A reference from Weave

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Track the economics of AI usage, from token accounting to cost per completed task. Connect spending to engineering outcomes rather than treating usage as value.

Terms beginning with M

5 terms

  • Marginal AI cost

    Marginal AI cost is the additional expense caused by one more request, token, user, or workflow. It gives teams a way to name, measure, or reason about an economic property of an AI workload without treating raw usage as proof of value.

    Token costs and AI ROI

  • Model fallback cost

    Model fallback cost is extra expense when a primary model fails and another route serves work. It gives teams a way to name, measure, or reason about an economic property of an AI workload without treating raw usage as proof of value.

    Token costs and AI ROI

  • Model mix

    Model mix is the distribution of workload across models, providers, or deployment tiers. It gives teams a way to name, measure, or reason about an economic property of an AI workload without treating raw usage as proof of value.

    Token costs and AI ROI

  • Model routing savings

    Model routing savings is spend reduction from sending work to a suitable efficient route. It gives teams a way to name, measure, or reason about an economic property of an AI workload without treating raw usage as proof of value.

    Token costs and AI ROI

  • Monthly recurring AI spend

    Monthly recurring AI spend is the recurring portion of monthly AI expense for ongoing workloads. It gives teams a way to name, measure, or reason about an economic property of an AI workload without treating raw usage as proof of value.

    Token costs and AI ROI