ModelCost was born from watching AI costs spiral, enterprise deals stall over compliance gaps, and realizing no tool existed to solve both problems at once.
I was working full-time at a company that had gone all-in on AI. We were shipping LLM-powered features across multiple products, calling models from OpenAI, Anthropic, and others — sometimes millions of requests a week. The technology was incredible. The bills were terrifying.
The worst part wasn’t the spend itself — it was the complete lack of visibility. We had no idea which teams were driving costs, which models were being over-provisioned, or whether our usage patterns even made sense. Finance would flag a surprise invoice, and engineering would spend days trying to trace it back to a specific service or feature.
But cost was only half the story. We had enterprise customers asking us to guarantee that their data wasn’t being sent to third-party AI providers in ways that violated their compliance requirements. We couldn’t make that guarantee confidently. We had no centralized way to audit what was being sent where, flag PII in prompts, or enforce data governance policies across our AI integrations.
Deals stalled. Revenue was left on the table. Not because our product wasn’t good enough, but because we couldn’t prove we were handling data responsibly. Enterprise buyers wanted zero-trust guarantees: no prompt data leaving their environment, anonymized user identities, and a full audit trail for every access.
I looked for off-the-shelf tools — something that could give us a cost dashboard, budget enforcement, anomaly detection, and data governance in one place. I couldn’t find anything that met our needs. The observability tools covered latency and errors but ignored cost. The FinOps platforms understood cloud infrastructure but had no concept of per-token pricing or model-level attribution. And nothing touched data governance for AI workloads.
ModelCost started as an internal tool, a lightweight SDK that intercepted AI API calls, captured cost and usage metadata, and piped it into a dashboard. No prompt content, no completion data, just the metadata needed to understand what was happening and how much it cost.
It worked. Within the first week, we found a rogue batch job that was burning through tokens at 10x the expected rate. Within the first month, we had per-customer cost attribution that let us price our AI features profitably. And we finally had the audit trail to show enterprise buyers that their data was being handled according to policy.
Later we added four layers of privacy hardening: metadata-only storage by default, role-based access for violation details, user anonymization with per-org salts, and ephemeral snippets with auto-expiry. Zero trust, built in.
I realized that every team shipping AI to production was fighting the same battles. So I turned it into a product.
Built for every team shipping AI to production that needs to see every dollar and control every call.