# Aws

> Browse 6 items tagged with Aws.

## Items

- [Amazon Aurora Serverless v2](https://vectordb.ever.works/items/amazon-aurora-serverless-v2) — Amazon Aurora Serverless v2 is a cloud-hosted, serverless relational database (Postgres/MySQL compatible) with pgvector support for managed vector workloads, featuring auto-scaling compute/memory, pay-per-use pricing, automated backups, and multi-AZ/multi-region high availability. Suited for enterprise RAG via Amazon Bedrock Knowledge Bases and production AI apps. Provides easier operations than self-hosted Milvus or Postgres, deeply integrated with AWS unlike standalone Zilliz.
- [Amazon Aurora Machine Learning](https://vectordb.ever.works/items/amazon-aurora-machine-learning) — Amazon Aurora Machine Learning provides managed vector storage and search capabilities integrated into Aurora PostgreSQL for AI workloads on AWS. Key features include serverless scaling, direct ML model calls via SQL for embeddings, and seamless integrations with Bedrock and SageMaker. Perfect for RAG pipelines and enterprise AI applications, it simplifies vectorization and abstracts infrastructure compared to self-hosted options like Milvus.
- [Amazon ElastiCache Vector Search](https://vectordb.ever.works/items/aws-elasticache-vector-search) — Vector search extension for Amazon ElastiCache for Redis, featuring HNSW indexing for k-NN similarity, hybrid lexical+vector search with BM25 fusion capabilities. Used for enterprise semantic caching, real-time recommendations, and RAG applications. Integrated Redis module offers sub-microsecond latency vs standalone like Weaviate, optimized for hot data workloads.
- [Amazon S3 Vector Search](https://vectordb.ever.works/items/s3-vector-search) — Leveraging Amazon S3 as a storage layer for vector databases, enabling 70-95% cost reduction for certain use cases. S3's low storage costs make it attractive for large-scale vector datasets with appropriate access patterns.
- [Amazon S3 Vectors](https://vectordb.ever.works/items/amazon-s3-vectors) — Serverless object storage with native vector storage and query capabilities, supporting up to 2 billion vectors per index and 20 trillion per vector bucket. Optimized for production-scale AI workloads including RAG, semantic search, and conversational AI with sub-second query latencies. Integrates directly with Amazon Bedrock Knowledge Bases and Amazon OpenSearch Service.
- [Amazon Bedrock Knowledge Bases](https://vectordb.ever.works/items/amazon-bedrock-knowledge-bases) — A fully managed service within Amazon Bedrock that automates the retrieval-augmented generation (RAG) workflow by ingesting unstructured and structured data, converting it into embeddings, and storing them in supported vector databases. It enables grounding generative AI responses with enterprise data without manual orchestration.

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