● Covered by 1 source · 1 reportLow impact1 neutral
BMW Group uses AWS-based system to detect cloud cost anomalies across 14,000 accounts
Key points
- BMW Group monitors 14,000 cloud accounts with CLEA.
- CLEA uses AWS and Reply to detect cost anomalies.
- The system forecasts daily cost per account per service.
- Alerts are sent to account owners for spending deviations.
BMW Group's Cloud Cost Monitoring System
BMW Group has implemented Cloud Efficiency Analytics (CLEA), an internal FinOps system built on AWS in collaboration with Reply. This system is designed to monitor over 14,000 cloud accounts within BMW Group's cloud infrastructure. CLEA originated as a set of dashboards in Amazon QuickSight, providing visibility into cloud spending.
Anomaly Detection and Alerting
To enhance cost management, CLEA now incorporates daily anomaly detection. The system sends email notifications to account owners when their spending patterns diverge from expected forecasts. This proactive alerting mechanism addresses the limitation of dashboards, which only show past events when actively viewed.
Forecasting and Data Processing
CLEA ingests daily billing data from AWS Cost and Usage Reports (CUR) and other providers, processing approximately 3 billion rows of data monthly. This data is aggregated to provide daily cost per account per service. The system analyzes yesterday's spend today, after confirming complete AWS CUR delivery, to avoid partial data issues. Each account-service combination receives its own forecast and anomaly evaluation, with expected spend based on 365 days of historical data.
Architecture and Cost Efficiency
The system's architecture processes every account daily using a serverless approach, costing approximately $50 per month for compute resources. This includes forecasting baselines, filtering logic to determine alert-worthy deviations, and an alert engine. CLEA utilizes Prophet, an open-source forecasting tool, to build its cost baselines.
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Reporting from
BMW Group developed Cloud Efficiency Analytics (CLEA), an in-house FinOps system on AWS, to monitor and detect cost anomalies across its 14,000 cloud accounts. This system forecasts expected cloud spend and alerts account owners daily when actual spending deviates from patterns, helping manage cloud costs effectively.