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Executive Summary

Enterprises and platform teams are facing an emerging blind spot: as LLM usage scales, token call volumes and API charges are becoming a material operational cost and an audit/compliance risk across 2.0M software and AI-using organizations. Finance, platform engineering and security/compliance owners lack reliable, model- and user-level attribution for LLM calls, and they cannot consistently enforce runtime access controls or produce audit trails when models are invoked by microservices, agents or third-party integrations. You could build a runtime meter and policy enforcement layer for AI stacks that sits in the LLM callpath (as a proxy/agent + optional on-prem collector), attributes spend and risk to user, app, model and dataset, produces immutable audit logs, and enforces throttles or blocks based on policies. Deliver this as a SaaS with an ACV-oriented go-to-market motion (~$6.0K ACV per organization target) and integrations into IAM, SIEM, and observability pipelines so security and finance teams get cost and compliance signals in their existing tools. The timing is favorable: the AI-usage explosion makes LLM spend visible and pressure on budgets immediate, enterprises are increasingly demanding governance and model-level accountability, and LLM vendors are exposing richer telemetry and hooks that make fine-grained attribution technically possible. That combination produces a $12.0B addressable market now — strong demand, growing regulatory scrutiny, and the technical means to instrument calls and enforce policies. To stand out you’ll need crisp product differentiation around precise spend attribution, low-latency runtime controls, and enterprise-grade auditability rather than generic cost dashboards, plus deep integrations into cloud identity and observability ecosystems. The honest challenges are significant: instrumenting heterogeneous stacks and serverless patterns, handling PII/privacy and encryption concerns, and competing with cloud providers and large observability vendors, so focus on quick time-to-value integrations, measurable cost-savings/risk-reductions, and reference customers in security/compliance to accelerate adoption.

Executive Summary

Unseen LLM spend & secret risk — runtime meter for AI stacks

Executive Summary

Unseen LLM spend & secret risk — runtime meter for AI stacks

Executive Summary

Market Validation

Market Opportunity

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More in Security & Compliance

Secure sensitive AI data flows with a low‑latency privacy gateway

Privacy-first AI triage for NDAs and confidential contracts at the edge

Secure model control planes — continuous audits + runtime enforcement

Ongoing security costs — prioritize & automate continuous remediation

Reduce compliance risk with AI-driven inspections, audits, and quality workflows

AI agents need enforceable stop‑signs — runtime safety controls