{"version":1,"type":"story","url":"https://digestai.news/story/amazon-advises-opensearch-aurora-postgresql-or-s3-vectors-for-bedrock-knowledge","json":"https://digestai.news/story/amazon-advises-opensearch-aurora-postgresql-or-s3-vectors-for-bedrock-knowledge.json","markdown":"https://digestai.news/story/amazon-advises-opensearch-aurora-postgresql-or-s3-vectors-for-bedrock-knowledge.md","slug":"amazon-advises-opensearch-aurora-postgresql-or-s3-vectors-for-bedrock-knowledge","headline":"Amazon advises OpenSearch, Aurora PostgreSQL or S3 Vectors for Bedrock Knowledge Base","summary":"Amazon’s Machine Learning Blog walks through how to pick a vector store for Amazon Bedrock Knowledge Bases, comparing three customer‑managed back‑ends: Amazon OpenSearch Service, Amazon Aurora PostgreSQL with pgvector, and Amazon S3 Vectors. The post benchmarks each option across three representative RAG use cases – an e‑commerce product catalog, a deep‑research agent, and a customer‑facing chatbot – highlighting latency, cost and retrieval quality trade‑offs.\n\nFor product search, the blog recommends OpenSearch Serverless because its hybrid search and low‑millisecond latency suit high‑traffic catalogs. For large‑scale research workloads, S3 Vectors is presented as the most cost‑effective choice, cutting storage costs by up to 90 % while keeping sub‑second query times even at a million vectors. For chatbot knowledge bases, Aurora PostgreSQL with pgvector offers a balanced mix of sub‑50 ms latency and relational join capability, allowing developers to fine‑tune indexing methods such as HNSW or IVFFlat. The guidance stresses profiling the application, matching it to a suitable store, and planning for future tiered architectures that combine stores as needs evolve.","keyPoints":["OpenSearch Serverless offers sub‑millisecond latency and hybrid search for e‑commerce product catalogs.","S3 Vectors reduces storage cost up to 90% and scales to millions of embeddings for research agents.","Aurora PostgreSQL with pgvector delivers sub‑50 ms query latency and relational joins for large chatbot knowledge bases."],"whyItMatters":"Choosing the right vector store directly impacts RAG application latency, cost and retrieval quality, influencing the viability of e‑commerce search, large‑scale research agents and customer‑facing chatbots built on Amazon Bedrock.","category":{"slug":"agents","name":"Agents & Tools","url":"https://digestai.news/category/agents"},"entities":{"companies":["Amazon","Amazon Web Services"],"models":["Titan Text Embedding v2"],"people":[]},"firstPublishedAt":"2026-09-17T15:53:13Z","updatedAt":"2026-09-17T15:53:13Z","sourceCount":1,"hasPrimarySource":true,"sources":[{"outlet":"AWS Machine Learning Blog","title":"Selecting a vector store for Amazon Bedrock Knowledge Bases","url":"https://aws.amazon.com/blogs/machine-learning/selecting-a-vector-store-for-amazon-bedrock-knowledge-bases","publishedAt":"2026-09-17T15:53:13Z","type":"primary","primary":true,"lead":true}],"sourceNotes":null,"discussions":[],"thread":null,"cite":{"text":"Digest AI, \"Amazon advises OpenSearch, Aurora PostgreSQL or S3 Vectors for Bedrock Knowledge Base\", 17 September 2026, https://digestai.news/story/amazon-advises-opensearch-aurora-postgresql-or-s3-vectors-for-bedrock-knowledge","publisher":"Digest AI","title":"Amazon advises OpenSearch, Aurora PostgreSQL or S3 Vectors for Bedrock Knowledge Base","datePublished":"2026-09-17T15:53:13Z","url":"https://digestai.news/story/amazon-advises-opensearch-aurora-postgresql-or-s3-vectors-for-bedrock-knowledge"},"generatedBy":"Written by Digest AI's editorial model from the linked sources; the sources are the record.","license":"Headlines, digests and key points are written by Digest AI and may be quoted with a link to the story page. Linked articles belong to their publishers. Terms: https://digestai.news/terms#reuse"}