Metrics and monitoring costs explained: series, cardinality, and retention
Metrics bills are usually a cardinality problem. If you model only \"metrics per host\" you will miss label explosion (pod names, paths, user IDs). This hub links the best checklists and the calculators that make series math explicit.
1) Estimate unique series (cardinality)
- Count series = metrics x label combinations x environments.
- High-cardinality labels are the common root cause of runaway spend.
- Tool: Metrics time series cost
2) Retention and query behavior
- Retention is a GB-month problem: longer retention grows stored dataset size.
- Dashboards and API polling can create hidden request-based charges on some platforms.
3) Alerts and high-frequency collection
- Alert counts and evaluation frequency can add recurring cost.
- Use high-resolution collection only for signals that require it.
Related tools
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Related calculators
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Estimate monthly metrics cost from active series and $ per series-month pricing.
CloudWatch Metrics Cost Calculator
Estimate CloudWatch metrics cost from custom metrics, alarms, dashboards, and API requests.
AWS CloudWatch Alarms Cost Calculator
Estimate alarm-month charges from standard, high-resolution, and composite alarm counts.
FAQ
What usually drives metrics cost?
The number of unique time series (cardinality) is the big driver. High-cardinality labels like userId, requestId, pod name, or URL path can multiply series quickly.
How do I estimate quickly?
Estimate the number of unique series, the scrape/publish frequency, and retention. Then add alerting and dashboard usage if your provider prices API calls or alarms separately.
What breaks estimates?
Unexpected label cardinality growth, verbose custom metrics, and always-on dashboards/alerts that poll frequently.
Last updated: 2026-01-22