Do vector similarity search inside SQL (pgvector / warehouse). When is this the right call?
A dedicated vector database is not always required. When embeddings sit beside your relational data, nearest-neighbor in SQL makes filters and joins easy. Here is how, and the scale where it breaks down.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
A dedicated vector database is not always required. When embeddings sit beside your relational data, nearest-neighbor in SQL makes filters and joins easy. Here is how, and the scale where it breaks down.
Lead with where the obvious approach breaks, because that is the judgment they are screening for — most candidates jump straight to the happy path and lose the room.
Then walk the failure back through the pipeline in order, naming the one metric the customer's exec sponsor actually cares about before you propose the fix.
