Why use a vector database instead of Postgres for RAG embeddings?

When the interviewer says the company already runs Postgres, Tanishka argues that a traditional SQL index compares values along one dimension, while an embedding is a vector with many dimensions. Tanishka cites 768-dimension vectors per chunk, with some embedding models going up to 8,192, and says comparing those is what a vector database is built for.

▸In their words

“in SQL you can only do it in one dimension, right?” Tanishka · at 1:36 — open in the transcript →

Source: our own transcription of the episode. The full conversation is on Gaurav Sen (2026-09-22). Read the whole episode summary →

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