Tokenomy.ai

Tokenomy.ai runs an OpenAI-compatible proxy that teams reach by swapping one base URL. With bring-your-own-keys, provider spend stays on the customer's own accounts, so there's no migration and no reseller markup on top. Every request lands in a real ledger rather than a sampled estimate.

Budgets are declarative, with hard and soft caps plus anomaly detection on a z-score basis. When a quota is hit, the proxy returns an HTTP 402 instead of forwarding the call. Circuit breakers are wired the same way, which is how an unexpected agent loop gets stopped mid-run rather than discovered at month end.

The router applies quality and latency thresholds that the user defines, then picks the cheapest model that still passes. Response caching with measured hit ROI sits beside it, and the Model Right-Sizing Lab sweeps an actual workload across models to find the lowest-cost plan that keeps its quality bar.

Attribution reaches agent, step, tool call, product, customer and environment, and even a request hash. That granularity comes from sitting in the request path, because the context that ties spend to a specific agent never leaves the application otherwise. Per-run breakdowns show which retry policy or context payload is doing the damage.

Model prices move without notice, and Tokenomy.ai keeps a dated row per model per day. Margin, forecast and chargeback figures stay reproducible because the rate used on any given day can be looked up on demand, rather than relying on a current-price lookup that changes underneath the report.

The platform turns metered cost into finance-ready output: margin per customer, forecast bands, commit burn-down and chargeback exports in CSV and PDF. Executive PDF reporting and scheduled digests are built in, which is what lets a platform team hand FP&A a number it can stand behind.