Prevent AI-driven revenue leakage with product-aware metering

Many product teams, FinOps groups, and engineering managers at AI-enabled product organizations face hidden AI-driven revenue leakage: per-token and per-inference costs rise while usage is poorly attributed to product features, multi-provider model usage fragments billing, and teams lack the telemetry to correlate AI spend with customer value. The result is inaccurate customer billing, missed chargebacks, and margin erosion that can be material for organizations at scale. You could build a product-aware metering platform that is model-agnostic and maps every model call (tokens, latency, output size) to product features and customer identities via lightweight SDKs and sidecar integrations, with out-of-the-box connectors to observability systems and billing pipes. The product would provide high-fidelity chargeback, real-time cost alerts, and reconciliation tools, sold as a SaaS with a target ACV of roughly $23K to the estimated 1,000,000 AI-enabled product organizations implied by a $23.0B addressable market. This market is attractive now because per-inference costs are increasing, customers are adopting multiple model providers, and teams want unified telemetry that correlates AI costs with product events—conditions that increase urgency and willingness to pay. The idea can stand out by focusing on product-level attribution (not just infra metrics), vendor-agnostic accuracy, and deep billing integrations, but you should expect medium competition, significant integration complexity across model providers and enterprise billing systems, and the need to prove token-level accuracy and privacy safeguards before large enterprises will adopt.