sense2vec

be k/d the size of the original vector data, because one integer and one float both occupy 32-bits of data. So if your vectors are 300 dimensions, you can save 150 neighbors and serve the results with half the space of the original data. The responses are then instant and exact, and you'll never have problems with load management because the runtime is so cheap. Example demo: https://explosion.ai/demos/sense2vec Again, this doesn't work for queries you have to construct on the fly, for instance it won't work if you need to construct a vector that incorporates information from the user's session or something. It only works if the process of constructing the vectors is already a batch job to e.g. embed the user's history. It also won't work well if you have to page through arbitrary amounts of history, as storing more than a few thousand neighbors per query would get impractical. Generally the similarity accuracy degrades to uselessness long before that point though (especially if you're using an approximation technique).