The Hidden Cost of Building AI for Indian Languages

The first time a client asked us to run a content pipeline natively in Hindi instead of writing in English and translating afterward, we expected the usual headaches: tone, idiom, getting a human reviewer to sign off on cultural nuance a model can't reliably judge on its own. What we didn't expect was for the same prompt, the same task, the same output length, to suddenly cost meaningfully more and hit context limits we'd never come close to in English.

It turns out there's a real, measured reason for that, and it has nothing to do with the model being worse at Hindi. It's the tokenizer.

The tax nobody puts in the pricing page

A recent paper, The Tokenizer Tax, quantified something a lot of teams building for Indian markets discover the hard way: using OpenAI's cl100k_base tokenizer, the same content in Indian languages takes on average 8 times...

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