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Load Based Instance Count Calculator

Work out load based instance count instantly with clear inputs, formula shown and shareable results.

About Load Based Instance Count Calculator

Target-tracking autoscaling computes desired capacity as the aggregate metric divided by the per-instance target, which is exactly how AWS and Kubernetes horizontal autoscalers behave. Step size then determines how many scaling actions are needed, and each action carries a cooldown — so a large gap takes several minutes to close regardless of the arithmetic.

Formula

Target tracking

desired = ceil(aggregate metric / target per instance); steps = ceil(|desired - current| / step size)

Worked example

With the default values below, Load Based Instance Count Calculator returns Desired instance count of 25. Change any input above and every figure here is recalculated in your browser.

Inputs used

Target metric per instanceminimum 0.001
60
Current aggregate metricminimum 0
1,450
Instances currently runningminimum 1
18
Instances added per scaling stepminimum 1
3

Results

Desired instance count
25
Instances to add or remove
7
Scaling steps required
3
Current metric value per instance
80.56

Step by step

  1. 1Values used
  2. 2Target tracking
  3. 3Desired instance count= 25
  4. 4Instances to add or remove= 7
  5. 5Scaling steps required= 3
  6. 6Current metric value per instance= 80.56

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, fees or charges or compounding frequency into account.
  • Figures are rounded for display to 2 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

Which metric works best for target tracking?

Request count or concurrency per instance, because they respond linearly. CPU is noisier and lags the actual load change.

Why do cooldowns matter?

They prevent oscillation. Without one, the scaler reacts to metrics that have not yet reflected the last change and overshoots in both directions.

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