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276 points Fendy | 2 comments | | HN request time: 0.001s | source
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resters ◴[] No.45170203[source]
By hosting the vectors themselves, AWS can meta-optimize its cloud based on content characteristics. It may seem like not a major optimization, but at AWS scale it is billions of dollars per year. It also makes it easier for AWS to comply with censorship requirements.
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barbazoo ◴[] No.45170388[source]
> It also makes it easier for AWS to comply with censorship requirements.

Does it, how? Why would it be the vector store that would make it easier for them to censor the content? Why not censor the documents in S3 directly, or the entries in the relational database. What is different about censoring those vs a vector store?

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resters ◴[] No.45170514[source]
Once a vector has been generated (and someone has paid for it) it can be searched for and relevant content can be identified without AWS incurring any additional cost to create its own separate censorship-oriented index, etc. AWS can also add additional bits to the vector that benefit its internal goals (scalability, censorship, etc.)

Not to mention there is lock-in once you've gone to the trouble of using a specific embedding model on a bunch of content. Ideally we'd converge on backwards-compatible, open source approaches, but cloud vendors want to offer "value" by offering "better" embedding models that are not open source.

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1. barbazoo ◴[] No.45170544[source]
And that doesn't apply to any other database/search technology AWS offers?
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2. resters ◴[] No.45170705[source]
It does to some but not to most of it, which is why Azure and GCP offer nearly the exact same core services.