I would suggest that anyone trying a real comparison of vector DB's consider the following

    - necessary functions / use cases (eg prefiltering, dense search)
    - embeddings version management
    - anticipated embedding size (the article only considers glove-100 on ANN-benchmarks, which is quite different from openai-ada-002 1536 - both in terms of their output distribution and the vector size)
    - required precision / recall
    - required ingestion speed 
    - required ingestion throughput / time to ingest periodic updates
    - required query speed (percentiles, not average!)
    - required query throughput
    - required RBAC, data privacy, active-active, etc

...and so much more. ANN-benchmarks is a good start for thinking about this but remember that actual throughput is quite different from whatever you see in the algorithms benchmarking!