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.