Harness utilizes Google BigQuery ML (BQML) to detect cloud cost anomalies through time-series forecasting models, specifically using the ARIMA_PLUS model, which is highly effective for cloud cost data due to its ability to capture seasonal patterns, trends, and irregular spikes. BQML enables in-database machine learning, eliminating the need for complex data transfers and external ML platforms, thus streamlining the process of anomaly detection in cloud cost management. The approach involves preparing cloud cost data in BigQuery, training the ARIMA_PLUS model, and using BQML's ML.DETECT_ANOMALIES to identify suspicious cost spikes. Additionally, BQML supports cost forecasting and requires periodic retraining to stay updated with the latest trends. With its SQL-based machine learning capabilities, BQML offers a scalable and efficient solution for handling large datasets and automating the anomaly detection process, significantly reducing false positives by incorporating seasonality detection. The system is cost-effective, with BQML training costs clearly labeled within Google Cloud Billing for easy tracking and analysis.