{"slug": "aws-introduces-native-vector-search-for-dynamodb", "title": "AWS Introduces Native Vector Search for DynamoDB", "summary": "Amazon Web Services (AWS) has introduced native vector search for Amazon DynamoDB, enabling developers to store embeddings and run approximate nearest-neighbor queries directly in the NoSQL database without a separate vector database. The feature supports up to 4096 dimensions, Euclidean, Cosine, and Dot product distance functions, and inline filtering, with billing based on data written, processed, and stored in the vector index. AWS principal solutions architect Esra Kayabali stated that vector indexes have no storage limits and scale horizontally, supporting applications like agentic memory and retrieval augmented generation.", "body_md": "[Amazon DynamoDB recently introduced native vector search](https://aws.amazon.com/about-aws/whats-new/2026/08/amazon-dynamodb-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.\n\n[Vector search](https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/VectorSearch.html) uses a new DynamoDB index type built on vector embeddings stored in table attributes. Developers can choose any embedding model, such as Amazon Bedrock Titan Text Embeddings, Cohere Embed, or OpenAI text embedding models, create a vector index with the required dimensions and distance function, and query it using the new `SearchVectors`\n\nAPI. [Esra Kayabali](https://www.linkedin.com/in/esrakayabali/), principal solutions architect at AWS, [writes](https://aws.amazon.com/blogs/aws/amazon-dynamodb-now-supports-real-time-vector-search-at-any-scale/):\n\nVector 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.\n\nUntil now, applications using the managed NoSQL database have had to copy data to a separate vector database and keep the two systems synchronized to support vector search, increasing architectural complexity and data transfer. Native vector search removes that extra data pipeline by allowing vector embeddings and application data to be stored and queried in the same DynamoDB table. Kayabali adds:\n\nDynamoDB 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.\n\nIn the article \"[Build semantic search with native vector support in Amazon DynamoDB](https://aws.amazon.com/blogs/database/build-semantic-search-with-native-vector-support-in-amazon-dynamodb/),\" [Leonid Koren](https://www.linkedin.com/in/leonid-koren/), principal NoSQL specialist solutions architect at AWS, and [Mo Kamioner](https://www.linkedin.com/in/mo-kamioner/), senior DynamoDB solutions architect at AWS, show how to build a Python semantic search app that uses Bedrock embeddings and DynamoDB to find relevant research papers by meaning, rather than exact keywords. They explain the cost implications of the new feature:\n\nA vector index is billed on three dimensions, in addition to the standard DynamoDB charges for the underlying table that holds your items. You pay for the data you write into the index, the data processed when you search, and the data you store. All three are metered per byte and billed per GB.\n\nAccording to the authors, using lower dimensions, minimal index projections, excluding embeddings from results, and selective partitioning are the main techniques that can significantly reduce vector search costs. Jeff Barr, VP and chief evangelist at AWS, [highlights](https://www.linkedin.com/feed/update/urn:li:activity:7490833549747589121/) on LinkedIn:\n\nThis scales as big as you want it (think trillions of vectors) while maintaining single-digit millisecond latency.\n\nWith many databases having already shipped vector support over the last couple of years, some practitioners argue that AWS is \"[too late to the party](https://www.linkedin.com/feed/update/urn:li:activity:7490831830942846976/?dashCommentUrn=urn%3Ali%3Afsd_comment%3A%287490840903767584769%2Curn%3Ali%3Aactivity%3A7490831830942846976%29).\" Humayun Khan, however, comments:\n\nThis is a really exciting addition. Native vector search in DynamoDB could make building AI powered applications much simpler by keeping vector search and application data in one place.\n\nSome developers wonder whether [DynamoDB applies attribute filters before or after](https://www.reddit.com/r/vectordatabase/comments/1vga4vy/amazon_dynamodb_now_supports_realtime_vector/) vector search, but the feature received [mostly positive feedback](https://www.reddit.com/r/aws/comments/1vgmwid/amazon_dynamodb_now_supports_realtime_vector/) from the community, with many highlighting its benefits over [S3 vector buckets](https://www.infoq.com/news/2026/01/aws-s3-vectors-ga/). User *coinclick* [warns](https://www.reddit.com/r/aws/comments/1vgmwid/comment/p23g40k/?utm_source=share&utm_medium=web3x&utm_name=web3xcss&utm_term=1&utm_content=share_button):\n\nS3 has limitless scale and will provide a consistent latency, even if the latency isn't great. I think DynamoDB will generally scale out pretty well too, with very low latency, but will probably be a lot more expensive than S3.\n\nThe team plans to ship vector search through the DynamoDB-compatible adapter ExtendDB for local development and self-managed deployments. Vector indexes are available in all regions where DynamoDB is currently available and with tables that use either the Standard or Standard-IA table class.", "url": "https://wpnews.pro/news/aws-introduces-native-vector-search-for-dynamodb", "canonical_source": "https://www.infoq.com/news/2026/08/aws-dynamodb-vector-search/?utm_campaign=infoq_content&utm_source=infoq&utm_medium=feed&utm_term=global", "published_at": "2026-08-16 07:21:00+00:00", "updated_at": "2026-08-16 07:41:29.888178+00:00", "lang": "en", "topics": ["artificial-intelligence"], "entities": ["Amazon Web Services", "Amazon DynamoDB", "Esra Kayabali", "Leonid Koren", "Mo Kamioner", "Jeff Barr", "Humayun Khan", "Amazon Bedrock"], "alternates": {"html": "https://wpnews.pro/news/aws-introduces-native-vector-search-for-dynamodb", "markdown": "https://wpnews.pro/news/aws-introduces-native-vector-search-for-dynamodb.md", "text": "https://wpnews.pro/news/aws-introduces-native-vector-search-for-dynamodb.txt", "jsonld": "https://wpnews.pro/news/aws-introduces-native-vector-search-for-dynamodb.jsonld"}}