How do companies decide between building AI models in-house or using APIs?

<!-- SC_OFF --><div class="md"><p>I’m trying to understand this from a real-world perspective.</p> <p>Right now, it feels like you can get very far just using existing models

<!-- SC_OFF --><div class="md"><p>I’m trying to understand why models from major labs (GPT, Claude, etc.) dominate real-world usage? You might say it's due to the expensive

<!-- SC_OFF --><div class="md"><p>Just went through a Stanford paper that tracked 51 actual AI deployments - not surveys or sentiment polls, real production systems across 41

<!-- SC_OFF --><div class="md"><p>I used to be all-in on cloud APIs. For any side project, I’d just grab an OpenAI or Anthropic key and not think twice. It was convenient. No

<!-- SC_OFF --><div class="md"><p>AI tools are still locked in its own platforms. Is there some standardized way how to set your own workspace once and if you switch between

<!-- SC_OFF --><div class="md"><p>I randomly had this thought the other day. How strict are AI companies about their employees using competing AI models?</p> <p>For example, if

<!-- SC_OFF --><div class="md"><p>This is just a bad business decision in general. In what world would I need a tool with a preset function to be made on the go?</p> <p><a

<!-- SC_OFF --><div class="md"><p>I ran 90 research queries through 8 AI models simultaneously. 86% of findings were unique to one provider... not rephrasing, literally different