2 posts tagged with "internationalization"

Your pricing page says every user pays the same. Your cost dashboard says otherwise. The same AI feature — same prompt template, same model, same feature flag — costs 55% more to serve a Spanish user, roughly double for a Japanese user, and over 3x for an Arabic or Bengali user. Meanwhile, the quality those users receive is measurably worse: on identical benchmark questions translated across languages, frontier models drop 13 to 24 percentage points when you leave the English distribution.

Most teams shipping AI features globally have never measured either number. They have per-locale pricing, per-locale support SLAs, per-locale legal review — and a single English eval suite standing in for the experience of every user on the planet.

This is the tokenizer tax, and it compounds with a quality gap that scale alone doesn't close. Both are invisible in your dashboards until you slice by language, and both were decided years before you wrote your first prompt, by the training corpus of a tokenizer you don't control.

The financial planning spreadsheet for the international launch had a clean line item: "extend eval coverage to seven new locales — assume 7× current eval cost." The English eval suite took two weeks and $40K to build, so seven locales would be $280K and a quarter of engineering time. The CFO signed it. The VP of Product signed it. The launch shipped.

Six months later the actual eval bill had crossed $310K and the team was still standing up the last two locales. The labeling vendor had churned through three replacements for the Portuguese-Brazilian pool because the first two kept producing inter-rater agreement scores an honest review would call random. The German judge model was scoring 6% lower than the English one on the same content — the team initially read this as a German model regression until a manual audit revealed the judge itself was the regression. And the eval lead was spending forty percent of their week on a question nobody had budgeted: how do we know when locale A's pass rate is actually worse than locale B's, versus when our cross-locale measurement is just noisier than the gap?