AWS vector solutions: Build agentic AI where your data lives
AWS embeds vector search into existing databases—no migrations. Six services, one framework: how to build agents where your data already lives.
Why it matters
AWS is removing friction from agent deployment by integrating vector capabilities into RDS, DynamoDB, S3, and other core services. For practitioners building production agents, this changes the architecture decision—no separate vector DB means faster time-to-agent and reduced operational complexity.
The key facts
10 to knowSix AWS services now have built-in vector search
No standalone vector database or data migration required
Vector capabilities embedded in existing databases and storage (RDS, DynamoDB, S3 implied)
Framed as enabling 'agentic AI' deployments
Customer proof points provided for each service
Decision framework included for service selection
Six AWS services now include vector search built-in
Framed explicitly as enabling 'agentic AI' deployment
Decision framework and customer proof points provided
Published Aug 2026 — current AWS infrastructure posture
Go to the source
AWS Machine Learning Blogaws.amazon.com
Publisher excerpt: AWS offers a broad portfolio of vector search built directly into the databases and storage services you already use, with no standalone vector database or data migration required. This post covers six purpose-built services, a decision framework for choosing the right engine, and customer proof…