--- title: Introducing Amazon S3 Vectors: Reducing Storage Costs... description: AWS launched Amazon S3 Vectors, a new cloud storage service optimized for AI workloads that provides native vector support with up to 90% cost reduction... canonical: https://daily.dev/posts/introducing-amazon-s3-vectors-reducing-storage-costs-for-llm-embeddings-kcjllw5wo twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Introducing Amazon S3 Vectors: Reducing Storage Costs for LLM Embeddings | daily.dev og:description: AWS launched Amazon S3 Vectors, a new cloud storage service optimized for AI workloads that provides native vector support with up to 90% cost reduction... og:url: https://daily.dev/posts/introducing-amazon-s3-vectors-reducing-storage-costs-for-llm-embeddings-kcjllw5wo og:image: https://api.daily.dev/og/posts/kcjllw5wO.png og:image:alt: Introducing Amazon S3 Vectors: Reducing Storage Costs for LLM Embeddings og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Introducing Amazon S3 Vectors: Reducing Storage Costs for LLM Embeddings **[Collections](https://daily.dev/sources/collections)** · 2 min read · 3 upvotes · 0 comments ## Summary AWS launched Amazon S3 Vectors, a new cloud storage service optimized for AI workloads that provides native vector support with up to 90% cost reduction compared to traditional vector databases. The service offers specialized vector buckets with dedicated APIs for storing and querying vector embeddings, supporting up to 10,000 vector indexes per bucket with millions of vectors each. Performance is optimized for hard drive storage with query times in the low hundreds of milliseconds, making it suitable for chatbots and search engines but less ideal for high-frequency applications. The service integrates seamlessly with AWS services like Bedrock, SageMaker, and OpenSearch, enabling enhanced RAG application development and flexible tiered storage strategies. ## Content # Amazon S3 Vectors: Optimizing AI Storage with Cost-Efficient Vector Buckets AWS has announced the launch of Amazon S3 Vectors, a breakthrough in cloud storage optimized for AI workloads. The service provides native vector support at scale, significantly reducing costs by up to 90% compared to traditional vector database solutions. ## Key Features Amazon S3 Vectors introduces specialized vector buckets with dedicated APIs, designed to store and query vector embeddings efficiently without the need for infrastructure provisioning. This innovation is tailored for hard drive storage, achieving performance in the low hundreds of milliseconds per query, making it suitable for applications such as chatbots and search engines, though less ideal for high-frequency queries that require in-memory or SSD speeds. ### Performance and Cost Optimization The vector buckets support advanced AI functionalities including similarity search and nearest neighbor queries. Each bucket can handle up to 10,000 vector indexes, with tens of millions of vectors each. The service automatically optimizes for both performance and cost, offering a practical solution for managing extensive AI workloads. ### Integration and Use Cases Amazon S3 Vectors seamlessly integrates with other AWS services like Amazon Bedrock, SageMaker Unified Studio, and OpenSearch Service. These integrations enhance RAG application development and facilitate flexible storage strategies for various access patterns. The tiered storage approach allows users to implement cost-effective and scalable solutions for AI-driven applications. ### Conclusion With Amazon S3 Vectors, AWS aims to redefine vector storage, providing developers and businesses with a robust and economical option for managing AI data. This innovative service leverages AWS's extensive cloud infrastructure to deliver efficient storage solutions, bringing down costs and complexity associated with AI data handling. ## Similar posts on daily.dev - [Don’t just attend KubeCon \+ CloudNativeCon, Merge Forward your experience\!](https://daily.dev/posts/don-t-just-attend-kubecon-cloudnativecon-merge-forward-your-experience--l0rpp73x8) · CNCF · 1 upvotes · 0 comments - [Announcing H2 2026 KCDs](https://daily.dev/posts/announcing-h2-2026-kcds-m96goajm1) · CNCF · 1 upvotes · 0 comments - [Two months of Open Community Groups](https://daily.dev/posts/two-months-of-open-community-groups-asf52zhbs) · CNCF · 0 upvotes · 0 comments - [CNCF Unveils Schedule for KubeCon \+ CloudNativeCon Europe 2026](https://daily.dev/posts/cncf-unveils-schedule-for-kubecon-cloudnativecon-europe-2026-ikhcoa5cb) · CNCF · 2 upvotes · 0 comments - [CNCF Debuts KubeCon \+ CloudNativeCon Japan 2026 Schedule](https://daily.dev/posts/cncf-debuts-kubecon-cloudnativecon-japan-2026-schedule-xp5pyudub) · CNCF · 1 upvotes · 0 comments --- Tags: [#machine-learning](https://daily.dev/tags/machine-learning), [#aws](https://daily.dev/tags/aws), [#llm](https://daily.dev/tags/llm), [#vector-search](https://daily.dev/tags/vector-search) [View this post on daily.dev](https://daily.dev/posts/introducing-amazon-s3-vectors-reducing-storage-costs-for-llm-embeddings-kcjllw5wo) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"Introducing Amazon S3 Vectors: Reducing Storage Costs for LLM Embeddings","url":"https://daily.dev/posts/introducing-amazon-s3-vectors-reducing-storage-costs-for-llm-embeddings-kcjllw5wo","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/introducing-amazon-s3-vectors-reducing-storage-costs-for-llm-embeddings-kcjllw5wo"},"datePublished":"2025-07-16T14:51:27.910Z","dateModified":"2025-07-17T06:39:29.434Z","description":"AWS launched Amazon S3 Vectors, a new cloud storage service optimized for AI workloads that provides native vector support with up to 90% cost reduction...","image":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/a7b0cc69a519262b1f617edc2f0a4f71?_a=AQAEulh","thumbnailUrl":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/a7b0cc69a519262b1f617edc2f0a4f71?_a=AQAEulh","isAccessibleForFree":true,"articleSection":"Collections","inLanguage":"en","publisher":{"@type":"Organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180}},"author":{"@type":"Organization","name":"Collections","logo":"https://media.daily.dev/image/upload/s--fk_6ycEi--/f_auto,q_auto/v1780996001/logos/collections?_a=BAMAMiWQ0","url":"https://daily.dev/sources/collections"},"commentCount":0,"discussionUrl":"https://daily.dev/posts/introducing-amazon-s3-vectors-reducing-storage-costs-for-llm-embeddings-kcjllw5wo","interactionStatistic":[{"@type":"InteractionCounter","interactionType":{"@type":"LikeAction"},"userInteractionCount":3},{"@type":"InteractionCounter","interactionType":{"@type":"CommentAction"},"userInteractionCount":0}],"keywords":"machine-learning,aws,llm,vector-search","timeRequired":"PT2M"} {"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"Collections","item":"https://daily.dev/sources/collections"},{"@type":"ListItem","position":3,"name":"Introducing Amazon S3 Vectors: Reducing Storage Costs for LLM Embeddings"}]} ```