Teams either over-provision (wasting money) or under-provision (causing outages) because capacity planning relies on gut feeling rather than data-driven forecasting. Historical usage data exists in monitoring systems, but extracting actionable forecasts from it requires time-series analysis that most operations teams do not have bandwidth for.

The AI Approach

An LLM combined with time-series analysis examines historical resource utilization, correlates it with business metrics (user growth, traffic patterns), and projects future capacity needs with confidence intervals.

Three-Step Build

  1. Collect metrics - Export CPU, memory, disk, and network utilization history from your monitoring platform. Include business metrics like active users, request volume, and data volume.
  2. Forecast - Use a time-series model or prompt an LLM with the historical data to project resource needs 30, 60, and 90 days out. Factor in known events: planned marketing campaigns, seasonal peaks, new feature launches.
  3. Recommend - Translate forecasts into specific infrastructure recommendations: when to add nodes, when to increase instance sizes, and what the cost impact will be.

Where It Breaks

Unexpected traffic spikes from viral events or press coverage are inherently unpredictable. The model also cannot account for efficiency improvements from code optimizations planned for the next quarter. Forecasts are educated guesses, not guarantees.

The Production Path

Run forecasts weekly and publish to a capacity planning dashboard. Compare predictions against actual usage to calibrate the model. Integrate with your cloud provider’s APIs to auto-generate scaling recommendations or even trigger auto-scaling adjustments with human approval.