Log Cost Calculator (Ingestion, Retention, and Scan Pricing)

1) Ingestion

Start with GB/day ingested for the logs you send to your vendor. If you have multiple log types, model them separately and add them up.

2) Retention storage

Retention is usually what makes log storage costs grow. Long retention multiplies steady-state stored GB.

3) Optional: scan/search

If your provider charges by GB scanned, estimate scan volume for typical daily query workloads.

Log cost is a chain, not one number

This page should be read as a three-step logging cost chain: ingestion, retention, and search or scan. Many teams only notice the first bill, but the expensive surprise often appears later when retention policies drift or dashboards keep scanning wide windows every few minutes.

• Ingestion: noisy producers and bytes per event create the first bill.
• Retention: policy decides how long the volume keeps costing money.
• Search: dashboards, alerts, and incident queries can create a behavior-driven second bill.

Where total logging estimates usually drift

• Ingestion is modeled, but retention and scan behavior are treated as rounding error.
• One retention rule is applied to every log class even though audit, app, and debug logs have different value.
• Incident months are blended into the baseline, hiding how expensive crisis-time search actually is.
• Query-heavy dashboards are left on aggressive refresh intervals and never reflected in the estimate.

What to separate before trusting the logging model

• Break out security, audit, application, and debug logs instead of using one blended GB/day assumption.
• Keep ingestion, storage, and search as separate cost lines so optimization work has a real target.
• Review retention by log value, not by default settings.
• Track automated queries separately from ad hoc debugging and incident response behavior.

Baseline vs incident-driven logging scenarios

How to review the first real logging bill

• Check whether the miss came from ingestion, retained volume, or scanned GB before changing every assumption.
• Review incident windows separately so search-heavy periods do not distort the calm-month baseline.

Fast optimization order

• Step 1: reduce noisy logs at source (highest ROI).
• Step 2: apply retention tiers by log value.
• Step 3: narrow scans with filters and shorter time windows.
• Step 4: revisit dashboard refresh intervals and ad-hoc query habits.

Log class policy matrix

• Security and audit logs: longer retention, strict sampling controls.
• Application debug logs: short retention and aggressive volume filtering.
• Infrastructure metrics-like logs: summarize or aggregate before ingestion.
• Incident burst logs: keep separate peak assumptions from baseline policy.

Failure patterns

• One retention policy applied to all log classes.
• High-cardinality attributes indexed by default.
• Dashboard auto-refresh intervals driving hidden scan charges.
• Incident months blended into normal months without separate scenario tracking.

Next steps