Today, we’re announcing the general availability of vector search in Amazon DynamoDB . You can now store vector embeddings alongside your operational data in DynamoDB and run similarity searches directly against that data, without replicating it to a separate vector store. DynamoDB supports native vector search with single-digit millisecond latency at 99%+ recall, and is designed for any scale, even trillions of vectors.
There are no servers to provision, patch, or manage, and no software to install, maintain, or operate. The service has no versions, no maintenance windows, and zero downtime maintenance. Vector indexes have no storage limits and scale horizontally as your data grows.
You can now build applications that require semantic retrieval on agentic memory, retrieval augmented generation, recommendation engines, personalized experiences, anomaly detection, and more using DynamoDB and its native vector search. If your application already uses DynamoDB, adding vector search previously required copying data into a dedicated vector database while maintaining a synchronization pipeline between the two services.
This added operational overhead, data movement costs, licensing costs, and the challenge of maintaining predictable low latency at scale. With vector search built into DynamoDB, your vectors and operational data share the same serverless infrastructure and the same pay-per-request pricing model. Vector search in DynamoDB introduces a new index type that you create on an attribute storing vector embeddings.
You generate embeddings using a model of your choice, such as Amazon Bedrock Titan Text Embeddings, Cohere Embed, or OpenAI text embedding models, and store them as a list of floats in your table using a standard PutItem call. You then create a vector index on that attribute and specify the number of dimensions, the distance function, and any non-vector attributes you want to use as filters to narrow search results at query time.
The SearchVectors API accepts a query vector, the number of results to return (up to 100), and optional filter conditions. It returns results ranked by similarity. Use vector search in DynamoDB when your operational data already lives in DynamoDB and you want to add similarity search without provisioning a separate database or managing a synchronization pipeline.
DynamoDB is fully serverless, so vector search scales automatically with no infrastructure to manage. It supports up to 4096 dimensions, Euclidean, Cosine, and Dot product distance functions, and inline filtering. Getting started with vector search in DynamoDB This walkthrough shows how to add vector search to an existing DynamoDB table using the DynamoDB console .
The scenario contains an online sporting goods store with a product catalog table. Each item has standard operational attributes such as productId , category , description , marketplace , name , and price . The goal is to add semantic search so shoppers can find products using natural language queries rather than exact keyword matches.
- Prepare DynamoDB table To enable semantic search, I first generate vector embeddings for the product descriptions already in my table. Embeddings are numerical representations of text generated by a machine learning model that capture the meaning of the content.
Two items with similar descriptions will have embeddings that are close to each other in vector space, which is what makes similarity search possible. I can generate embeddings using Amazon Bedrock Titan Text Embeddings or another embedding model, then add them to my table using the AWS Management Console , AWS Command Line Interface (AWS CLI) , AWS SDKs , AWS CloudFormation , or other infrastructure-as-code (IaC) tools.
For an existing table like ProductCatalog , I add the embeddings to each item as a new attribute named descriptionEmbedding using an UpdateItem call. DynamoDB stores vector embeddings using its existing List data type. Each element in the list is a Number that represents a single float value of the embedding vector.
This means I do not need a new data type or schema change to start storing vectors alongside my existing operational attributes. 2. Create vector index In the DynamoDB console , open the ProductCatalog table and choose the Indexes tab.
I choose Create vector index . On the Create vector index page, I fill in the index details as follows. I enter ProductDescriptionIndex as the Index name and descriptionEmbedding as the Vector attribute .
I enter the number of Dimensions that matches my embedding model’s output and select Cosine as the Distance function . Cosine measures the angle between vectors rather than their magnitude, which makes it effective for comparing semantic similarity of text embeddings. Vector search in DynamoDB also supports Euclidean and Dot product distance functions.
Euclidean : Use when the magnitude of the vectors is meaningful, such as clustering items by a numeric value like purchase count. Dot product : Use when both direction and magnitude matter, such as in recommendation systems that weight interest alignment and frequency together. As a general rule, match the distance function to the one used to train your embedding model for the best accuracy.
I enter marketplace as the Partition key . The vector index partition key controls how DynamoDB distributes vectors across partitions, allowing the index to scale out while maintaining predictable latencies. Each search is scoped to a single partition key value, so a product catalog serving multiple marketplaces can search within one marketplace’s inventory without scanning the entire index.
The partition key is optional, but recommended for large datasets with high query throughput. I expand Inline filter attributes and add category as a filter attribute. This helps me narrow search results to a specific product category at query time.
Originally published at aws.amazon.com
