Mountpoint for Amazon S3 can now limit memory usage, either automatically based on the environment it runs in or with a limit that you define. This lets you run Mountpoint alongside memory-intensive applications, for example, in machine learning training or analytics workloads where applications share a memory budget. Previously, Mountpoint’s memory usage could expand over time based on usage patterns, potentially causing performance or stability issues when competing with other memory-intensive applications. With this launch, you can define a memory target for Mountpoint to reserve memory for your applications. Alternatively, Mountpoint can automatically determine a safe default based on the environment it runs in. For example, when running in Amazon EKS, Mountpoint can automatically detect a container's assigned memory budget. Under memory pressure, Mountpoint slows down operations to stay within this budget, enabling you to run Mountpoint in containers with strict memory allocations.   Mountpoint is available in all AWS Regions. To upgrade to the latest version, visit the Mountpoint GitHub repository. To learn more about Mountpoint, see the overview page and the configuration guide in the Mountpoint GitHub repository.

Mountpoint for Amazon S3 can now limit memory usage, either automatically based on the environment it runs in or with a limit that you define. This lets you run Mountpoint alongside memory-intensive applications, for example, in machine learning training or analytics workloads where applications share a memory budget. Previously, Mountpoint’s memory usage could expand over time based on usage patterns, potentially causing performance or stability issues when competing with other memory-intensive applications. With this launch, you can define a memory target for Mountpoint to reserve memory for your applications. Alternatively, Mountpoint can automatically determine a safe default based on the environment it runs in. For example, when running in Amazon EKS, Mountpoint can automatically detect a container's assigned memory budget. Under memory pressure, Mountpoint slows down operations to stay within this budget, enabling you to run Mountpoint in containers with strict memory allocations.   Mountpoint is available in all AWS Regions. To upgrade to the latest version, visit the https://aws.amazon.com/s3/features/mountpoint/. To learn more about Mountpoint, see the https://github.com/awslabs/mountpoint-s3/blob/main/doc/CONFIGURATION.md and the https://github.com/awslabs/mountpoint-s3/blob/main/doc/CONFIGURATION.md in the Mountpoint GitHub repository.

Amazon S3 Vectors is now available in the AWS European Sovereign Cloud (Germany) Region. Amazon S3 Vectors is purpose-built vector storage for AI agents, inference, Retrieval Augmented Generation (RAG), and semantic search at billion-vector scale. S3 Vectors is designed to provide the same elasticity, durability, and availability as Amazon S3, with a dedicated set of APIs that let you store, access, and query vectors without provisioning any infrastructure. For a full list of AWS Regions where Amazon S3 Vectors is available, see AWS Regions and endpoints. To learn more, visit the product page, documentation, and the Amazon S3 pricing page.

Amazon S3 Vectors is now available in the AWS European Sovereign Cloud (Germany) Region. Amazon S3 Vectors is purpose-built vector storage for AI agents, inference, Retrieval Augmented Generation (RAG), and semantic search at billion-vector scale. S3 Vectors is designed to provide the same elasticity, durability, and availability as Amazon S3, with a dedicated set of APIs that let you store, access, and query vectors without provisioning any infrastructure. For a full list of AWS Regions where Amazon S3 Vectors is available, see https://docs.aws.amazon.com/AmazonS3/latest/userguide/s3-vectors-regions-quotas.html. To learn more, visit the https://aws.amazon.com/s3/features/vectors/, https://docs.aws.amazon.com/AmazonS3/latest/userguide/s3-vectors.html, and the https://aws.amazon.com/s3/pricing/.

Amazon S3 Tables now support the Variant data type as defined in the Apache Iceberg Version 3 (V3) specification. You can now write semi-structured data like JSON directly to S3 Tables without defining a fixed schema in advance, allowing you to land data faster while still getting efficient analytical query performance. With the Variant data type, Apache Iceberg V3-compatible engines shred your semi-structured data into hidden columns as you write it, generating Parquet column statistics that query engines use for optimizations like file pruning, which reduces the data your analytical queries scan. S3 Tables also provide ongoing table maintenance, including compaction, for Variant columns, so you can consolidate data from small files into large files that Iceberg engines can read. Variant support in S3 Tables is available in the following AWS Regions: US East (N. Virginia), US East (Ohio), US West (Oregon), Asia Pacific (Mumbai), Asia Pacific (Seoul), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), Canada (Central), Europe (Frankfurt), Europe (Ireland), Europe (London), Europe (Paris), Europe (Stockholm), and South America (São Paulo). To learn more, see Amazon S3 Tables and the AWS Prescriptive Guidance for working with Iceberg table format specification version 3.

Amazon S3 Tables now support the Variant data type as defined in the Apache Iceberg Version 3 (V3) specification. You can now write semi-structured data like JSON directly to S3 Tables without defining a fixed schema in advance, allowing you to land data faster while still getting efficient analytical query performance. With the Variant data type, Apache Iceberg V3-compatible engines shred your semi-structured data into hidden columns as you write it, generating Parquet column statistics that query engines use for optimizations like file pruning, which reduces the data your analytical queries scan. S3 Tables also provide ongoing table maintenance, including compaction, for Variant columns, so you can consolidate data from small files into large files that Iceberg engines can read. Variant support in S3 Tables is available in the following AWS Regions: US East (N. Virginia), US East (Ohio), US West (Oregon), Asia Pacific (Mumbai), Asia Pacific (Seoul), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), Canada (Central), Europe (Frankfurt), Europe (Ireland), Europe (London), Europe (Paris), Europe (Stockholm), and South America (São Paulo). To learn more, see https://aws.amazon.com/s3/features/tables/ and the https://docs.aws.amazon.com/prescriptive-guidance/latest/apache-iceberg-on-aws/table-spec-v3.html.

Last week, my team visited Seoul to meet AWS Korea User Group (AWSKRUG) leaders. AWSKRUG is the largest cloud developer community in Korea, with 20 meetup groups organized by topic and area that collectively host over 100 events each year, primarily in Seoul. My team regularly visits countries across the Asia-Pacific region, listens to feedback […]

Amazon S3 Event Notifications now include system-generated tags in events delivered to all destinations including Amazon EventBridge, Amazon SQS, Amazon SNS, and AWS Lambda. System-generated tags are metadata labels attached to your bucket by AWS services. You can use these tags to filter events from thousands of buckets with a single EventBridge rule, instead of listing each bucket name individually. To get started, enable S3 Event Notifications on your general purpose buckets through the AWS Management Console, AWS SDK, or AWS CLI. If AWS services like AWS CloudFormation have already applied system-generated tags to your buckets, S3 automatically includes them in new event notifications. System-generated tags in S3 Event Notifications are available at no additional cost in all AWS Regions and require no changes to existing configurations. To learn more, visit the S3 Event Notifications documentation.

Amazon S3 Event Notifications now include system-generated tags in events delivered to all destinations including Amazon EventBridge, Amazon SQS, Amazon SNS, and AWS Lambda. System-generated tags are metadata labels attached to your bucket by AWS services. You can use these tags to filter events from thousands of buckets with a single EventBridge rule, instead of listing each bucket name individually. To get started, enable S3 Event Notifications on your general purpose buckets through the AWS Management Console, AWS SDK, or AWS CLI. If AWS services like AWS CloudFormation have already applied system-generated tags to your buckets, S3 automatically includes them in new event notifications. System-generated tags in S3 Event Notifications are available at no additional cost in all AWS Regions and require no changes to existing configurations. To learn more, visit the https://docs.aws.amazon.com/AmazonS3/latest/userguide/notification-content-structure.html.

Amazon S3 now supports delivering server access logs to Amazon CloudWatch Logs, giving you instant querying, alarms, cross-account and cross-Region aggregation, and AWS Key Management Service (KMS) encryption for your access log data. You can also mirror your logs to Amazon S3 Tables in Apache Iceberg format at no additional storage cost. These new delivery paths complement the existing free delivery of server access logs to S3 general purpose buckets, giving you more flexibility in how you monitor and analyze access to your data. With delivery to CloudWatch Logs, you can set alarms on error rates, monitor traffic patterns, investigate access incidents across accounts and Regions, and correlate S3 access activity with the rest of your operational data. Logs mirrored to S3 Tables are immediately queryable with standard SQL in Amazon Athena, Amazon Redshift, and other Iceberg-compatible query engines, so you can audit access patterns, analyze usage trends, and identify cost drivers across buckets over time. S3 server access logs delivery to CloudWatch Logs is available today in all AWS Regions, except for AWS China Regions and AWS GovCloud (US) Regions. To learn more, see the Amazon S3 webpage, server access logging in the Amazon S3 User Guide, and the AWS Storage Blog post.

Amazon S3 now supports delivering server access logs to Amazon CloudWatch Logs, giving you instant querying, alarms, cross-account and cross-Region aggregation, and AWS Key Management Service (KMS) encryption for your access log data. You can also mirror your logs to Amazon S3 Tables in Apache Iceberg format at no additional storage cost. These new delivery paths complement the existing free delivery of server access logs to S3 general purpose buckets, giving you more flexibility in how you monitor and analyze access to your data. With delivery to CloudWatch Logs, you can set alarms on error rates, monitor traffic patterns, investigate access incidents across accounts and Regions, and correlate S3 access activity with the rest of your operational data. Logs mirrored to S3 Tables are immediately queryable with standard SQL in Amazon Athena, Amazon Redshift, and other Iceberg-compatible query engines, so you can audit access patterns, analyze usage trends, and identify cost drivers across buckets over time. S3 server access logs delivery to CloudWatch Logs is available today in all AWS Regions, except for AWS China Regions and AWS GovCloud (US) Regions. To learn more, see the https://aws.amazon.com/s3, https://docs.aws.amazon.com/AmazonS3/latest/userguide/ServerLogs.html in the Amazon S3 User Guide, and the https://aws.amazon.com/blogs/storage/query-amazon-s3-access-logs-instantly-with-cloudwatch-and-s3-tables/.

Last week AWS Summit New York City brought together thousands of customers, partners, and builders for a free, one-day event showcasing the latest in cloud and AI innovation. Dr. Swami Sivasubramanian, VP of Agentic AI at AWS unveiled a stack of AI launches in his keynote, all built around one thesis: agents that compound value […]

A recap of the top announcements from AWS's New York Summit 2026

Amazon S3 Vectors has reduced data processed charges for queries on vector indexes with over 10 million vectors by up to 80%. This reduction lowers costs for customers running similarity search across large-scale AI, RAG, and semantic search workloads. The new pricing applies automatically with no application changes required. While this change reduces costs for large indexes, we continue to recommend distributing vectors across multiple indexes for improved query performance. S3 Vectors query pricing reductions are effective today in all AWS Regions where S3 Vectors is available. For updated pricing information, visit the S3 pricing page. To learn more about S3 Vectors, visit the product page and S3 User Guide.

Amazon S3 Vectors can now return up to 10,000 similarity search results per query, a 100x increase from the previous limit. The higher result limit helps you retrieve a larger, more comprehensive set of candidates during similarity queries. This is especially valuable for applications with multi-stage retrieval pipelines that need to apply additional processing such as reranking, aggregations, or deduplication to produce a more relevant final result set. To get started with the higher limit, use the latest AWS SDK and update your application code to specify up to 10,000 relevant results (topK nearest neighbors) when making a QueryVectors API request. Query results are now returned across multiple pages, and you can start processing the first page immediately while retrieving additional pages as needed. For queries that return larger result sets, you pay a small data-returned fee based on the total size of results returned. The first 512 KB of data returned per query is free. For full pricing details, visit the S3 pricing page. S3 Vectors supports retrieving up to 10,000 results per query in all AWS Regions where it is available. To learn more about S3 Vectors, visit the product page and S3 User Guide.

Amazon S3 now lets you attach up to 1 GB of rich, mutable, and queryable context directly to your objects using annotations, purpose-built for AI agents and autonomous workflows that need to discover, understand, and act on data at scale without maintaining separate metadata systems.

Amazon S3 Vectors has reduced data processed charges for queries on vector indexes with over 10 million vectors by up to 80%. This reduction lowers costs for customers running similarity search across large-scale AI, RAG, and semantic search workloads. The new pricing applies automatically with no application changes required. While this change reduces costs for large indexes, we continue to recommend distributing vectors across multiple indexes for improved query performance. S3 Vectors query pricing reductions are effective today in all https://docs.aws.amazon.com/AmazonS3/latest/userguide/s3-vectors-regions-quotas.html where S3 Vectors is available. For updated pricing information, visit the https://aws.amazon.com/s3/pricing/ To learn more about S3 Vectors, visit the https://aws.amazon.com/s3/features/vectors/ and https://docs.aws.amazon.com/AmazonS3/latest/userguide/s3-vectors.html.

Amazon S3 Vectors can now return up to 10,000 similarity search results per query, a 100x increase from the previous limit. The higher result limit helps you retrieve a larger, more comprehensive set of candidates during similarity queries. This is especially valuable for applications with multi-stage retrieval pipelines that need to apply additional processing such as reranking, aggregations, or deduplication to produce a more relevant final result set. To get started with the higher limit, use the latest AWS SDK and update your application code to specify up to 10,000 relevant results (topK nearest neighbors) when making a QueryVectors API request. Query results are now returned across multiple pages, and you can start processing the first page immediately while retrieving additional pages as needed. For queries that return larger result sets, you pay a small data-returned fee based on the total size of results returned. The first 512 KB of data returned per query is free. For full pricing details, visit the https://aws.amazon.com/s3/pricing/ S3 Vectors supports retrieving up to 10,000 results per query in all https://docs.aws.amazon.com/AmazonS3/latest/userguide/s3-vectors-regions-quotas.html where it is available. To learn more about S3 Vectors, visit the https://aws.amazon.com/s3/features/vectors/ and https://docs.aws.amazon.com/AmazonS3/latest/userguide/s3-vectors.html.

AWS Glue Data Catalog now supports AWS IAM-based authorization for Amazon S3 Tables and Apache Iceberg materialized views. With IAM-based authorization, you can define all necessary permissions across storage, catalog, and query engines in a single IAM policy. This capability simplifies the integration of S3 Tables or materialized views with any AWS Analytics service, including Amazon Athena, Amazon EMR, Amazon Redshift, and AWS Glue. You can also opt in to AWS Lake Formation at any time to manage fine-grained access controls using the AWS Management Console, AWS CLI, API, and AWS CloudFormation. This feature is now available in AWS GovCloud (US-East) and AWS GovCloud (US-West) Regions. To learn more, visit the S3 Tables documentation and the AWS Glue Data Catalog documentation.

AWS Glue Data Catalog now supports AWS IAM-based authorization for Amazon S3 Tables and Apache Iceberg materialized views. With IAM-based authorization, you can define all necessary permissions across storage, catalog, and query engines in a single IAM policy. This capability simplifies the integration of S3 Tables or materialized views with any AWS Analytics service, including Amazon Athena, Amazon EMR, Amazon Redshift, and AWS Glue. You can also opt in to AWS Lake Formation at any time to manage fine-grained access controls using the AWS Management Console, AWS CLI, API, and AWS CloudFormation. This feature is now available in AWS GovCloud (US-East) and AWS GovCloud (US-West) Regions. To learn more, visit the https://docs.aws.amazon.com/AmazonS3/latest/userguide/s3-tables-integration-overview.html and the https://docs.aws.amazon.com/glue/latest/dg/glue-federation-s3tables.html.

Amazon S3 Files makes S3 buckets accessible as high-performance file systems on AWS compute resources, eliminating the tradeoff between object storage benefits and interactive file capabilities while enabling seamless data sharing with ~1ms latencies.

In my last Week in Review post, I mentioned how much time I’ve been spending on AI-Driven Development Lifecycle (AI-DLC) workshops with customers this year. A common theme in those sessions is the need for better cost visibility. Teams are moving fast with AI, but as they go from experimenting to full production, finance and […]

Amazon S3 Files makes S3 buckets accessible as high-performance file systems on AWS compute resources, eliminating the tradeoff between object storage benefits and interactive file capabilities while enabling seamless data sharing with ~1ms latencies.

In my last Week in Review post, I mentioned how much time I’ve been spending on AI-Driven Development Lifecycle (AI-DLC) workshops with customers this year. A common theme in those sessions is the need for better cost visibility. Teams are moving fast with AI, but as they go from experimenting to full production, finance and […]

Twenty years ago this past week, Amazon S3 launched publicly on March 14, 2006. While Amazon Simple Storage Service is often considered the foundational storage service that defined cloud infrastructure, what began as a simple object storage service has grown into something far larger in scope and scale. As of March 2026, S3 stores more […]