AI-Assisted Forecasting and Capacity Planning in Multi-Tenant Environments

Capacity planning and infrastructure automation are still some of the hardest problems in modern distributed systems, with a direct impact on overall costs. However, they are often underestimated, especially as hardware keeps improving and each CPU core becomes faster over time. As infrastructure scales in size and complexity, historical statistics and operational models based on historical data fail to provide reliable forecasting metrics and cost projections. Building on prior research that examines operational ownership, infrastructure reliability, and deployment correctness, this paper investigates the application of Artificial Intelligence (AI) to capacity planning and Infrastructure as Code (IaC). We demonstrate how learning-based optimization, predictive analysis and automation can improve planning accuracy, reduce operational toil, and help achieving reliability and efficiency objectives. The paper positions AI not as a replacement for capacity management systems but presents a force multiplier to ensure the accuracy of such pieces of software is high ensuring forecasts and reality do not differ by a large amount.