Blog
What we measure
Numbers from running inference in this market: what a language costs to tokenize, what a payment actually confirms, and where our own pricing sits.
How to choose a model for an African language
The same Amharic sentence costs 1.94x English on one model and 7.69x on another. Picking on benchmark scores alone can quadruple your bill for identical work. Here is the measured data for eight languages, and the trap that makes the best tokenizer the wrong choice.
Seven ways to cut an inference bill without changing model
Most teams reach for a smaller model first, which usually costs them quality they did not need to trade. These seven changes are free, take an afternoon, and several of them cut spend by more than switching model would.
Building an LLM feature that survives a bad connection
Streaming, retries and timeouts all behave differently when the network drops halfway through a two-second response. Most LLM tutorials assume a connection that does not. Here is what actually breaks and what to do about each one.
Structured output that actually parses
We tested every model we serve with a real strict json_schema request instead of trusting the catalogue tags. Twelve of them can call a function and still cannot hand you parseable JSON. Here is how to tell, and what to do about the ones that fail.
Migrating an OpenAI app to open-weight models
Two values change and your SDK, retries and parsing all carry over. What does not carry over is model names, three parameters that behave differently, and one assumption about max_tokens that will bite you on reasoning models.