🚀 Building Multi-Tenant Vector Data Store with Amazon Aurora PostgreSQL

Josh Hart & Nihilson Gnanadason are excited to share their comprehensive two-part blog and sample on implementing vector search in multi-tenant environments.

Many organizations want to bring generative AI applications into their software-as-a-service (SaaS) deployments. These applications often use a "multi-tenant" designs, which isolates the data of a tenant (e.g. a customer of the SaaS app) from other tenants to meet security and privacy guidelines. In a multi-tenant environment, multiple customers share the same application instance and often the same database. Without proper isolation, there's a risk that one tenant could potentially access or manipulate another tenant's data, leading to severe privacy breaches and security vulnerabilities. When you query to retrieve vector embeddings, they need to be tenant-aware to retrieve only tenant-scoped data from the vector store and you need a way to enforce tenant isolation.

Learn how to choose the right approach for your SaaS application's vector search needs. From basic setup to production-ready implementations.

Part1: Self-Managed : https://aws.amazon.com/blogs/database/self-managed-multi-tenant-vector-search-with-amazon-aurora-postgresql/

Part2: Fully-Managed: https://aws.amazon.com/blogs/database/multi-tenant-vector-search-with-amazon-aurora-postgresql-and-amazon-bedrock-knowledge-bases/

Note : For OpenSearch as a your vector store, you can reference this sample here : https://github.com/aws-samples/data-for-saas-patterns/tree/main/samples/multi-tenant-vector-database/amazon-opensearch

#AWS #Aurora-PostgreSQL #Vector #GenAI #SaaS