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Teams building AI features usually cost models cost a simple assumption. One user sends one request and the model generates one response. You multiply that by your daily active users, add some margin,
This is for people trying to run AI models locally on a personal machine. Say you want to run Llama locally on a MacBook Air, you visit Hugging Face and see a number printed on every model card that a
Saw a comment online about Models getting dumber or Model Labs trying to just pass benchmark as a tradeoff for losing some capabilities. This brings back memory of model selection in early days where
When building AI products, the fastest and laziest way is to pick a model the way you typically pick a cloud provider. Evaluate a few options, choose one, and move on. One model, One API and everythin
I remember when prompt design was one of the hottest topics in getting value out of LLM For teams building AI features, it sits in the engineering backlog, or the AI team's to-do list, or nowhere at a
Engineers building AI/ML should understand continuous batching intuitively. Some PMs have never heard of it or don't understand it. That gap costs product teams real money and slower user experiences,
There's a pricing detail most product teams don't look at closely enough. Input tokens and output tokens are not the same price. Output tokens cost more. Sometimes 2-3x more depending on the model. Th
At first glance, you think there is no price tag for Nobody context windows. It's not listed in the model pricing. It doesn't show up in your planning. Your engineering team probably hasn't flagged it
There's a question that comes up a lot when teams start measuring AI performance. "Why does our model feel slow? We're using the same model as everyone else." The answer almost always lives in the sam
