dynamodb vector-search embedding aws rag

DynamoDB enables native vector search without separate stores

Store embeddings directly in DynamoDB and query them with single-digit millisecond latency—eliminates ETL to specialized vector DBs.

Summary

Reduces operational complexity by collocating vector and relational data, cutting latency and infrastructure overhead for RAG pipelines and similarity search workflows built on existing DynamoDB deployments.

Why it matters

Implementation verdict

Replaces managed vector database overhead for AWS-native stacks. Requires schema refactor to add vector columns (up to 4,096 dimensions) and migration of existing embedding pipelines. Worth adopting now if you're already DynamoDB-heavy; monitor 100-result query limit for ranking workloads.

Sources

1.supports up to 4,096 dimensions, Euclidean, Cosine, and Dot product distance functions, and returns up to 100 results per query with single-digit millisecond latency at 99% or higher recall
2.It is generally available in all commercial AWS regions, including the AWS GovCloud (US) regions

Dev Signal

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