CloudWatch metrics cost optimization: reduce custom metric sprawl

CloudWatch metrics cost typically grows from custom metrics, high-cardinality dimensions, and API polling. The best savings come from reducing metric sprawl while keeping the signals that actually detect incidents.

Step 0: identify the dominant driver

• Custom metric count: how many unique time series you publish (names × dimensions).
• Resolution: standard vs high-resolution metrics where applicable.
• API requests: dashboards and tools polling GetMetricData, etc.
• Coupled costs: alarms and dashboards created to “use” the metrics.

High-leverage savings levers

• Control cardinality: avoid dimensions like userId/tenantId/podId unless you truly need per-entity alerting.
• Aggregate by default: publish service-level metrics (rate, error rate, latency) instead of per-instance metrics for dashboards.
• Right-size resolution: high-resolution is valuable for fast failure, but wasteful for slow-moving metrics.
• Reduce polling: avoid multiple tools polling the same metrics at high frequency.
• Prune unused metrics: stop emitting metrics that are not used by dashboards/alerts or incident response.

Common sprawl patterns

• Kubernetes: per pod/container metrics multiplied across clusters and namespaces.
• Multi-tenant: per customer dimensions explode when customer count grows.
• Copy-paste dashboards: each team clones a full dashboard pack and keeps it forever.
• “Just in case” metrics: metrics emitted without a consumer (no alert, no dashboard, no investigation use).

Practical guardrails that prevent future sprawl

• Dimension budget: require justification for any dimension with unbounded cardinality (tenantId, userId, podId).
• Metric lifecycle: new metrics must have an owner and an expiration/review date.
• One source of truth: avoid multiple agents exporting the same metrics under different names.
• Dashboards-first is risky: keep dashboards focused on a small operational set; explore in logs/traces when needed.

API polling is part of the story

Even if custom metric volume is stable, API request costs can grow as dashboards and tooling refresh more frequently.

Related: estimate metrics API requests.

Validation checklist (do not break observability)

• For every metric removed, name the incident class it supports and what replaces it.
• Ensure you keep service-level SLIs: availability, error rate, and latency.
• Ensure you keep saturation/capacity signals for critical dependencies (queues, DB, CPU/memory).
• After changes, validate dashboards and alerts still function during a test incident window.

Sources

• CloudWatch pricing: aws.amazon.com/cloudwatch/pricing
• CloudWatch metrics concepts: docs.aws.amazon.com

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