The Future of Vector Databases

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Quadrant is going about their own path in terms of how they achieve on-disc larger than memory data. So the performance there is already close to bare metal. It's really, really fast. And I think the future is heading towards one where on-disc becomes a requirement and the standard way of implementing an index. But the engineering challenge is still ongoing. The question for me that remains unanswered is which model in vectors database, vectors search will dominate in the longer term? Embedded versus client server, right? We are so used to the model of client server that's been working for more than a decade right now. Pretty much every database we've used is based on the client

There’s so much talk (and hype) these days about vector databases. We thought it would be timely and practical to have someone on the show that has been hands on with the various options and actually tried to build applications leveraging vector search. Prashanth Rao is a real practitioner that has spent and huge amount of time exploring the expanding set of vector database offerings. After introducing vector database and giving us a mental model of how they fit in with other datastores, Prashanth digs into the trade offs as related to indices, hosting options, embedding vs. query optimization, and more.

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