The three horizons sit under the guardrail layering: a CloudWatch alarm in minutes, an anomaly monitor in about a day, a budget across the month. This card is the middle one.
Flash card
AWS Cost Anomaly Detection: machine-learning detection of unusual spend against a baseline it learns for itself, delivered as part of AWS Cost Explorer at no charge.
- Monitors are scoped to an AWS service (Bedrock), to a cost allocation tag, to a cost category, or to a linked account, so a per-feature monitor becomes possible once inference profiles carry tags.
- It learns each monitor’s own pattern instead of comparing spend to a number somebody chose, so a seasonal or steadily growing workload does not alarm on its own growth.
- Alerts carry a dollar-impact threshold and go out by email or Amazon SNS, either individually or batched as a daily or weekly summary.
- Detection runs on billing data and lands roughly a day behind the spend, so cost anomaly detection is not the control that stops a runaway retry loop.
- It costs nothing to run, which makes a monitor per major workload cheap enough to be the default rather than a decision.
Pick it when
Pick it over a budget when what you want is “tell me when this looks unusual for us” rather than “tell me when this passes a number”, and over a CloudWatch alarm when the signal you want is dollars rather than tokens or invocations.
It's the wrong answer when
It is the wrong answer for a same-minute reaction to a retry storm, which wants a CloudWatch alarm on Invocations or token counts; for enforcing a hard ceiling, which wants a budget action, a service quota, or an application rate limiter; and for a workload with no spend history for the model to learn from.