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

Date:
Fri, 14 Aug 2026 09:57:27 +0000

Description:
Saving on token costs without factoring in risk can be a fatal step.

FULL STORY ======================================================================Copy link Facebook X Whatsapp Reddit Pinterest Flipboard Threads Email Share this article 0 Join the conversation Follow us Add us as a preferred source on Google Newsletter Subscribe to our newsletter Agentic AIs 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 From TechRadar
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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. Its 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 were 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. You may like Beyond Tokenmaxxing: the rising token tax on enterprise AI Tokenomics: AI Has an income statement - its time to read it What is Tokenmaxxing, and why
should businesses care about it?

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. Are you a pro? Subscribe to our newsletter Sign up to the TechRadar Pro newsletter to get all the top news, opinion, features and guidance your business needs to succeed! Contact me with news and offers from other Future brands Receive email from us on behalf of our trusted partners or sponsors By submitting your information you agree to the Terms & Conditions and Privacy Policy and are aged 16 or over.

About two years after OpenAI released ChatGPT, the Chinese company DeepSeek shook up the AI market with the release of a powerful, open-weighted large language model whose training parameters were publicly available, allowing users to customize the model and build on the cheap compared with other generative AI models.

ChatGPT-maker OpenAI and other AI companies started doing the same, and suddenly, the costs of using GenAI systems dropped off a cliff. In fact, prices fell faster for GenAI than for any other technology in history.

The emergence of agentic AI has introduced some stealth costs into the equation, however. The costs of agentic software range from free for open-source models to enterprise agents, with prices that vary from one-time fees (roughly $15,000 for basic models to more than $1 million for global enterprise models) to monthly subscriptions (which can range from a few thousand to $13,000 or more). What to read next Token maxxing is your AI programs quiet failure mode The hidden tax on your AI ambitions How to
embrace the spirit of Tokenmaxxing without breaking the bank

But those costs are fixed. Inference costs are another story: they scale with usage and can amount to 90% of AI lifecycle costs.

Tokens come into play when an AI agent requests processing from GenAI models, which charge agents for processing information. At a glance, the costs may appear inconsequential. Input tokens generally range from 15 cents to $5 per million requests. Output tokens, which require slightly more processing, cost from about 60 cents to $25 per million.

They may start small, but can add up in no time, thanks to AI agents that
work very quickly, autonomously, and unpredictably. They are designed to interact with systems and other agents throughout the enterprise. A single action might generate scores of LLM calls. Token use, which has grown exponentially with the use of AI agents, has already increased IT budgets by about 20% according to recent estimates.

The accelerating cost of agentic AI is prompting CISOs to look for ways to save money where they can, and one way is to identify LLMs that charge the least per token. But what they may not be considering are the risk factors associated with those LLMs. If CISOs concern themselves only with the costs, they may open themselves up to security risks.

But better security doesnt necessarily have to cost more. Depending on what theyre using agentic AI for, they may find they dont always have to trade security for lower token costs. Getting costs (and risks) under control There are a few things organizations can do to help stop token costs from getting out of hand, including:

Match Agents and LLMs to the Job at Hand. Commodity AI systems can cost
little or nothing, but they lack the deep reasoning for complex security synthesis. But not every application or function within the organization requires a reasoning engine. You can set up agents to work with low-cost LLMs on low-risk projects, while preserving higher-cost LLMs for critical tasks. Its also worth being aware of which agents are likely to request more LLM calls.

Factor Risk Scores in Choosing Agents and LLMs. The security implications of using AI cant be ignored. When developing a budget plan, include risk
factors.

Monitor Workflows. Keeping a close watch on workflows can help you track
costs and performance, allowing you to better understand which tools work
best in which situations.

Lean on Human Oversight. Despite agentic AIs autonomy, in fact, because of agentic AIs autonomy, forgetting about the importance of the human element is risky business. Teams need thorough upskilling in secure development, with clearly defined ownership roles. And they must be given prominent oversight roles throughout the ADLC.

Agentic AI is fast becoming integral to enterprise operations, and organizations must control its associated costs. But a race to the bottom on token pricing creates hidden technical debt. Instead, CISOs need to weigh security performance when choosing AI tools as part of establishing an up-to-date security maturity model and an AI governance policy that
emphasizes performance, costs, and risk management .

Only that approach allows agentic AI to be deployed without either breaking the budget or putting your entire organization at risk. We've featured the best AI website builder. This article was produced as part of TechRadar Pro Perspectives , our channel to feature the best and brightest minds in the technology industry today.

The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc. If you are interested in contributing find out more here: https://www.techradar.com/pro/perspectives-how-to-submit



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