Executive Summary

Many teams building products with embedded LLMs—engineering leads, product managers and FinOps owners—are now getting surprised by highly variable, pay-per-use API bills and have no reliable way to map those costs back to individual users, features or experiments. This makes chargebacks, ROI decisions and feature-level optimizations difficult for at least the estimated 500,000 software teams that could care, producing opaque monthly invoices and finger-pointing between finance and engineering. You could build a small-footprint solution that captures request-level metadata via lightweight SDKs and OpenTelemetry hooks, attributes token- and request-costs to user IDs and feature flags, reconciles that attribution with provider invoices, and exports chargeback-ready reports or integrates with existing billing systems. Key product elements would be per-user/per-feature dashboards, anomaly detection for cost spikes, privacy-preserving identifiers, and a low-overhead proxy or agent to support legacy apps; sampling and reconciliation logic would mitigate telemetry volume and ensure accuracy. This market is attractive now because LLM adoption is accelerating and pay-per-use pricing makes AI spend volatile; with an addressable market estimated at $6.0B (500k teams x $12K ACV), analysts’ signals (market score 90/100, revenue potential 84/100) and the maturation of observability tools, buyers are ready to pay for clarity. Finance and engineering are increasingly demanding FinOps for AI, and OpenTelemetry/SDKs make request-level capture inexpensive enough that a product-led go-to-market can scale. To stand out you must deliver demonstrably higher attribution accuracy and much lower overhead than generic observability tools, be provider-agnostic, and offer turnkey integrations and third-party attestation so finance teams will trust the numbers; the main challenges are instrumenting legacy stacks, preventing privacy leaks, and fending off eventual competition from cloud/LLM vendors that could build native attribution into their platforms.

Market Opportunity

Per-user AI API spend attribution — map requests to users/features for billing targets a $6.0B = 500k software teams x $12K ACV total addressable market with medium saturation and a year-over-year growth rate of 80% — driven by LLM adoption and observability spending.

Key trends driving demand: LLM adoption surge -- more products embed LLMs with pay-per-use pricing, increasing bill variability.; FinOps for AI -- finance and engineering teams demanding tools to allocate and optimize AI spend.; Observability maturation -- OpenTelemetry and lightweight SDKs make request-level capture inexpensive.; Usage-based pricing normalization -- per-token / per-call billing forces tighter per-feature visibility..

Key competitors include PromptLayer, OpenAI usage dashboard (and other LLM vendors' dashboards), Datadog (APM & logging), Kubecost, Apptio / Cloudability (FinOps and cloud cost management).

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Per-user AI API spend attribution — map requests to users/features for billing

Executive Summary

Market Validation

Market Opportunity

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