Logging calculators

Model ingestion + retention first, then add query/scan and metrics cardinality where it matters.

A practical model

  1. Estimate GB/day ingested and retention days.
  2. Convert to GB-month stored and apply $/GB-month.
  3. Add scan/query cost if you run frequent or wide queries.
  4. For metrics, model active series (cardinality) and retention.

What spikes bills

  • High-cardinality labels (metrics) and noisy debug logs.
  • Wide queries that scan large time ranges or many log streams.
  • Incident retries that multiply log volume without increasing useful signal.

How to get your inputs

  • Ingestion GB/day: use log platform metrics or billing exports by log group.
  • Retention days: check policies for each log group or bucket.
  • Scan GB: query history or dashboards (range length x data scanned).
  • Metrics series: active series counts and cardinality reports.
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Result interpretation

  • If ingestion dominates, focus on log volume controls before tuning pricing tiers.
  • If query costs dominate, add guardrails: shorter ranges, fewer streams, and sampled queries.
  • Retention changes often have a bigger impact than storage class tweaks.
  • Use a peak scenario; incident bursts and retries are the real cost multipliers.

Scenario planning

  • Baseline: average GB/day ingested and normal query frequency.
  • Peak: 2x-3x ingestion for incident months and wider query windows.
  • Retention shift: model shorter retention as a separate scenario.
  • Cardinality growth: add a growth rate for metrics series counts.

Fastest logging cost-reduction order

  1. Cut noisy sources first (debug duplicates, repetitive success logs).
  2. Apply retention tiers by value instead of one global retention setting.
  3. Narrow query windows and enforce filtered dashboards for high-volume streams.
  4. Control metrics cardinality before scaling collectors or storage tiers.

Validate after changes

  • Compare ingestion GB/day to billing usage types after policy changes.
  • Check retention policies for each log group or bucket.
  • Review query scan volume after dashboard or alert updates.
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