically several times more expensive than input.
AI tokenomics is the economics of AI consumption: how token usage translates into cost, how that consumption scales with adoption, and ultimately how AI expenditure translates into business value. It is also where enterprise AI budgets come unstuck.
Why agentic AI breaks the budget
Agentic AI changes the economics because one business task no longer corresponds to one model call. An agent may plan, retrieve context, call tools, invoke other models, validate results and retry before completing a task. Token consumption therefore depends not only on the number of users, but on the complexity and behaviour of the workflow itself. So, the same task can consume a different number of tokens every time it runs, and consumption grows with adoption.
The usual fixes, such as capping tokens per user or routing requests to cheaper models, treat the symptoms. They trade value for control, and the bill stays open-ended.
Cheaper tokens are not the answer
Some AI offerings respond to this problem by reducing the price per token. But cheaper tokens do not fundamentally change the economic model: a lower price on an unpredictable quantity is still unpredictable. To sign off on agentic AI, CFOs need more than a smaller variable bill; they need a predictable and governable cost model.
Nor are frontier models always necessary. Smaller and specialized open models, combined with enterprise context, retrieval, tools and well-designed workflows, can provide the required performance for a growing range of use cases.
Zero per-token fees
This is the thinking behind GaiaB™ by AUSTRIACARD: a sovereign enterprise AI platform designed to run open models within the enterprise perimeter, including in fully air-gapped environments, ensuring your data stays within your perimeter without per-token inference fees.
Instead of paying for each token consumed, enterprises invest in dedicated AI capacity. There are no per-token inference fees, infrastructure costs are predictable and capacity can be planned against expected workloads.
This does not make AI computation free. It changes its economics: from an open-ended consumption model to infrastructure-based economics that enterprises can own, govern and scale. With GaiaB, the investment is bounded and knowable, allowing enterprises to experiment and scale within a defined cost envelope.
Take back control of the “I”, and ROI becomes something you can actually calculate.
Meet us at Web Summit Lisbon
- “The Token Trap: Precision Over Power with GaiaB” – Corporate Innovation Stage, 10 November, 16:20
- Masterclass: “AI Sovereignty: Data Control, Governance and Predictable AI Costs” – AI Stage, 11 November, 11:25