Observability costs explained: logs, metrics, traces, and query behavior

Observability is one of the most common sources of surprise cloud bills. A planning-safe model includes: log GB/day + retention, metrics series count, and a scenario for incident windows (spikes in volume and queries).

1) Logs: ingestion + retention + scan/search

  • Estimate log GB/day and retention days (steady state storage ~ GB/day x retention days).
  • Model scan/search if your provider charges by GB scanned.
  • Hub: log costs

2) Metrics: series cardinality

  • Series count multiplies quickly with labels (pod name, path, userId, requestId).
  • Hub: metrics costs

3) Traces and incident scenarios

  • Trace volume is usually traffic-driven; sample aggressively for low-value spans.
  • During incidents, logs, retries, and query scans often spike together.

Related tools

Log cost Log scan/search Metrics series CloudWatch metrics

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Estimate monthly scan charges from GB scanned per day and $/GB pricing.
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Estimate monthly metrics cost from active series and $ per series-month pricing.
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Estimate CloudWatch metrics cost from custom metrics, alarms, dashboards, and API requests.

FAQ

What usually drives observability cost?
Logs (GB ingested and retained) and metrics series cardinality are the most common drivers. Query/scan/search charges can spike during incidents and from dashboards that run broad queries.
How do I estimate quickly?
Estimate log GB/day and retention days, then estimate unique metrics series count. Add a peak scenario for incident windows where logs and queries spike.
What breaks estimates?
Verbose logs, high-cardinality labels, and always-on dashboards. Retention defaults can silently grow costs over time.
Last updated: 2026-01-22