Canary Rollout Percentage Calculator

Work out canary rollout percentage instantly with clear inputs, formula shown and shareable results.

About Canary Rollout Percentage Calculator

A canary trades blast radius against signal speed. A small share limits damage but gathers errors slowly, and with rare failures you need roughly 30 events before a rate difference is distinguishable from noise. Working out that time explicitly stops the common mistake of running a one percent canary for five minutes and concluding nothing from zero errors.

Formula

Canary signal

canary rate = total rate x share; time to signal = 30 / (canary rate x expected canary error rate); blast radius = canary rate x error rate increase

Worked example

With the default values below, Canary Rollout Percentage Calculator returns Time to accumulate a usable signal of 42. Change any input above and every figure here is recalculated in your browser.

Inputs used

Total trafficminimum 0.01 req/s: 2,400 req/s
Canary traffic shareaccepts 0.01–100 %: 5 %
Baseline error rateaccepts 0–100 %: 0.2 %
Error increase to detectaccepts 1.01–100 x: 3 x

Results

Time to accumulate a usable signal: 42 s
Traffic reaching the canary: 120.00 req/s
Additional failures per second while the canary runs: 0.4800
Assessment: Canary share gives a statistically usable signal within the bake window

Step by step

1Values used
2Canary signal
3Time to accumulate a usable signal= 42 s
4Traffic reaching the canary= 120.00 req/s
5Additional failures per second while the canary runs= 0.4800
6Assessment= Canary share gives a statistically usable signal within the bake window

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 4 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

Why 30 events?

It is a rule of thumb for the point at which a Poisson count becomes reasonably stable. Fewer events and the confidence interval is wider than the effect you are looking for.

Should the canary get random or targeted traffic?

Random for statistical validity. Routing only internal users gives a fast signal but a distribution that does not represent production.