**Amazon DynamoDB Now Offers Native Vector Search – Here’s How It Works**
Today, Amazon Web Services (AWS) announced the general availability of vector search directly in **Amazon DynamoDB**. This new capability allows developers to store vector embeddings alongside their operational data and perform similarity searches without needing a separate vector database.
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### What Is Vector Search in DynamoDB?
Vector search enables you to find items based on semantic similarity rather than exact matches. For example, you can search for products, documents, or records that are conceptually similar to a query, even if the wording differs.
DynamoDB’s vector search is fully serverless, supports single-digit millisecond latency at 99%+ recall, and scales to trillions of vectors without any infrastructure management. There are no servers to provision, no software to maintain, and no downtime.
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### Key Features
– Native vector search built directly into DynamoDB
– No need for a separate vector store or synchronization pipeline
– Pay-per-request pricing with serverless scaling
– Supports up to 4096 dimensions
– Works with Euclidean, Cosine, and Dot product distance functions
– Allows inline filtering on non-vector attributes
– Indexes scale horizontally as your data grows
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### How to Use Vector Search: A Step-by-Step Walkthrough
Amazon’s guide walks through adding semantic search to a product catalog in DynamoDB:
1. **Prepare your DynamoDB table**
Generate vector embeddings for your data (for example, using Amazon Bedrock Titan Text Embeddings, Cohere, or OpenAI). Store these embeddings as a list of floats in a new attribute.
2. **Create a vector index**
In the DynamoDB console, define the vector attribute, choose a distance function (Cosine is common for text similarity), and optionally specify a partition key and filter attributes.
3. **Run vector searches**
Submit a query vector (e.g., from a natural language search) and receive ranked results based on similarity. You can also filter results by attributes such as category or marketplace.
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### Use Cases
– Semantic product search
– Retrieval-augmented generation (RAG)
– Personalized recommendations
– Anomaly detection
– Agentic memory and contextual retrieval
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### Frequently Asked Questions (FAQ)
**Q: Do I need to migrate my data to a new database?**
A: No. Vector search is added directly to your existing DynamoDB tables. You can store vectors alongside your current data without replication.
**Q: Which embedding models are supported?**
A: You can use embeddings from Amazon Bedrock Titan Text, Cohere, OpenAI, or any other model that outputs a list of floats.
**Q: Are there any limitations on vector dimensions?**
A: DynamoDB supports vector embeddings with up to 4096 dimensions.
**Q: Can I filter search results?**
A: Yes. You can specify non-vector attributes as filters to narrow results at query time. Only exact-match filtering is supported.
**Q: Is there additional cost for vector search?**
A: Vector search uses the same pay-per-request pricing model as DynamoDB, with no extra licensing or infrastructure charges.
**Q: Does vector search impact table performance?**
A: No. Vector indexes operate independently and scale horizontally, so they do not affect the performance of your base table.
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### Conclusion
Amazon DynamoDB’s native vector search removes the complexity and cost of managing multiple databases for operational and AI workloads. By bringing similarity search directly into DynamoDB, AWS enables developers to build intelligent, real-time applications with seamless scalability and serverless convenience. Whether you’re enhancing recommendation engines, building semantic search, or advancing generative AI workflows, DynamoDB vector search offers a powerful and unified data platform.
