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.