Amazon DynamoDB Announces Native Vector Search General Availability

August 9, 2026 Rachel Kim – Technology Editor Technology

Amazon DynamoDB Vector Search Launches for Real-Time AI Workloads

Amazon Web Services announced the general availability of native vector search in Amazon DynamoDB, allowing engineering teams to store vector embeddings alongside operational data and execute similarity searches directly without maintaining a separate vector database.

The Tech TL;DR:

  • Native Storage: Store vector embeddings as list data types directly inside standard DynamoDB tables alongside existing operational attributes.
  • Performance: Achieve single-digit millisecond latency with 99%+ recall without provisioning separate dedicated vector databases.
  • Architectural Simplicity: Eliminate complex synchronization pipelines, data movement costs, and external licensing overhead using built-in serverless pay-per-request pricing.

Architectural Implications for Serverless AI Pipelines

Building high-throughput retrieval-augmented generation (RAG) and agentic memory systems traditionally forces engineering teams into a persistent operational compromise. Historically, pairing a fast operational key-value store like DynamoDB with vector workloads required spinning up a secondary database, writing custom synchronization code, and managing eventual consistency gaps across independent services. According to AWS documentation, the new integration introduces a dedicated vector index type that shares the same serverless infrastructure and pricing model as standard tables.

“Eliminating the synchronization tier changes the math for enterprise machine learning pipelines,” notes an enterprise software architect monitoring modern backend patterns. “When your operational database handles semantic similarity natively, you drop an entire category of infrastructure failure points.” When organizations scale beyond standard internal capabilities, engineering leads frequently partner with specialized software engineering agencies to refactor legacy data models and optimize query patterns for serverless environments.

Implementation and Index Configuration

Developers can generate embeddings using models like Amazon Bedrock Titan Text Embeddings, Cohere Embed, or OpenAI text embedding models, and store them via a standard PutItem call. DynamoDB accommodates vector embeddings using its existing List data type, where each element is a Number representing a single float value.

To implement semantic search on an existing table, developers create a vector index specifying dimensions, distance functions, and optional non-vector attributes for inline filtering. The following configuration demonstrates creating a vector index and querying it via the API:

// Example AWS CLI command to create a vector index on an existing DynamoDB table
aws dynamodb update-table 
    --table-name ProductCatalog 
    --attribute-definitions AttributeName=descriptionEmbedding,AttributeType=L 
    --global-secondary-index-updates 
        "[{"Create": {"IndexName": "ProductDescriptionIndex", "KeySchema": [{"AttributeName": "marketplace", "KeyType": "HASH"}], "Projection": {"ProjectionType": "ALL"}, "VectorConfig": {"Dimensions": 1536, "DistanceFunction": "COSINE"}}}]"

Once the index status transitions to active, the SearchVectors API accepts a query vector, a top-K result limit up to 100, and inline filter conditions. Filtering supports exact-match parameters such as product categories, restricting search scope efficiently within partition keys.

Distance Functions and Scale Constraints

Amazon DynamoDB vector search supports up to 4096 dimensions across three primary distance functions: Cosine, Euclidean, and Dot Product. Selecting the appropriate function depends strictly on the underlying model training parameters. Cosine measures angular similarity, making it optimal for normalized text embeddings. Euclidean suits applications where vector magnitude carries meaning, while Dot Product aligns with recommendation engines balancing preference frequency and interest weighting.

Kiro Crew, DynamoDB Vector Search & Cheap Open-Weight Models

For organizations navigating complex cloud migrations or tuning throughput limits, engaging vetted cloud architecture consultants ensures that partition keys and throughput consumption remain optimized as data volumes scale horizontally.

Future Trajectory of Operational Vector Stores

By baking vector indexing directly into managed NoSQL infrastructure, AWS removes the friction of managing dedicated vector infrastructure. Engineering teams can now deploy semantic search capabilities with zero maintenance windows, zero downtime maintenance, and automatic horizontal scaling.

Disclaimer: The technical analyses and security protocols detailed in this article are for informational purposes only. Always consult with certified IT and cybersecurity professionals before altering enterprise networks or handling sensitive data.


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