FinOps 2.0: Forecast GenAI Cloud Spend with AWS Cost Explorer and Prophet

Forecasting cloud spend for a generative AI workload means dealing with wildly variable GPU instance costs, token-based API charges, and inference traffic spikes — here's how to model it with the AWS Cost Explorer API and Facebook Prophet. You'll learn: How to pull historical cost data via the AWS Cost Explorer API using get_cost_and_usage with granularity and filter parameters scoped to your GenAI services Why Prophet handles the irregular seasonality and step-change cost patterns common in AI workloads better than ARIMA-style models How to separate fixed infrastructure costs (SageMaker endpoints, EKS nodes) from variable token/inference costs before feeding data into your forecast model How to set anomaly detection thresholds and wire Cost Explorer Anomaly Detection alongside your Prophet forecast as a sanity check FinOps tagging strategy for GenAI apps — without clean cost allocation tags, your forecast data is noise Keywords: FinOps cloud cost forecasting, AWS Cost Explorer API, Prophet ML forecasting, generative AI cloud spend, SageMaker cost optimization 🎧 Listen, then go deeper — DevOps & Cloud interview-prep ebooks at DevOpsInterview.Cloud