Find a term
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.
Search results
80 termsEffective AI rate
Effective AI rate is the realized cost per unit after discounts, caching, and routing adjustments. 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 ROIFixed versus variable AI costs
Fixed versus variable AI costs is the distinction between stable expenses and usage-scaled expenses. 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 ROIInference unit cost
Inference unit cost is the expense of one defined model-serving unit such as a request 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 ROIInput token
An input token is a unit of text or other encoded content sent to a language model before generation. The prompt, system instructions, conversation history, retrieved passages, and tool results can all contribute input tokens.
Token costs and AI ROIInput-output token mix
Input-output token mix is the proportion of input and output tokens in a workload. 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 ROILatency-adjusted AI cost
Latency-adjusted AI cost is a cost comparison that accounts for the operational impact of response time. 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 ROIMarginal 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 ROIModel 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 ROIModel 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 ROIModel 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 ROIMonthly 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 ROIOutput token
An output token is a unit generated by a language model in its response. Output tokens include visible text and, depending on the API, structured fields or reasoning content returned for the application to process.
Token costs and AI ROIOutput verbosity cost
Output verbosity cost is expense associated with generating longer model responses. 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 ROIPrompt cache savings
Prompt cache savings is cost avoided when repeated context is served from an eligible cache. 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 ROIPrompt overhead
Prompt overhead is request context supporting execution rather than the primary user payload. 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 ROIQuality-adjusted AI cost
Quality-adjusted AI cost is effective expense after accounting for quality or acceptance. 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 ROISpend attribution
Spend attribution is the mapping of AI spend to a product, team, workflow, user, or outcome. 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 ROIToken budget
A token budget is a configured limit or allowance for the tokens an AI system may process or generate during a request, task, or accounting period. The exact scope can refer to output length, context capacity, spending, or a workflow's total usage.
Token costs and AI ROIToken budget utilization
Token budget utilization is the percentage of a token budget consumed in a period. 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 ROIToken budget variance
Token budget variance is the difference between planned token consumption and actual consumption. 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 ROIToken forecast
Token forecast is an estimate of future token consumption from workload history and planned demand. 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 ROIToken ledger
Token ledger is a durable accounting record of token usage and explanatory dimensions. 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 ROIToken meter
Token meter is a mechanism that records token usage for requests or workflows. 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 ROIToken overrun
Token overrun is usage that exceeds a configured allowance or expected request envelope. 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 ROIToken quota
Token quota is a token allowance assigned to a user, team, application, or account. 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 ROIToken rate
Token rate is the number of tokens processed per unit of time. 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 ROIToken utilization
Token utilization is how much of an available token allowance a workload consumes. 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 ROIToken volume
Token volume is the amount of input and output text processed by an AI workload. 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 ROIUncached token
Uncached token is an input token processed without a reusable cache hit. 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 ROIWasted tokens
Wasted tokens is tokens consumed without contributing to an accepted outcome or necessary operation. 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