tokenisation.

Where tokenisation fits on a long engagement.

Tokenisation is not uniform across languages, and that has direct commercial consequences. English is efficiently tokenised because the vocabularies were built around it; Thai, Japanese and many other languages consume considerably more tokens for the same content, so a multilingual product costs more per user in some markets than others.

It also explains behaviours that otherwise look like model defects. Difficulty counting characters, inconsistent handling of unusual formatting, and sensitivity to whitespace all follow from the model seeing tokens rather than characters.

What an assigned team does with tokenisation.

Counting tokens accurately matters for anything that must fit a context window. Estimating from character counts is unreliable enough to cause truncation failures in production, and the correct tokeniser for the specific model is the only accurate source.

Getting that right in the retrieval path is detail work that prevents a recurring class of failure, handled inside the capacity assigned under managed ai services.

What we use tokenisation for.

• Cost projected per language Token efficiency measured per market, because it varies substantially.
• Context budgets calculated accurately Real token counts rather than character estimates, so truncation does not surprise.
• Model behaviour explained Character-level oddities understood as tokenisation rather than treated as defects.

How tokenisation capacity is assigned.

Tokenisation detail sits inside AI capacity, with accurate counting built into any path that must respect a context limit.