AWS Introduces Native Vector Search for DynamoDB
Amazon DynamoDB recently introduced native vector search, allowing developers to store embeddings alongside application data and run approximate nearest-neighbor queries directly from DynamoDB without using a separate vector database. The feature supports filtered similarity searches and configurable vector indexes for semantic search workloads. By Renato Losio
Amazon Web Services (AWS) has introduced native vector search capabilities for its DynamoDB database service. This new feature enables developers to store embeddings alongside application data and perform approximate nearest-neighbor queries directly within DynamoDB, eliminating the need for a separate vector database. The introduction of vector search allows for filtered similarity searches and configurable vector indexes, making it suitable for semantic search workloads.
Developers can utilize any embedding model, including Amazon Bedrock Titan Text Embeddings, Cohere Embed, or OpenAI text embedding models, to create vector indexes with the desired dimensions and distance functions. The SearchVectors API is used to query the vector index. Esra Kayabali, a principal solutions architect at AWS, highlights that vector indexes have no storage limits and scale horizontally as data grows.
The native vector search feature simplifies the development of AI-powered applications by keeping vector search and application data in a single place. It removes the need for additional data pipelines, such as copying data to a separate vector database, which was previously required to support vector search. DynamoDB's serverless architecture ensures automatic scaling with no infrastructure management required.
AWS has provided cost implications for the new feature, stating that a vector index is billed based on three dimensions, in addition to the standard DynamoDB charges. Developers can reduce costs by using lower dimensions, minimal index projections, excluding embeddings from results, and selective partitioning.
While some practitioners argue that AWS is late to the vector search bandwagon, as other databases have already shipped vector support, AWS emphasizes the scalability and low latency of its native vector search. Among the positive feedback, many developers appreciate the simplicity and benefits over alternative solutions like S3 vector buckets.
However, there are concerns about the potential cost differences between DynamoDB and S3 for vector search capabilities. AWS plans to offer vector search through the DynamoDB-compatible adapter ExtendDB for local development and self-managed deployments, with the feature currently available in all regions where DynamoDB is available.
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