How to Build a Usage Ledger for AI Features

Model invoices show what a provider charged. They do not necessarily show which product feature created the cost or whether that cost produced business value. An AI application needs its own usage ledger. The usage event Every model request should generate a normalized event. interface IntelligenceUsageEvent { requestId: string; timestamp: string; workspaceId: string; customerId: string; feature: string; task: string; provider: string; model: string; inputTokens: number; outputTokens: number; latencyMs: number; estimatedCostUsd: number; succeeded: boolean; } This creates a consistent record even when different providers return different usage formats. Measure costs by feature Provider-level totals are useful for accounting, but feature-level totals are more useful for product decisions. SELECT feature, COUNT(*) AS requests, SUM(estimated_cost_usd) AS total_cost, AVG(latency_ms) AS average_latency FROM intelligence_usage GROUP BY feature; Teams can now answer practical questions: Which feature consumes the most intelligence? What is the AI cost per customer? Which models create the best cost-to-quality balance? Where should a lower-cost model be introduced? Which failures require a fallback policy? Tokens alone are not a complete business metric. They need to be connected to features, customers, outcomes and revenue. VectorNode’s new market role is the programmable intelligence utility for AI products: delivering model capabilities while making their consumption measurable and manageable. Model access starts the request. A usage ledger turns that request into an operable business resource.