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 workflows without moving data to specialized infrastructureThe service was first previewed in July with an initial capacity of 50 million vectors in a single index. With the GA release, AWS has scaled that up dramatically to 2 billion vectors in a single index and up to 20 trillion vectors per S3 storage bucket. According to AWS, customers created more than 250,000 vector indexes and ingested more than 40 billion vectors in the four months since the preview launch. The scale increase with the GA launch now allows organizations to consolidate entire vector datasets into single indexes rather than fragmenti …
