Tagged
Most AWS cost forecasts miss by 30–50% not because engineers are careless, but because the forecasting model does not match how AWS actually charges. This is the playbook for getting forecasts right: which metrics to measure, which models to use, and where the structural gaps are.
Autoscaling surprise bills are pattern-shaped: asymmetric thresholds, bad metrics, Lambda duration, Spot storms. July 2026 refresh — target tracking, Budget Actions, FinOps Agent.
The reason AWS cost problems grow undetected is not technical — it is organizational. Engineers make architectural decisions with no cost feedback. Finance sees bills 30 days late. No one owns the gap between the two.
Surprise AWS bills usually come from one of a handful of sources. Cost Explorer patterns, budget alerts, anomaly detection, and the tagging discipline that turns "who owns this $4,000 spike" into a 30-second answer instead of a week-long Slack thread.