Why the observability log cost calculator matters
Log volume grows with every deploy and nobody is accountable for it, which is how observability quietly becomes one of the largest lines in an infrastructure budget. This page turns that decision into a handful of inputs you can defend in a budget review: volume, unit cost, rate of adoption, and time. The output is a planning baseline, not a promise — it tells you whether the idea deserves a vendor quote, a pilot, or a pass.
- • Biggest swing factor: log volume per service
- • Second-order factor: per-GB ingestion and retention cost
- • Often ignored: retention period, which multiplies storage
What actually changes the answer
log volume per service moves this number first, then per-GB ingestion and retention cost. Run a conservative case and an upside case before you commit. If the maths only works in the upside case, treat it as a time-boxed test with a kill date rather than a line in next year's plan.
What to do with the result
Sample debug logs, drop health-check noise at the agent, and shorten retention on anything not used in an incident review. Those three changes usually cut the bill by a third.
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Read the guideFAQ
What does the observability log cost calculator work out?
It applies Monthly cost = (volume × unit price) + platform fee to the values you enter for gb of logs ingested per month, ingestion + retention cost per gb, platform + host fees per month, monthly log volume growth. Log volume grows with every deploy and nobody is accountable for it, which is how observability quietly becomes one of the largest lines in an infrastructure budget.
How accurate is this observability log cost calculator?
Exact on volumes. Use your invoice's blended per-GB rate — list pricing rarely matches what you pay. Replace the defaults with your own invoice, usage export, payroll data, statement, or vendor quote before making a commitment — the maths is exact, so the answer is only as good as the inputs you feed it.
Which input should I stress-test first?
log volume per service. Re-run with a pessimistic value for it; if the decision flips, that assumption is the thing you need real data on before signing anything. After that, check per-GB ingestion and retention cost and retention period, which multiplies storage.
Which scenario should I start from?
Start with the preset closest to your situation — lean case, expected case, scaled case — then edit the sliders. Presets are realistic starting points, not benchmarks to match, and every change updates the result instantly.
What should I do after running the numbers?
Sample debug logs, drop health-check noise at the agent, and shorten retention on anything not used in an incident review. Those three changes usually cut the bill by a third. A useful planning benchmark to compare against: Observability often lands at 5–15% of total infrastructure spend.
Can I share or save this calculation?
Yes. Your inputs are written into the page URL, so copying the link shares the exact scenario you are looking at — the person who opens it sees the same numbers. You can also export the inputs and results to CSV or PDF from the result card and keep it with the rest of your workings.
How this calculator is built
Independently maintained
Written by Sam Doshi and the RevenueLab editorial team. We don't sell the data feeds this tool is built on.
Sourced from primary data
Benchmarks come from public AdSense / Stripe / IRS disclosures and reader-submitted data — never third-party "$X per view" claims. Full methodology.
Last editorial review
Reviewed on a rolling quarterly cycle. Dated reviews are published on the methodology record for each calculator.
Editorial standards
See our editorial policy and disclaimer. Results are estimates, not advice.