Beware the token trap: Why saving on inference might put your ADLC at risk
Saving on token costs without factoring in risk can be a fatal step.
Agentic AI models, while cost-effective upfront, can generate significant hidden expenses for organizations through token usage. Organizations must factor in security risks to avoid jeopardizing their Agentic Development Lifecycle (ADLC). The costs of using agentic software vary widely, with inference costs accounting for up to 90% of AI lifecycle expenses.
Token costs may appear insignificant individually, but they can quickly accumulate due to the rapid, autonomous, and unpredictable actions of AI agents. To control these costs and risks, organizations should match agents and Large Language Models (LLMs) to specific tasks, factor risk scores into AI tool selection, monitor workflows, and ensure human oversight throughout the ADLC.
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