Estimate log ingestion volume (GB/day): fast methods + validation

Most log pricing models start with ingestion volume: how many GB of logs you send per day. If you do not have a clean export yet, you can still estimate GB/day with a few practical methods and validate quickly once you have real telemetry.

Method 1: From your vendor usage export (best)

If you already have a bill or usage dashboard, take the average daily ingestion over a representative window (7 or 30 days). This is the most accurate planning input.

  • Prefer a window that includes a normal day and a peak day.
  • If your workload is seasonal, keep separate baseline and peak months.

Method 2: From events per second and average event size

If you can estimate event rate and average event size, you can estimate GB/day with this formula (decimal GB):

  • GB/day ~= events/sec × avg bytes/event × 86,400 ÷ 1,000,000,000

Tool: Log ingestion cost calculator (includes event-rate conversion).

  • Sample real logs to estimate bytes/event (do not guess a single number for everything).
  • Split by source: access/ingress, application logs, audit/security logs.
  • Keep a peak multiplier for incidents (errors and retries increase logs dramatically).

Method 3: From throughput (Mbps)

If you have throughput charts for your log shipper or exporter, convert average throughput into GB/day. Make sure you distinguish Mbps (bits) from MB/s (bytes).

Tool: Unit converter.

What to include (and what to separate)

  • Include: access logs, application logs, audit logs, infrastructure logs (kube, systemd), security logs (WAF/firewall).
  • Separate: one-time migrations, bulk debug dumps, and synthetic monitoring (model as special cases).

Common pitfalls (why estimates miss)

  • Duplicate shipping: two agents ship the same logs (doubles ingestion).
  • Verbose debug logs: one noisy service dominates total volume.
  • Multiline events: stack traces can be much larger than normal log lines.
  • High-volume sources: ingress/firewall/audit logs are often bigger than application logs.
  • Incident spikes: retries and errors create a peak month that looks nothing like baseline.

Next: translate GB/day into dollars (and retention)

Once you have GB/day, you typically need at least two more line items: retention storage and optional scan/search.

Ingestion cost Retention storage Tiered storage Scan/search Total log cost

How to validate

  • Pick your top 3 log sources and measure their actual bytes/event (sample 100–1000 events).
  • Validate baseline and peak separately (incident week vs normal week).
  • After changes, verify ingestion GB/day moves in the expected direction and the bill follows.

Sources

Related guides

Log Cost Guide (ingestion, retention, search)
Estimate log costs from ingestion volume, retention, and query/scan fees. Includes common pitfalls and sizing tips.
Log retention storage cost: steady-state GB-month explained
How log retention turns GB/day into stored GB-month cost. Learn the steady-state model, retention tiers, and the fastest levers to reduce long-term log spend.
Azure API Management pricing: model requests, transfer, and log volume
A practical API Management estimate: request volume, response transfer, and logs/observability. Includes a checklist to validate retries, payload size, and usage tiers.
Azure Log Analytics pricing: ingestion, retention, and query costs
A practical model for Log Analytics-style costs: GB ingested, retention storage, and query/scan behavior. Includes a method to estimate log GB from event rate and payload size, plus a validation checklist for high-volume sources.
Cloud Functions pricing (GCP): invocations, duration, egress, and log volume
A practical Cloud Functions cost model: invocations, execution time, outbound transfer, and logs. Includes a workflow to estimate baseline + peak and validate retries, cold starts, and log bytes per invocation.
Estimate DNS queries per month (Route 53 query volume)
How to estimate DNS query volume for Route 53 cost models: from metrics and logs, and what drives query spikes (TTL, retries, resolver behavior).

Related calculators

Log Cost Calculator
Estimate total log costs: ingestion, storage, and scan/search.
Log Ingestion Cost Calculator
Estimate monthly log ingestion cost from GB/day or from event rate and $/GB pricing.
Log Retention Storage Cost Calculator
Estimate retained log storage cost from GB/day, retention days, and $/GB-month pricing.
Log Search Scan Cost Calculator
Estimate monthly scan charges from GB scanned per day and $/GB pricing.

FAQ

What's the fastest way to estimate GB/day?
Use your provider usage/billing export if you have it. If not, estimate from events/sec × average bytes/event and validate with a short real sample.
Should I use uncompressed or compressed size?
Use the billable size for your provider. Some bill on ingested bytes, some on stored bytes, and some on scanned bytes. If you're unsure, start with raw bytes as an upper bound and calibrate later with real usage.
Why is my estimate lower than the bill?
Common causes are verbose debug logs, duplicate shipping (multiple agents), unexpected high-volume sources (ingress, firewall, audit), and incident spikes with retries/errors.
What is the most common mistake?
Using one average and ignoring peaks. One incident week can produce more logs than three normal weeks combined.
Last updated: 2026-01-27