A/B Test CPU Core-Hours vs Average CPU Capacity

Compare monthly CPU core-hours, average CPU capacity, and alternative A/B test planning scenarios to interpret experiment workload estimates.

Monthly CPU core-hours and average CPU cores describe the same total experiment work in different ways, but neither alone captures peak demand. These comparisons explain which metric is useful for workload reporting, continuous-capacity planning, and testing different experiment designs.

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About A/B Test CPU Core-Hours vs Average CPU Capacity

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Monthly CPU core-hours vs average CPU capacity

Two views of the same modeled experiment workload.

Factor	Option A: Monthly CPU Core-Hours	Option B: Average CPU Cores	What It Means
What it measures	Total CPU work over the month	Core-hours divided by planning-month hours	Core-hours quantify cumulative work; average cores express the same work as a continuous average.
Useful for	Workload comparison and usage reporting	Baseline capacity planning	The appropriate metric depends on whether the question is total consumption or ongoing average demand.
Traffic timing	Does not show timing	Assumes even timing	Neither metric reveals hourly or minute-level demand spikes.
Planning-month dependency	Not affected by month length	Changes with month hours	The same work produces a slightly different average-core figure in a 720-hour versus 744-hour month.
Peak sizing value	Needs traffic-profile context	Needs traffic-profile context and headroom	Peak capacity should be based on observed or modeled peak request rates and CPU behavior.

Use core-hours to describe total monthly experiment CPU work and average CPU cores to express an even-load baseline. Use neither as a standalone peak-capacity target.

Partial traffic test vs full traffic test

Comparison of two traffic-allocation approaches with all other CPU inputs held constant.

Factor	Option A: Partial Traffic Test	Option B: Full Traffic Test	What It Means
Experiment requests	Only the assigned share is processed in the test	All eligible requests are processed in the test	A lower allocation produces fewer experiment requests and lower modeled experiment CPU.
Monthly CPU core-hours	Scales with traffic percentage	Highest for the same eligible traffic and request CPU	Doubling test allocation approximately doubles modeled CPU when other inputs stay fixed.
Exposure speed	Slower accumulation of test exposure	Faster accumulation of test exposure	The better approach depends on operational goals and acceptable exposure, not CPU alone.
Peak-risk validation	May underrepresent full-rollout behavior	More representative of maximum experiment traffic	Full allocation can reveal production behavior, while a partial allocation reduces immediate load.
Capacity impact	Lower immediate incremental demand	Higher immediate incremental demand	This comparison only addresses modeled CPU; other systems can have different constraints.

Test traffic percentage is a direct workload lever. A partial allocation reduces modeled CPU, while full allocation represents the largest experiment traffic workload.

Baseline-only estimate vs overhead-inclusive estimate

Comparison of ignoring experiment processing with explicitly modeling it.

Factor	Option A: Baseline-Only CPU	Option B: CPU Including Experiment Overhead	What It Means
Assignment and flag checks	Not separately represented	Included through the overhead percentage	Experiment processing can consume CPU beyond normal request execution.
Logging and analytics	May be omitted	Can be represented in overhead	The model is more complete when the chosen overhead reflects additional work.
Simplicity	Fewer assumptions	Requires an overhead estimate	A baseline-only figure is simpler but may understate the experiment workload.
Capacity-planning realism	Can be optimistic	Depends on quality of overhead input	A measured or cautious overhead estimate generally better represents incremental experiment cost.
Double-counting risk	Low when baseline is measured without testing	Possible if CPU time already includes experiment work	Use zero added overhead when the request CPU input already includes all experiment processing.

An overhead-inclusive estimate is generally more useful for experiment planning, provided baseline CPU and experiment overhead are defined consistently.

Key Differences at a Glance

CPU core-hours describe total monthly work, while average CPU cores spread that work across the planning month.

Test traffic percentage changes experiment workload directly when all other inputs stay constant.

Experiment overhead represents extra processing beyond baseline request CPU.

More variants divide the equal-split per-variant figure but do not automatically change total modeled CPU.

Peak CPU requirements can be much higher than the monthly average CPU capacity.

How to Decide

Choose this if: Use monthly core-hours when comparing the total CPU workload of different tests or months.

Choose this if: Use average CPU cores as an even-load baseline, then assess peak traffic separately.

Choose this if: Model traffic stages separately when an experiment ramps from a small allocation to a larger one.

Choose this if: Use a separate estimate for variants with different backend calls, rendering work, or CPU time.

Choose this if: Avoid adding overhead twice when the CPU-time input was measured with experiment processing enabled.

Choose this if: Compare estimates with production telemetry before making capacity changes.

Assumptions

Comparisons hold eligible request volume, baseline request CPU time, and planning month constant unless the scenario states otherwise.
The traffic allocation comparison assumes that CPU work scales approximately with experiment request volume.
The overhead-inclusive comparison assumes experiment overhead can be represented as a percentage of baseline experiment CPU.
Average CPU capacity assumes traffic is evenly distributed throughout the month.

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Frequently Asked Questions

Should I use CPU core-hours or average CPU cores for A/B test planning?

Use core-hours to compare total workload and average cores to express an even-load baseline. Review peak traffic separately for capacity decisions.

Does 100% experiment traffic always double CPU compared with 50% traffic?

It approximately doubles the modeled experiment CPU when CPU per request and overhead remain unchanged. Real systems may vary with cache behavior and load.

Is an overhead-inclusive estimate always better?

It is more complete when overhead is measured or reasonably estimated. Do not add it if the request CPU input already includes experiment processing.

Do more variants require more CPU?

Not necessarily. Total CPU remains similar if total traffic and request processing are unchanged, but additional variant-specific logic can increase it.

Why is peak capacity not shown by average CPU cores?

Average cores divide total work over every hour of the month, which hides concentrated traffic periods and burst behavior.

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