AWS Reshaping AI Infrastructure: How S3 Vectors Changes the Vector Search Game

Published:
  Lastmod: 2025-07-30
Random Notes > AI
by Ernest Chiang

Post Title Image (Illustration: By deconstructing and reintegrating the world using vectors, we may be able to rediscover a new world. Image source: Photo by James Wainscoat on Unsplash)

✳️ tl;dr for Technical Managers

  • Amazon S3 Vectors integrates native vector search functionality directly into S3 object storage, aiming to simplify architecture and reduce costs.1
  • For RAG, semantic search and other AI applications, AWS claims up to 90% savings in vector storage and query costs.
  • This means we no longer need to maintain a separate, expensive vector database for certain AI scenarios, significantly reducing operational complexity (TCO).2
  • S3 Vectors provides sub-second query performance, suitable for large-scale applications with non-real-time latency requirements.3
  • Core Advantage: Achieves “Storage-Compute Separation” for vector data, maximizing the cost-effectiveness of long-term storage.4
  • Through integration with Amazon OpenSearch, enables “hot-cold” data tiering strategies that balance cost with high-performance query requirements.5
  • Seamless integration with Amazon Bedrock Knowledge Bases makes building and scaling RAG applications unprecedentedly simple.6
  • The emergence of S3 Vectors may force existing vector database vendors (like Pinecone) to rethink their market positioning and pricing strategies.
  • Technical teams now need to reassess existing AI technology stacks to determine which workloads can migrate to S3 Vectors for cost optimization.
  • The feature introduces new Vector Buckets, making vector management as simple as managing regular S3 objects.
  • Speculation: S3 Vectors may further enhance its query capabilities in the future, such as supporting hybrid search to adapt to more complex scenarios.

✳️ tl;dr for Engineers & Developers

  • Amazon S3 now natively supports vector storage and search with the new S3 Vectors feature.1
  • You can directly create Vector Buckets and Vector Indexes in S3, then use APIs to store and query embeddings.
  • For many RAG applications, you can skip the step of deploying and managing a separate Vector DB.7
  • APIs support k-NN similarity search with sub-second response times, supporting distance metrics like Cosine and Euclidean.
  • Development Highlight: Queries can use metadata filters, such as (category = 'ernest-pkm' AND year > 2023), which is very practical.3
  • Seamless integration with Amazon Bedrock Knowledge Bases - once you set up S3 as a data source, Bedrock automatically handles embedding and synchronization.6
  • If your application needs lower latency or more complex searches (like hybrid search), you can export hot vectors to Amazon OpenSearch Service.5
  • For developers already using AWS, the learning curve is low - basically just learning a few more S3 API calls.
  • The entire service is serverless, meaning you don’t need to worry about scaling, provisioning, or aRPU - just focus on customer application scenarios.
  • You can more economically build a massive long-term memory repository for your AI agents, with all interaction records or knowledge vectorized and stored in S3.
  • For massive documents or images stored in S3 Data Lake,4 you can now directly build indexes in-place and perform semantic search without ETL to another system. (Well… if your massive files aren’t in S3 yet… just make a few API calls XD)

✳️ tl;dr for Marketing & Product People

  • Imagine all your company’s documents, customer service conversations, product images, and even videos being searchable using natural language. Amazon S3 Vectors is making this cheaper and simpler.1
  • Previously, only resource-rich companies could afford to build large-scale vector search systems. Now, AWS has built this capability directly into S3, dramatically lowering the technical barriers and costs.
  • Use Cases: Media companies can quickly find relevant clips in PB-scale video libraries; healthcare institutions can identify similar cases among millions of medical images.
  • For e-commerce, this can build more precise semantic search engines that understand what users “want,” not just what they “type.”
  • Business Value: This is not just a technical upgrade, but a key to unlocking the value of enterprise “unstructured data”.4
  • S3 Vectors makes RAG (Retrieval-Augmented Generation) technology more accessible, meaning your chatbots or AI customer service can provide relatively accurate, well-grounded responses.6
  • Market Trend: Vector search is evolving from a niche technology to part of cloud storage infrastructure.
  • This innovation will accelerate AI adoption across industries by solving the fundamental “data preparation” and “knowledge storage” cost problems.
  • For product managers, this means you can be bolder when planning AI features that require massive knowledge bases. (Think of S3 as the master key, like the keymaker in The Matrix.)
  • S3 Vectors’ integration with Amazon Bedrock Knowledge Bases provides a one-stop knowledge base solution.6
  • Speculation: More third-party SaaS applications based on S3 Vectors will emerge, focusing on industry-specific knowledge management and semantic search. This speculation is based on the development patterns of ISVs in the AWS ecosystem.2
  • Enterprises should now consider: What dormant data can be “vectorized” to create new business value? Don’t worry about data formats initially - if you think it’s “data,” try it out, starting with small datasets.

✳️ Knowledge Graph

(More about Knowledge Graph…)

graph LR
    subgraph "Core AI Use Cases"
        RAG["Retrieval-Augmented Generation (RAG)"]
        SemanticSearch["Semantic Search"]
        AIAgentMemory["AI Agent Long-term Memory"]
    end

    subgraph "Vector Data Foundation"
        UnstructuredData["Unstructured Data 
 (text, images, videos)"]
        VectorEmbeddings["Vector Embeddings"]
        SimilaritySearch["Similarity Search 
 (k-NN)"]
        MetadataFiltering["Metadata Filtering"]
    end
    
    subgraph "AWS S3 Vectors Innovation"
        S3["Amazon S3"]
        S3Vectors["Amazon S3 Vectors"]
        VectorBucket["Vector Bucket"]
        VectorIndex["Vector Index"]
        SubSecondQuery["Sub-second Query Performance"]
    end

    subgraph "AWS Ecosystem Integration"
        Bedrock["Amazon Bedrock 
 Knowledge Bases"]
        OpenSearch["Amazon OpenSearch
 Service"]
        SageMaker["Amazon SageMaker"]
        
        HotColdTiering["Hot-Cold Data Tiering"]
        AutoEmbedding["Automated 
 Embedding & Sync"]
    end

    subgraph "Business Benefits"
        CostSaving["90% Cost Reduction 
 vs Traditional Vector DB"]
        ArchitectureSimplification["Simplified Architecture 
 (No separate Vector DB)"]
        ServerlessModel["Serverless Architecture 
 (No Infrastructure Management)"]
        StorageComputeSeparation["Storage-Compute Separation"]
    end

    %% Data Flow
    UnstructuredData --> VectorEmbeddings
    VectorEmbeddings --> SimilaritySearch
    SimilaritySearch --> MetadataFiltering
    
    %% S3 Vectors Core
    S3 --> S3Vectors
    S3Vectors --> VectorBucket
    VectorBucket --> VectorIndex
    VectorIndex --> SubSecondQuery
    VectorEmbeddings --> VectorIndex
    
    %% AI Applications
    RAG --> SimilaritySearch
    SemanticSearch --> SimilaritySearch
    AIAgentMemory --> SimilaritySearch
    
    %% AWS Integration
    S3Vectors --> Bedrock
    S3Vectors --> OpenSearch
    S3Vectors --> SageMaker
    
    Bedrock --> AutoEmbedding
    OpenSearch --> HotColdTiering
    
    %% Benefits Flow
    S3Vectors --> CostSaving
    S3Vectors --> ArchitectureSimplification
    S3Vectors --> ServerlessModel
    S3Vectors --> StorageComputeSeparation
    
    %% Concept Classes (Orange) - Abstract concepts that can have different implementations
    classDef conceptClass fill:#FF8000,stroke:#333,stroke-width:2px,color:#000
    class RAG,SemanticSearch,AIAgentMemory,VectorEmbeddings,SimilaritySearch,MetadataFiltering,HotColdTiering,AutoEmbedding,StorageComputeSeparation conceptClass
    
    %% Instances (Blue) - Concrete implementations or products
    classDef instanceClass fill:#0080FF,stroke:#333,stroke-width:2px,color:#fff
    class S3,S3Vectors,VectorBucket,VectorIndex,Bedrock,OpenSearch,SageMaker,UnstructuredData,SubSecondQuery,CostSaving,ArchitectureSimplification,ServerlessModel instanceClass

✳️ Bottomline

  • Latency Requirements:
    • < 50ms queries → Traditional vector databases;
    • 100-500ms acceptable → S3 Vectors.
  • Data Scale:
    • Under millions of vectors → Dedicated databases more efficient;
    • Tens of millions and above → S3 Vectors cost advantage obvious.
  • Query Complexity:
    • Need hybrid search, complex filtering → Traditional solutions;
    • Simple k-NN similarity search → S3 Vectors suitable.
  • Start testing S3 Vectors with small-scale, non-critical scenarios.
  • Establish clear performance benchmarks and cost targets, evaluate regularly.
  • Prepare rollback plans to ensure business continuity.

✳️ Extended Reading

  1. Introducing Amazon S3 Vectors - AWS News Blog ↩︎ ↩︎ ↩︎

  2. Amazon S3 Vectors Just Killed the Vector Database Market - Medium ↩︎ ↩︎

  3. AWS Introduces Vector Capabilities on Amazon S3 - InfoQ ↩︎ ↩︎

  4. AWS adds vector buckets to S3 to cut RAG storage costs - Blocks & Files ↩︎ ↩︎ ↩︎

  5. Optimizing vector search using Amazon S3 Vectors and Amazon OpenSearch Service - AWS Big Data Blog ↩︎ ↩︎

  6. Building cost-effective RAG applications with Amazon Bedrock Knowledge Bases and Amazon S3 Vectors - AWS Machine Learning Blog ↩︎ ↩︎ ↩︎ ↩︎

  7. Announcing Amazon S3 Vectors (Preview) - AWS What’s New ↩︎