A/B Test Raw Storage vs Provisioned Storage

Compare raw A/B test data storage with provisioned storage after database overhead and replication.

Raw record data is useful for understanding payload volume, while provisioned storage is more useful for infrastructure planning. The difference comes from database overhead and the number of complete copies retained.

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About A/B Test Raw Storage vs Provisioned Storage

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Raw data estimate vs provisioned capacity

Compare the two storage views used when planning an experiment database.

Factor	Option A: Raw Storage	Option B: Provisioned Storage	What It Means
Includes event and assignment payloads	Yes	Yes	Both begin with the same estimated event and assignment records.
Includes indexes and metadata	No	Yes, through overhead allowance	Raw storage omits database structures outside record payloads.
Includes operational headroom	No	Yes, if included in overhead	Capacity planning generally needs an allowance beyond measured payloads.
Includes replica copies	No	Yes	Provisioned storage multiplies the requirement by the replication factor.
Best use	Estimating data volume and payload efficiency	Planning total database capacity	The useful view depends on the planning question.

Raw storage shows the underlying experiment data volume; provisioned storage estimates the space required after practical database allowances.

Lower event detail vs richer event detail

Compare compact event records with detailed records for the same experiment traffic.

Factor	Option A: Compact Event Tracking	Option B: Detailed Event Tracking	What It Means
Average record size	Smaller	Larger	Fewer retained properties generally reduce stored bytes per event.
Storage growth	Lower per event	Higher per event	Event record size is multiplied by every tracked event.
Analysis context	Limited to essential fields	May support deeper segmentation	The appropriate level of detail depends on the questions the experiment must answer.
Schema and governance effort	Usually simpler	Often more complex	More properties may require more validation, documentation, and retention management.
Best use	Focused experiments with defined metrics	Exploratory analysis with justified fields	Avoid collecting fields that are not needed for measurement or analysis.

Detailed event tracking can improve analytical flexibility, but it increases the stored volume of every event and may add operational complexity.

Key Differences at a Glance

Raw storage excludes database overhead and replica copies; provisioned storage includes both.

Event data commonly grows faster than assignment data because multiple events can be stored per visitor.

Replication increases total stored capacity in direct proportion to the number of complete copies.

Larger event payloads affect every event record, so small per-event changes can have a large total effect.

The number of variants does not normally multiply visitor assignments within one experiment.

How to Decide

Choose this if: Use raw storage to assess the underlying volume created by your event schema.

Choose this if: Use provisioned storage when estimating database capacity across replicas.

Choose this if: Base record-size inputs on stored samples rather than network payload sizes where possible.

Choose this if: Model backups, exports, logs, and long-term retention separately from the calculator result.

Choose this if: Review event properties and indexes before increasing infrastructure capacity.

Assumptions

Each visitor has one assignment record for the experiment.
The selected overhead reasonably represents the database environment being considered.
Replica copies contain complete copies of the experiment data.
Comparisons focus on storage volume rather than cost, reliability, or query performance.

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Frequently Asked Questions

Which result should I use for database capacity planning?

Use provisioned storage because it includes the chosen overhead allowance and replication factor.

Can reducing event size have a meaningful impact?

Yes. The average event size is applied to every stored event, so even a small reduction can affect total volume at scale.

Is a higher replication factor always better?

It increases the number of stored copies and capacity required. The appropriate configuration depends on system requirements outside this estimate.

Should I remove all event properties to save storage?

Not necessarily. Retain fields needed for valid measurement and analysis, while avoiding unnecessary or rarely used properties.

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