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A/B Testing Video Bitrate Monthly Calculator Examples

Worked scenarios showing how bitrate, traffic allocation, viewing time, and CDN price can change monthly video delivery estimates.

These scenarios illustrate how the calculator applies monthly plays, average watch time, bitrate mix, and a per-GB delivery rate. They are estimates designed to help interpret expected bandwidth and cost differences before comparing test results.

How to Read Your Results

Total monthly data transfer is the combined estimated decimal GB delivered by both test variants.

Variant-level transfer shows which test group produces the larger share of delivery usage.

Monthly delivery cost equals estimated total GB multiplied by the CDN cost per GB entered.

Cost change versus all Variant A compares the chosen traffic mix with an all-Variant-A delivery baseline.

A negative cost change indicates that the selected mix is estimated to cost less than serving Variant A to all viewers.

Assumptions & Important Notes

  • Examples use decimal gigabytes, with 1 GB equal to 1,000 MB.
  • The reported bitrate is assumed to be the average bitrate delivered during watched time.
  • Average watch time is assumed not to change between variants.
  • The examples include delivery transfer only, not video processing or storage charges.

Related Examples

Frequently Asked Questions

What happens in a 50/50 bitrate test?

Half of monthly plays are estimated using Variant A's bitrate and half using Variant B's bitrate, so the result reflects both delivery volumes.

How does a small high-bitrate test share affect costs?

Its impact depends on the bitrate gap, watch time, and play volume. Even a small share can add meaningful transfer at high scale.

Can I model a lower-bitrate Variant B?

Yes. Enter a lower Variant B bitrate and the result can show a lower estimated transfer and delivery cost than the all-Variant-A baseline.

Why might actual results differ from a worked estimate?

Actual results can differ because of adaptive streaming choices, regional traffic, content complexity, caching, and changes in viewer behavior.

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