I deleted my entire AI microservice and just used Postgres (here is why) 🐘⚡

A few months ago, I needed to build a feature that everyone is asking for right now: "Let our users chat with their messy data." (In my case, it was a massive dump of chaotic customer support tickets). If you Google how to build this, the internet will immediately try to sell you a 5-tier architecture: You need a Vector DB for embeddings. You need a graph database for relationships. You need LangChain to glue it together. You need a separate Python microservice to run it all. I fell for it. I built it. And two weeks later, I was dealing with the most annoying bug in modern engineering: State mismatch. A user would delete a ticket in our main database, but the vector representation of that ticket still lived in our Vector DB. The AI kept hallucinating answers based on deleted data. The "Aha" Moment 💡 Syncing data between a relational database and a dedicated vector database is a nightmare. You have to write custom webhooks, handle failed retries, and pay for two separate servers. So, I