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Interesting graphic, bland and unvoiced conclusion
You're also missing a lot of details. For example, Milvus and Zilliz are actually a little different, check this out for more details: https://github.com/zilliztech/VectorDBBench (of course run it on your own stuff, don't blindly trust companies just because their product is open source)
Also if you want to throw some more comparisons in their checkout elastic search
Metal [1] is another one on my radar. Their API looks super simple.
Disclosures: None
[1] https://getmetal.io
With Vectara (full disclosure: I work there; https://vectara.com) we provide a simple API to implement applications with Grounded Generation (aka retrieval augmented generation). The embeddings model, the vector store, the retrieval engine and all the other functionality - implemented by the Vectara platform, so you don't have to choose which vector DB to use, which embeddings model to use, and so on. Makes life easy and simple, and you can focus on developing your application.
txtai (https://github.com/neuml/txtai) is another option to consider. It has vector search with SQL, topic modeling and LLM prompt-driven search (retrieval augmented generation).
Disclaimer: I am the author of txtai
chroma can help here https://github.com/chroma-core/chroma