{ "id": "87480c1c-dc07-40b7-a20f-814b0aab76d4", "created_at": "2026-04-06T00:17:38.815614Z", "updated_at": "2026-04-10T13:12:47.31864Z", "deleted_at": null, "sha1_hash": "5436d653144c2d1612ba892a456f87028e142c2d", "title": "Amazon S3 - Cloud Object Storage - AWS", "llm_title": "", "authors": "", "file_creation_date": "0001-01-01T00:00:00Z", "file_modification_date": "0001-01-01T00:00:00Z", "file_size": 5868872, "plain_text": "Amazon S3 - Cloud Object Storage - AWS\r\nArchived: 2026-04-05 14:03:46 UTC\r\nProducts\r\nStorage\r\nAmazon S3\r\nAmazon S3: 20 years of building what’s next\r\nFrom storage for the internet to the data foundation for AI and analytics, Amazon S3 scales and evolves with your\r\nambition\r\nWhat is Amazon S3?\r\nAmazon Simple Storage Service (Amazon S3) is an object storage service offering industry-leading scalability,\r\ndata availability, security, and performance. Millions of customers of all sizes and industries store, manage,\r\nanalyze, and protect any amount of data for virtually any use case, such as data lakes, cloud-native applications,\r\nand mobile apps. With cost-effective storage classes and easy-to-use management features, you can optimize\r\ncosts, organize and analyze data, and configure fine-tuned access controls to meet specific business and\r\ncompliance requirements.\r\nAn error occurred.\r\nUnable to execute JavaScript.\r\nhttps://aws.amazon.com/s3/\r\nPage 1 of 8\n\nVideo Player is loading.\r\nCurrent Time 0:00\r\nDuration 1:31\r\nRemaining Time 1:31\r\nBenefits\r\nAmazon S3: 20 launches that defined 20 years of storage innovation\r\nExplore key S3 launches across architecture, storage classes, security, and management that transformed how\r\ncustomers store, analyze, and build with data.Learn more\r\nDurable storage at scale\r\nAmazon S3 launched on Pi Day, March 14, 2006, as the first generally available AWS service. Amazon S3 gave\r\ndevelopers something new: a simple, durable, and secure-by-default way to store virtually unlimited data, from\r\nanywhere, at any time. Twenty years later, S3 stores hundreds of exabytes of data and more than 500 trillion\r\nobjects and serves millions of customers worldwide. \r\nAs data accumulated faster than enterprises could afford to keep it, S3 Glacier became the cloud on-ramp for\r\nfinancial services, healthcare, and media. S3 Glacier Deep Archive pushed costs lower still, and S3 Glacier Instant\r\nRetrieval modernized archival storage by combining archive pricing with instant access.\r\nData foundation for AI\r\nhttps://aws.amazon.com/s3/\r\nPage 2 of 8\n\nS3 became the foundation of modern data lakes, enabling any organization to centralize structured and\r\nunstructured data, run analytics, and build applications without managing complex infrastructure. S3 Intelligent-Tiering rewrote the economics of storage, saving S3 customers more than $6 billion. S3 didn't just scale with\r\nindustry shifts—it helped accelerate them.\r\nAs AI training and generative AI raised the bar for what storage needed to deliver, S3 evolved. S3 Express One\r\nZone is the fastest cloud object storage class, S3 Tables deliver native Apache Iceberg support, and S3 Vectors\r\nprovides native vector storage and query for AI applications.\r\nWhat began as storage for the internet is now the data foundation the world runs on.\r\nAmazon S3 today\r\n500T+\r\nobjects and hundreds of exabytes of data\r\n200M+\r\nrequests per second on average\r\n300B+\r\nevent notifications sent to serverless applications daily\r\n$6B+\r\nsaved in storage costs with S3 Intelligent-Tiering\r\nUse cases\r\nBuild an open data architecture for analytics and AI\r\nMore than 1,000,000 data lakes run on AWS. Whether you’re building a data lake or lakehouse architecture,\r\nAmazon S3 provides the ideal foundation for petabyte-scale analytics and AI workloads because of its unmatched\r\ndurability, availability, scalability, security, and price performance. S3 supports open table formats like Apache\r\nIceberg, allowing you to run queries directly against a single, reliable copy of data using your preferred AWS or\r\nthird-party analytics engine. For a fully managed experience, S3 Tables automatically handles compaction,\r\nsnapshot management, and file cleanup of Apache Iceberg tables, reducing the operational burden of managing a\r\ndata lakehouse and scaling performance as your data volumes grow.\r\nhttps://aws.amazon.com/s3/\r\nPage 3 of 8\n\nAccelerate performance-critical applications\r\nBuilding on the scale and durability of Amazon S3, the S3 Express One Zone storage class accelerates\r\nperformance-intensive workloads with consistent single-digit millisecond latency and up to 10x faster data access\r\nthan the S3 Standard storage class. It’s ideal for high-throughput workloads such as AI training and inference,\r\nreal-time analytics, media processing, and interactive applications. As the fastest cloud object storage, S3 Express\r\nOne Zone delivers faster access to the most frequently accessed dataset, improves compute efficiency, and lowers\r\nAPI request costs, reducing the total cost of ownership for the most demanding workloads.\r\nhttps://aws.amazon.com/s3/\r\nPage 4 of 8\n\nScale and differentiate generative AI and agentic applications\r\nBuild generative AI and agentic applications on Amazon S3, the durable and scalable foundation for your data.\r\nAccess diverse data types at scale—including unstructured, structured, streaming, and vector data—to train, fine-tune, and customize models or improve contextual understanding through RAG. With direct integrations across\r\nAWS analytics and AI/ML services, S3 accelerates the path from data to intelligence without managing complex\r\ninfrastructure.\r\nS3 simplifies data pipelines, protects critical information with security by default, and delivers cost-efficient\r\nscalability so you can support evolving AI workloads. Whether you’re developing domain-specific assistants,\r\nintelligent agents, or personalized generative AI experiences, S3 provides the trusted data foundation to build,\r\ncustomize, and deploy quickly and efficiently.\r\nhttps://aws.amazon.com/s3/\r\nPage 5 of 8\n\nOptimize vector stroage and query costs for AI and semantic search\r\nSemantic search enables AI applications to understand the meaning and context of data by using vector\r\nembeddings to represent relationships across content such as documents, images, and videos. Amazon S3 Vectors\r\nbrings native vector support to S3, allowing you to store and query vectors alongside your source data in a fully\r\nserverless architecture. By reducing the cost of storing and querying vectors by up to 90% while maintaining\r\nsubsecond query performance, S3 Vectors makes it cost-effective to create and use large vector datasets to\r\nimprove the memory and context of AI agents as well as conduct semantic search results of your S3 data. With S3\r\nVectors, developers can get started quickly, reduce operational complexity, and scale vector-driven semantic\r\nsearch and AI workloads efficiently on a durable, trusted foundation.\r\nhttps://aws.amazon.com/s3/\r\nPage 6 of 8\n\nBack up, restore, and archive data while meeting compliance requirements\r\nMeet your recovery time objective (RTO), recovery point objective (RPO), and compliance requirements with the\r\nrobust replication functionality of S3, data protection with AWS Backup, and various AWS Partner Network\r\nsolutions. For cost-optimized, long-term data storage, archive data using Amazon S3 Glacier storage classes to\r\nlower costs, eliminate operational complexities, and gain new insights.\r\nhttps://aws.amazon.com/s3/\r\nPage 7 of 8\n\nCustomers\r\nGet started\r\nSource: https://aws.amazon.com/s3/\r\nhttps://aws.amazon.com/s3/\r\nPage 8 of 8", "extraction_quality": 1, "language": "EN", "sources": [ "MITRE" ], "origins": [ "web" ], "references": [ "https://aws.amazon.com/s3/" ], "report_names": [ "s3" ], "threat_actors": [], "ts_created_at": 1775434658, "ts_updated_at": 1775826767, "ts_creation_date": 0, "ts_modification_date": 0, "files": { "pdf": "https://archive.orkl.eu/5436d653144c2d1612ba892a456f87028e142c2d.pdf", "text": "https://archive.orkl.eu/5436d653144c2d1612ba892a456f87028e142c2d.txt", "img": "https://archive.orkl.eu/5436d653144c2d1612ba892a456f87028e142c2d.jpg" } }