aiqbench analysis
This article focuses on enterprise observability architectures for managing multi-provider AI spend, specifically addressing the challenge of attributing costs to specific users and teams within large organizations like Cisco. While it discusses tokenomics as a mechanism for financial control, the core subject is the operationalization of AI infrastructure metrics rather than novel algorithms or quantum principles.
Beyond AI cost visibility: Designing tokenomics for real users

Managing AI spend at scale demands more than just usage counts. Learn how mapping tokenomics to organizational hierarchies empowers engineers, managers, and finance to balance innovation with financial discipline.
Pratyusha Duvvuri is a Technical Lead on Cisco’s AI Engineering team, designing tokenomics and observability architectures that help enterprise teams scale AI usage with clarity and accountability. In the previous blog, we showed how tokenomics became essential to managing AI spend at scale. Here’s how we operationalized it by building for the humans who actually use it. As AI adoption accelerated across Cisco, our spend visibility became critical. We needed a clearer view of where spend was going and who was driving AI adoption with finance control, budget accountability, and fewer surprises as usage grew. Employees were trying new coding tools, teams were finding new ways to use them, and costs were growing across several providers. We could see budgets being consumed quickly, but we could not see the full story behind that spend. This is why tokenomics is a key priority for us at Cisco, and why being customer zero for Splunk Agent Observability is so impactful. To…
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