AI Spend Anomaly Alerts

ML teams cannot trace which source data ended up in which model, making it impossible to respond to data recall requests or debug production incidents

ML engineers lose track of which dataset version produced which model, making reproducibility and debugging nearly impossible

Engineers submit fine-tuning jobs without knowing the cost upfront and receive bills far higher than expected, especially when re-running failed or misconfigured jobs