Executive Summary

Many B2B product teams embedding LLMs lack reliable per-customer cost attribution: they either use coarse proxies that misallocate spend or force traffic through vendor-run proxies that raise privacy and vendor-risk concerns. This problem is acute across the estimated 2M businesses in the $6.0B addressable market (assumed $3K ACV), where teams need accurate per-customer profitability and model-selection signals to avoid subsidizing heavy users. You could build a non-proxy solution consisting of lightweight SDKs for client and server runtimes that emit signed, per-request cost receipts based on local token accounting, combined with a server-side collector that aggregates encrypted tallies without ingesting raw prompts. Product features would include verifiable receipts, billing system connectors, model-routing APIs to enforce cost SLAs, and dashboards; target accuracy would be 95–99% with a small latency overhead (target <5%), though streaming and multi-model chains increase engineering complexity. The market is attractive now because rising LLM API costs and usage-based pricing make per-customer cost visibility a pressing economic need, while growing scrutiny around data flows and vendor risk makes solutions that keep raw text under customer control more saleable. With a $6B TAM and clear cost-recovery imperatives, early customers will be mid-market SaaS and regulated verticals where a handful of heavy users can materially affect margins. To stand out you must prove the non-proxy promise: open SDKs, auditable cryptographic receipts, and turnkey billing integrations that preserve privacy while delivering near-perfect attribution. The honest challenges are significant—engineering for streaming, cryptographic correctness, and earning trust from security teams—but a successful technical proof and a dozen pilots in target verticals would create a defensible product-led GTM with medium competition and high revenue potential.

Executive Summary

Market Opportunity

Attributing LLM cost to each customer without forcing a proxy targets a $6.0B = 2M businesses × $3K ACV total addressable market with medium saturation and a year-over-year growth rate of 40% YoY based on rapid LLM adoption and growth in AI developer tools (industry synthesis of market reports and developer surveys).

Key trends driving demand: Trend — Developers are embedding LLMs across B2B products, creating a fast-growing need for cost observability and per-customer billing.; Trend — Increased scrutiny around data flows and vendor risk makes non-proxy solutions that keep raw data in customer control more attractive.; Trend — Rising LLM API costs and usage-based billing models are pushing product teams to track per-customer profitability and optimize model selection.; Trend — Better edge and serverless tooling plus SDKs make lightweight request tagging and server-side correlation feasible without major infra changes..

Key competitors include PromptLayer, LangSmith, OpenAI Cost Insights / Cloud Provider Dashboards.

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Attributing LLM cost to each customer without forcing a proxy

Executive Summary

Attributing LLM cost to each customer without forcing a proxy

Executive Summary

Market Validation

Market Opportunity

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