Put a Cost Budget Around Every AI Feature

AI applications often select models using quality benchmarks. Production systems also need an economic constraint.

A feature that works technically can still become unsustainable when usage grows.

Define a feature budget

interface AIFeatureBudget {

feature: string;

maximumCostPerRequest: number;

maximumLatencyMs: number;

minimumQualityScore: number;

}

The budget belongs to the product feature rather than the provider.

const supportReplyBudget: AIFeatureBudget = {

feature: "support-reply",

maximumCostPerRequest: 0.02,

maximumLatencyMs: 2500,

minimumQualityScore: 0.85

};

Estimate before execution

interface ModelCandidate {

model: string;

estimatedCost: number;

estimatedLatency: number;

qualityScore: number;

}

function eligibleModels(

candidates: ModelCandidate[],

budget: AIFeatureBudget

) {

return candidates.filter(candidate =>

candidate.estimatedCost <= budget.maximumCostPerRequest &&

candidate.estimatedLatency <= budget.maximumLatencyMs &&

candidate.qualityScore >= budget.minimumQualityScore

);

}

The cheapest model should not automatically win. A failed or unusable result may cost more once retries, support work and customer churn are considered.

Record actual economics

interface FeatureUsageEvent {

feature: string;

customerId: string;

model: string;

inputTokens: number;

outputTokens: number;

actualCost: number;

latencyMs: number;

successful: boolean;

}

These events allow teams to compare estimated and actual costs, identify expensive workflows and calculate cost per successful task.

VectorNode is developing this AI economics layer for model-powered products: infrastructure that connects model usage with product and cost decisions.

A model request is a technical event.

Its cost is a business event.