Canyon Code argues $/workflow as key enterprise AI metric

TL;DR. Canyon Code proposes using dollar-per-workflow as the critical metric for enterprise AI efficiency, moving beyond traditional cost-per-token. - Enterprises often mismanage AI spending due to inadequate tracking based on token costs. - $/workflow offers better visibility into specific agentic applications driving expenses and value. - The methodology also accounts for hidden GPU infrastructure waste from poor workflow scheduling. - This approach enables more holistic optimization of AI compute resources and business outcomes.

• Enterprise AI cost tracking is often flawed by relying on $/token, which aggregates all workflow types.
• Canyon Code advocates for $/workflow as a more precise metric, attributing costs to specific agentic applications.
• This new metric reveals hidden inefficiencies in GPU infrastructure, where poor scheduling wastes significant compute cycles.
• Optimizing at the $/workflow level allows businesses to identify profitable AI investments and reduce waste.

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