7/7/2026, 12:00:00 AM ~ 7/8/2026, 12:00:00 AM (UTC)

Recent Announcements

Amazon GameLift Streams introduces secure terminal access for stream sessions

Amazon GameLift Streams now supports Stream Session Admin Shell, a secure terminal connection to the live runtime environment of a stream session for real-time troubleshooting. You can inspect logs, query running processes, check GPU utilization, and examine application state — all without managing SSH keys, open ports, or infrastructure credentials.\n Stream Session Admin Shell provides a terminal connection with the same level of access as your Amazon GameLift Streams applications. To connect, call the new CreateStreamSessionAdminShell API with your stream group and stream session identifiers, then use the returned credentials with the SSM Session Manager plugin for the AWS CLI. The feature supports Linux (Ubuntu 22.04), Proton, and Windows Server 2022 runtimes. The terminal connection is scoped to your application environment and automatically closes when the stream session ends. Stream Session Admin Shell is available at no additional cost in all AWS Regions where Amazon GameLift Streams is offered. For a full list of supported Regions, see the AWS Region table. To get started, see the Stream Session Admin Shell developer guide and CreateStreamSessionAdminShell API reference.

Amazon S3 Vectors is now available in AWS GovCloud (US) Regions

Amazon S3 Vectors is now available in AWS GovCloud (US-East) and AWS GovCloud (US-West).\n 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.

AWS Security Hub extends unified security management to Microsoft Azure

Today, AWS announces that AWS Security Hub now monitors Microsoft Azure resources, extending risk analytics, cloud security posture management, vulnerability management, and security response management across both clouds. Many AWS customers running workloads in AWS and Azure have had to operate separate security tools for each environment, making it difficult to prioritize risks holistically or respond consistently. Security Hub now provides a single, unified experience to detect and respond to risks across your AWS and Azure environments.\n Security Hub automatically discovers Azure resources, including Azure Virtual Machines (VMs), Azure Container Registry (ACR) container images, Azure Function Apps, and Azure identities, and evaluates them for misconfigurations, internet exposure, and software vulnerabilities. You receive posture checks against security standards including the CIS Benchmarks™ for Microsoft Azure Foundations, unified resource inventory, risk and exposure analysis, and automated response through existing EventBridge integrations. AWS and Azure findings appear in the same prioritized view with the same finding formats and automation workflows, so security teams can operate from one console rather than switching between tools. Security Hub includes an independent 30-day free trial to monitor Azure resources that begins once you create your integration with Microsoft Azure. After the trial, you pay the same price for monitoring Azure resources and equivalent AWS resources. You can create an integration to Azure from all AWS Regions where Security Hub is available except Middle East (UAE), Middle East (Bahrain), Asia Pacific (Taipei), and Asia Pacific (New Zealand). You can also create integrations to Microsoft Azure for AWS Security Hub CSPM for posture management checks and Amazon Inspector for vulnerability management independently from AWS Security Hub. To learn more, see AWS Security Hub Pricing and AWS Security Hub documentation.

Amazon ECS Managed Instances reduces GPU management fees by up to 60%

Amazon Elastic Container Service (Amazon ECS) Managed Instances now offers significantly reduced management fees for GPU and accelerated instance types. Beginning July 1, 2026, G-series ECS management fees are reduced by 35%, and P-series and AWS Trainium fees are reduced by 60%. These reductions apply automatically and no action is required from customers already using GPU instances with ECS Managed Instances.\n With ECS Managed Instances, you get the application performance you want and the simplicity you need. Simply define your task requirements such as the number of vCPUs, memory size, and CPU architecture, and Amazon ECS automatically provisions, configures and operates most optimal EC2 instances within your AWS account using AWS-controlled access. You can also specify desired instance types, including GPU-accelerated, network-optimized, and burstable performance, to run your workloads on the instance families you prefer. ECS Managed Instances includes capabilities built specifically for accelerated workloads: GPU metrics (utilization, memory, and temperature) through Amazon CloudWatch Container Insights, and automatic health monitoring that detects GPU-specific hardware failures and replaces unhealthy instances to minimize workload disruption. With today’s pricing update, customers running GPU workloads on ECS Managed Instances can now benefit from fully managed infrastructure at lower management fees. This pricing update is available in all AWS Regions where ECS Managed Instances is available. For the complete updated rate table, see ECS Managed Instances pricing. Amazon EKS is implementing identical management fee reductions for GPU instances on EKS Auto Mode. See the EKS What’s New Post for details. To learn more about ECS Managed Instances, visit the feature page, documentation, and AWS News launch blog.

Amazon EMR Serverless now supports larger worker sizes to run more compute and memory intensive workloads

Amazon EMR Serverless now offers larger worker configurations of 32 vCPUs with up to 244 GB of memory, allowing you to run more compute and memory-intensive workloads. Previously, the largest worker configuration available on EMR Serverless was 16 vCPUs with up to 120 GB of memory. Larger workers can help you improve the runtime performance as well as cost profiles for your workloads.\n For shuffle-heavy workloads, larger workers reduce inefficient data transfers between executors. For jobs with data skew, larger workers reduce the chances of out-of-memory failures. For jobs that need to cache data, larger workers allow holding more data in memory, boosting job performance. To take advantage of these benefits, we recommend using larger workers for your compute and memory-intensive Spark and Hive workloads.

To learn more about different worker configurations, please visit EMR Serverless documentation. Larger workers are available in all AWS Regions where EMR Serverless is available.

Amazon EC2 C8ine instances are now available in AWS Europe (Frankfurt) region

Starting today, Amazon Elastic Compute Cloud (Amazon EC2) C8ine instances are available in the AWS Europe (Frankfurt) region. C8ine instances are powered by custom sixth generation Intel Xeon Scalable processors, available only on AWS. These instances feature the latest sixth generation AWS Nitro cards, delivering up to 43% higher performance compared to previous generation C6in instances.\n C8ine instances offer up to 2.5 times higher packet performance per vCPU versus prior generation network optimized instances, providing up to 2x higher network throughput for traffic going through Internet gateways compared to existing C6in network optimized instances. C8ine instances are designed for security and network virtual appliances, including virtual firewalls, load balancers, and Telco 5G UPF workloads.

Amazon EC2 C8ine instances are available in US East (N. Virginia), US West (Oregon), Asia Pacific (Tokyo), and Europe (Frankfurt) regions. C8ine instances are available via Savings Plans and On-Demand instances. For more information, visit the Amazon EC2 C8i instance pages.

Amazon SageMaker now supports data lineage in IAM-based domains

Amazon SageMaker Unified Studio now supports OpenLineage compatible data lineage in IAM-based domains, capturing lineage events from Apache Spark jobs run on Amazon EMR, AWS Glue, SageMaker Visual ETL, and notebooks. This capability is already available in IAM Identity Center-based domains. The interactive lineage graph provides an aggregate visual representation of how data moves from source to consumption, with configurable graph depth, event timestamp mode for detailed column-level lineage, and a dataset-only view for simplified visualization. For both IAM-based and IAM Identity Center-based domains, you can programmatically publish, query, and manage data lineage from OpenLineage compatible applications. You can now also remove published events using the DeleteLineageEvent API.\n This feature is available in all AWS Regions where Amazon SageMaker Unified Studio is available. To get started, visit the Amazon SageMaker Unified Studio documentation and API reference.

Amazon Redshift RG instances now available in AWS GovCloud (US) Regions

Amazon Redshift RG instances, powered by AWS Graviton processors, are now available in the AWS GovCloud (US-West) and AWS GovCloud (US-East) Regions. RG instances deliver better performance, running data warehouse and data lake workloads up to 2.4x as fast as previous generation RA3 instances, at 30% lower price per vCPU. RG instances include Redshift’s custom-built vectorized data lake query engine that processes Apache Iceberg and Parquet data on your cluster nodes, enabling you to run SQL analytics across your data warehouse and data lake using a single engine.\n RG instances are available in two instance sizes, rg.xlarge and rg.4xlarge. Customers with existing RA3 clusters can upgrade them to RG using Snapshot & Restore, Elastic Resize, or Classic Resize. RG instances are available with flexible pricing options, including On-Demand, and 1-year and 3-year Reserved Instances with All Upfront, Partial Upfront, and No Upfront payment options. For pricing details, visit the Amazon Redshift pricing page. To get started, refer to the following resources:

Amazon Redshift RG Instance Documentation

RA3 to RG Upgrade Guide

Amazon Redshift Pricing

Amazon RDS for Oracle now supports Oracle Database 26ai

Amazon RDS for Oracle now supports Oracle Database 26ai, Oracle’s latest Long Term Support Release, with Amazon Bedrock integration which provides access to foundation models such as Anthropic Claude, Amazon Nova, and Meta Llama. With Oracle Database 26ai, you can leverage Oracle’s Select AI feature to generate and run SQL queries from natural language prompts, increasing productivity for both developers and business users. You can also implement retrieval augmented generation (RAG) directly from SQL using Oracle AI Vector Search without moving data out of their database.\n Oracle Database 26ai also includes AI Vector Search for storing vector embeddings alongside relational data and performing semantic similarity and hybrid searches without a separate vector database, JSON Relational Duality Views for accessing the same underlying data as either JSON documents or relational tables, and SQL Property Graphs for in-database graph analytics. You can create new DB instances running Oracle Database 26ai or upgrade from Oracle Database 19c or 21c container databases (CDBs). Oracle Database 26ai is available in Enterprise Edition only. To create a new Oracle Database 26ai instance, use the AWS Management Console, AWS CLI, or AWS SDK and select an Oracle 26.0.0.0 engine version. To upgrade existing Oracle Database 19c or 21c CDB instances, use the Modify DB Instance workflow and select a 26.0.0.0 engine version. If your DB instance runs Oracle Database 19c as a non-CDB, you must first convert it to the CDB architecture before upgrading to 26ai. For more information, see Converting a non-CDB to a CDB. Amazon RDS for Oracle Database 26ai is available in all commercial AWS Regions and the AWS GovCloud (US) Regions. For more information, see Oracle Database 26ai with Amazon RDS and Amazon Bedrock integration for RDS for Oracle.

Amazon EKS Auto Mode reduces GPU management fees by up to 60%

Amazon Elastic Kubernetes Service (Amazon EKS) Auto Mode now offers significantly reduced management fees for GPU and accelerated instance types. Beginning July 1, 2026, G-series Auto Mode management fees are reduced by 35%, and P-series and AWS Trainium fees are reduced by 60%. These reductions apply automatically to all EKS Auto Mode clusters and no action is required from customers already using GPU instances with Auto Mode.\n EKS Auto Mode simplifies Kubernetes operations by automatically provisioning and managing infrastructure for machine learning inference, fine-tuning, rendering, and batch processing workloads. It includes capabilities built for accelerated workloads: automatic parallel image pulling and unpacking on GPU instances with local NVMe storage, so large container and model images start faster, and accelerator-aware node repair that detects GPU hardware failures and automatically replaces unhealthy nodes. With today’s price reduction, customers can run GPU workloads on Auto Mode at lower management fees, making its fully managed infrastructure more cost-effective.

This pricing update is available in all AWS Regions where EKS Auto Mode is available. Amazon ECS is implementing identical management fee reductions for GPU instances on ECS Managed Instances. See the ECS What’s New post for details.

To get started with GPU workloads on EKS Auto Mode, see the EKS for AI/ML documentation. For the complete updated rate table, see Amazon EKS pricing.

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