Trace Sampling Rate Calculator

Work out trace sampling rate instantly with clear inputs, formula shown and shareable results.

About Trace Sampling Rate Calculator

Trace volume is traces times spans times span size, and with 24 spans per trace the multiplier is large enough that head-based sampling at one percent is common. The trade-off is that a failure occurring in one request per million is almost never captured, which is why tail-based sampling — deciding after the trace completes, keeping all errors and slow traces — is preferred where the backend supports it.

Formula

Trace volume

sampled traces = rate x sampling fraction; daily bytes = sampled traces x spans x bytes per span x 86400

Worked example

With the default values below, Trace Sampling Rate Calculator returns Trace data per day of 26.55 GiB. Change any input above and every figure here is recalculated in your browser.

Inputs used

Request rateminimum 0.001 req/s: 2,500 req/s
Spans per traceminimum 1: 24
Bytes per spanminimum 1 bytes: 550 bytes
Sampling rateaccepts 0.001–100 %: 1 %

Results

Trace data per day: 26.55 GiB
Sampled traces per second: 25.00
Sampled traces per hour: 90,000
Seconds between sampled traces: 0.040 s

Step by step

1Values used
2Trace volume
3Trace data per day= 28,512,000,000
4Sampled traces per second= 25.00
5Sampled traces per hour= 90,000
6Seconds between sampled traces= 0.040 s

Assumptions and limits

The result is derived only from the 4 inputs listed above. Anything not among them is not part of the calculation.
In particular, this calculator does not take inflation, tax, fees or charges or compounding frequency into account.
Figures are rounded for display to 3 decimal places, so totals built from several rounded parts can differ slightly from the unrounded arithmetic.

How these calculators are built and tested is described on the Methodology page. If a figure looks wrong, please report it.

Frequently Asked Questions

What is tail-based sampling?

The collector buffers complete traces and then decides, so it can keep every error and every slow request while discarding routine successes. It gives far better coverage at the same volume.

Should sampling be consistent across services?

Yes, the decision must propagate in the trace context. Independent per-service decisions produce broken traces with missing spans.