AWS claims 90% vector cost savings with S3 Vectors GA, calls it ‘complementary’ – analysts split on what it means for vector databases
📋 AWS claims 90% vector cost savings with S3 Vectors GA, calls it ‘complementary’ – analysts split on what it means for vector databases 완벽가이드
✨ AWS claims 90% vector cost savings with S3 Vectors GA, calls it ‘complementary’ – analysts split on what it means for vector databases
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Vector databases emerged as a must-have technology foundation at the beginning of the modern gen AI era. What has changed over the last year, however, is that vectors, the numerical representations of data used by LLMs, have increasingly become just another data type in all manner of different datab
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Vector databases emerged as a must-have technology foundation at the beginning of the modern gen AI era. What has changed over the last year, however, is that vectors, the numerical representations of data used by LLMs, have increasingly become just another data type in all manner of different databases. Now, Amazon Web Services (AWS) is taking the next leap forward in the ubiquity of vectors with the general availability of Amazon S3 Vectors. Amazon S3 is the AWS cloud object storage service widely used by organizations of all sizes to store any and all types of data. More often than not, S3 is also used as a foundational component for data lake and lakehouse deployments. Amazon S3 Vectors now adds native vector storage and similarity search capabilities directly to S3 object storage. Instead of requiring a separate vector database, organizations can store vector embeddings in S3 and query them for semantic search, retrieval-augmented generation (RAG) applications and AI agent workflo