Beware the token trap: Why saving on inference might put your ADLC at risk

Token use can create unexpected, sizeable costs for organizations

by · TechRadar

Opinion By Pieter Danhieux Published 14 August 2026

(Image credit: Blue Planet Studio/Shutterstock)

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Agentic AI’s prolific use of tokens can create sizeable, unexpected costs for organizations. But saving on token costs without factoring in risk can be a fatal step.

As upfront prices for flagship artificial intelligence models continue to shrink, organizations have begun to wise up to the hidden costs they encounter with agentic AI models.

Specifically, the costs of tokens, which may look tiny when viewed as individual charges, can add up exponentially as AI agents become more active, leaving organizations with hefty AI expenditures they may not have anticipated.

Latest Videos FromTechRadarWatch full video here: Pieter Danhieux

Co-Founder and CEO of Secure Code Warrior.

This is putting CISOs in something of a bind. If they seek to save money on inference costs, primarily driven by token generation incurred by agentic AI, they may increase their security risk and accumulate hidden technical debt that puts their Agentic Development Lifecycle (ADLC) in jeopardy. It’s a problem that many CISOs may not have factored into their security budgets, but it cannot be left unaddressed.

The effectiveness of automated security processes is being impeded by fragmented pricing across the AI landscape, whether we’re talking about hyper-optimized nano models (essentially lightweight, yet powerful models built for a specific use, like Google’s Nano Banana 2 image generator) or premium reasoning engines, like Salesforce Atlas or OpenAI o3.

Organizations do have to keep a close eye on token costs to prevent them from spiraling, but CISOs also need to examine how agentic AI is affecting their security.

The hidden costs of AI agents

Erratic pricing has been a trademark of generative AI pretty much from the beginning.

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