How do large AI chatbots/companions manage LLM costs at scale?

I’ve been looking at multiple repos for memory, intent detection, and classification, and most rely heavily on LLM API calls. Based on rough calculations, self-hosting a 10B parameter LLM for 10k users making ~50 calls/day would cost around $90k/month (~$9/user). Clearly, that’s not practical at scale.

There are AI apps with 1M+ users and thousands of daily active users. How are they managing AI infrastructure costs and staying profitable? Are there caching strategies beyond prompt or query caching that I’m missing?

Would love to hear insights from anyone with experience handling high-volume LLM workloads.