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Perhaps it’s not that different than other solutions have but my impression was their payload system was more flexible and extensible than competing options, especially with the speed that Qdrant delivers, so for example pg_vector can leverage all of postgres capabilities but I don’t believe it can compete with Qdrant on vector specific problems.

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qdrant vs pgvector remains to be benchmarked for a range of relevant sizes, please drop any if you see them!

the only time we've found pgvector to be preferable to qdrant is when the per-category embeddings are very few. E.g. in cases where filtering is expected to reduce the dataset greatly for each query (>99.9%).

This is when the relational nature of postgres really shines - you can even do in-mem calculations for distance.

Qdrant does this as well, with 100% accuracy. But it's not on small categories, but rather on small segments.

You could put different categories in different shards though, resulting in the same effect.

You can also specify exact=true to do the same on any size, but this can get rather slow when your collection grows.

Disclaimer: I'm part of Qdrant.

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