Tried 2 vector DBs couldn’t justify difference maybe setup issue

This experience usually means the vector database was never the main differentiator for your use case. Teams often expect dramatic improvements from swapping infrastructure layers, but if chunking, embeddings, metadata, and ranking stay the same, the results may look almost identical.

That does not mean the experiment was pointless. It may simply be telling you that the bottleneck lives elsewhere. Retrieval quality is often more sensitive to data preparation and query handling than to the brand name of the vector store itself.

The useful next step is to compare setups under controlled conditions: same corpus, same embeddings, same queries, same reranking. If the difference still feels small, you have learned something valuable. It means your optimization effort probably belongs higher up the stack.