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

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