AWS Databases

Unlock high throughput and highly relevant vector search outcomes with low latency using native vector database capabilities. Integrations with Amazon Bedrock provide fully managed and automated retrieval augmented generation (RAG).

Secure your applications with encryption at rest and in transit, network isolation, and more. AWS delivers resiliency with up to 99.999% availability, multi-AZ deployments, multi-Region replication, automated failovers, and more.

Start small and scale with relational databases that have 3–5x higher throughput than alternatives or nonrelational databases that provide submillisecond to microsecond latency at any scale.

Lower total cost of ownership by using the right tool for the job, and leverage innovations like serverless, Aurora I/O-Optimized for price predictability, and EC2 Graviton4 instances for additional price performance benefits.

Free your teams from time-consuming tasks. With proven operational excellence, AWS handles tasks like server provisioning and backups and also provides continuous monitoring, self-healing storage, and automated scaling.

Scale instantly using serverless, with no servers to manage and zero downtime maintenance. Also, AWS zero-ETL integrations simplify analytics, ML, and search on operational data, removing the need to build or manage data pipelines.

A clear majority indicated that AWS is their chosen primary cloud provider for deploying SQL Server workloads. This IDC report shares why, including key criteria for choosing a cloud provider, benefits and options for using AWS, and essential guidance.

This whitepaper provides comprehensive guidance for organizations looking to migrate Oracle database workloads to AWS. It outlines the key motivations, challenges, and strategies for successful Oracle migrations, including licensing optimization, modernization opportunities, and available migration tools and services.

Organizations who adopted Amazon Aurora realized a 42% lower TCO, 6 months payback on investment, and 434% three-year ROI.

This blog explains how MCP servers transform traditional database development workflow by helping developers build more efficiently by providing AI-assisted tools that understand context, suggest improvements, and help reason through system configurations.

The blog discusses how developers can use Vercel's v0 generative UI tool to build modern applications with AWS databases. It demonstrates how to build various applications using different databases including Aurora, DynamoDB, ElastiCache, and Neptune, while emphasizing secure connectivity through OpenID Connect (OIDC).

This blog discusses the crucial role of vector databases in generative AI applications, particularly in implementing RAG. It explains how vector databases help store and query embeddings of domain-specific data to enhance the accuracy and relevance of AI responses.

The blog discusses how Aurora Optimized Reads significantly improves vector search performance, particularly when using HNSW indexing, delivering up to 20x better query performance compared to IVFFlat indexing.

This blog explains how organizations can customize foundation model responses with their private data while leveraging Aurora's performance features and Amazon Bedrock's fully managed RAG workflow.

The workshop demonstrates how to integrate foundation models from Amazon Bedrock and Hugging Face with Aurora PostgreSQL to create, store, and efficiently query vector embeddings.

This workshop demonstrates how to combine DynamoDB's high-performance transactional capabilities with OpenSearch Servicel search and analytics features, creating a robust architecture for context-aware reasoning applications. Through practical exercises, you will gain expertise in setting up Amazon Bedrock Connector, managing product catalog data, and implementing vector-based search functionalities.

Amazon Neptune Analytics help you reveal breakthrough insights and apply generative AI via graph analytics, 80 times faster data loading, and vector similarity search.