RAG Is Over: Vector Databases Just Became Irrelevant

Why I deleted my vector database and got faster, cheaper, smarter AI responses instead.

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I spent $847 on Pinecone last month.

Not because my app was scaling. Not because I had millions of users. Because I bought into the story we all bought into: “You need a vector database for RAG. That’s how AI applications work.”

Last Tuesday I deleted the entire thing. Switched to a different approach. My response quality went up. My costs dropped to $23. And the part that still makes me laugh — my retrieval got faster.

The Story We All Believed

March 2024. Everyone building with LLMs was doing the same thing:

1. Chunk your documents
2. Generate embeddings
3. Store in vector database
4. Semantic search on user query
5. Stuff results into context
6. Send to LLM

Clean. Logical. Backed by every tutorial and startup pitch deck.

I built three production RAG systems this way. Used Pinecone for one, Weaviate for another, and Qdrant for the third. Spent genuine money. Genuine time. Convinced myself this was the only…