June saw a tectonic shift in the AI data stack with the preview of Amazon S3 Vector Search.

For the last two years, we have been told we need a specialized vector database (Pinecone, Milvus, etc.) for Retrieval-Augmented Generation (RAG). AWS simplified the equation: “Just store your vectors in S3.” Technically, this adds a native vector indexing layer to S3 buckets. You can store embeddings as metadata on your objects and perform KNN (K-Nearest Neighbor) searches directly via API.

This reduces the “complexity tax” of building AI apps. You no longer need a separate pipeline to sync your data lake with your vector DB. S3 is now a high-performance, searchable memory for your AI agents.