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Best Vector Databases for AWS

Building on AWS? Choosing a vector database that integrates well with your existing AWS infrastructure means lower latency, simpler networking, and unified billing. From fully managed services on AWS Marketplace to self-hosted options on EC2/EKS, these databases work seamlessly with Amazon services like SageMaker, Bedrock, Lambda, and S3. Whether you need a serverless option or an enterprise-grade deployment, there's a vector database that fits your AWS stack.

12 databases compatible with AWS

Why use AWS with a vector database?

Building on AWS? Choosing a vector database that integrates well with your existing AWS infrastructure means lower latency, simpler networking, and unified billing. From fully managed services on AWS Marketplace to self-hosted options on EC2/EKS, these databases work seamlessly with Amazon services like SageMaker, Bedrock, Lambda, and S3. Whether you need a serverless option or an enterprise-grade deployment, there's a vector database that fits your AWS stack.

How to get started with AWS

  1. 1Choose between a managed service (OpenSearch, Pinecone) or self-hosted (Milvus, Qdrant on EKS)
  2. 2Deploy in the same AWS region as your application for lowest latency
  3. 3Configure VPC peering or PrivateLink for secure database connectivity
  4. 4Integrate with AWS Bedrock or SageMaker for embedding generation and AI workflows

FAQ — AWS & Vector Databases

What is the best AWS-native vector database?

Amazon OpenSearch Service is the most AWS-native option with deep integration into the AWS ecosystem. For a dedicated vector database on AWS, Pinecone and Qdrant Cloud both offer AWS deployments.

Can I run vector databases on Amazon EKS?

Yes. Milvus, Qdrant, Weaviate, and Vald all support Kubernetes deployment and work well on Amazon EKS with auto-scaling and persistent storage via EBS.

Which vector database works with AWS Bedrock?

OpenSearch, Pinecone, Redis, and pgvector (via Amazon Aurora) integrate with AWS Bedrock for RAG applications. OpenSearch has the deepest native integration.

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