The Cost of Running AI Search in Production 💸

Everyone talks about building RAG systems with LLMs. Nobody talks about what it costs to run them.

Spent the last two weeks auditing AI search implementations and pricing models. The findings surprised me.

Three things that stood out:

1. Vector database costs aren't the problem 📊 Most teams obsess over their vector database pricing. But at scale, your database is barely 15% of total spend. The money drain in your LLM pipeline comes from somewhere else entirely.

2. Your AI embedding model choice compounds fast ⚡ That $0.11 difference per million tokens seems negligible. Until you're processing 10M tokens monthly and suddenly your AI costs are 6.5x higher than they need to be.

3. Three configuration settings control your burn rate 🎯 The difference between a $500/month LLM system and a $5,000/month one often comes down to: chunk size, how many results you retrieve, and when you're calling your embedding APIs.

Most teams move from proof-of-concept to production without understanding how their vector database → LLM pipeline charges them. That's when the bills start surprising people. 💰