7/10/2026, 12:00:00 AM ~ 7/13/2026, 12:00:00 AM (UTC)
Recent Announcements
Amazon EC2 network/EBS instances now available in additional regions
Starting today, Amazon Elastic Compute Cloud (Amazon EC2) R8in, R8ib, R8idn, and R8idb instances are available in the AWS Asia Pacific (Tokyo) and Europe (Frankfurt, Ireland) regions. These instances are powered by custom sixth generation Intel Xeon Scalable processors, available only on AWS and feature the latest sixth generation AWS Nitro cards. These instances deliver up to 43% better compute performance per vCPU compared to previous generation R6in and R6idn instances.\n R8in, R8idn instances deliver 600 Gbps network bandwidth, the highest network bandwidth among enhanced networking EC2 instances. R8in instances are ideal for workloads such as real-time big data analytics, distributed web scale in-memory caches, caching fleets for AI/ML clusters, and Telco applications such as 5G User Plane Function (UPF). R8idn instances are ideal for network-intensive general purpose workloads requiring local storage, such as distributed compute, data analytics, and high-performance file systems. R8ib, R8idb instances deliver up to 300Gbps EBS bandwidth, the highest among non-accelerated compute EC2 instances. R8ib instances are best suited for workloads that benefit from high block storage performance, such as high-performance file systems and NoSQL databases. R8idb instances are ideal for storage-intensive general purpose workloads such as large commercial databases, data lakes, and NoSQL databases that benefit from both high EBS throughput and low-latency local NVMe storage. R8in, R8ib, R8idn, and R8idb instances support Elastic Fabric Adapter (EFA) networking on 48xlarge, 96xlarge, metal-48xl, and metal-96xl sizes. EFA networking enables lower latency and improved cluster performance for workloads deployed on tightly coupled clusters.
Amazon EC2 R8in an R8ib instances are available in US East (N. Virginia, Ohio), US West (Oregon), Asia Pacific (Tokyo), and Europe (Spain, Frankfurt, Ireland) regions, via Savings Plans, On-Demand, and Spot instances. For more information, visit the Amazon EC2 R8i instance page.
Amazon EMR on EKS now supports Apache Spark troubleshooting agent
Amazon EMR on EKS now supports the Apache Spark troubleshooting agent. Data engineers can now diagnose EMR on EKS job failures through natural language, receiving automated root cause analysis and PySpark code recommendations without manually navigating distributed logs and Spark History Server data.\n The agent analyzes Spark History Server data, distributed executor logs, and cluster configurations to identify issues such as memory errors, data skew, resource contention, and connectivity failures. With this launch, the Spark troubleshooting agent now covers all EMR deployment options: EMR on EC2, EMR Serverless, and EMR on EKS. The agent is accessible directly from the EMR on EKS console through a “Troubleshoot with AI” option on failed jobs. Additionally, the agent is available through MCP (Model Context Protocol) using any compatible AI coding agent, including Kiro, Claude Code, and Cursor. All operations are read-only, authenticated with IAM roles, and logged in AWS CloudTrail. The Spark troubleshooting agent for Amazon EMR on EKS is available in all AWS Regions where the SageMaker Unified Studio is available. To get started, go to EMR on EKS console, or set up the MCP server in your preferred AI coding agent. For detailed guidance, see the EMR troubleshooting agent documentation.
Amazon Location Service enhances Places APIs with new address and search options
Today, Amazon Location Service announced new enhancements to its Places APIs that give developers greater control over address name formatting, multilingual address, travel-optimized POI search, and drive-through data. These capabilities span the Geocode, ReverseGeocode, GetPlace, Suggest, Autocomplete, SearchNearby, and SearchText APIs. Amazon Location Service is a mapping service that offers geospatial data and location functionality such as maps, places search and geocoding, route planning, device tracking, and geofencing.\n Developers can now control how address component names are returned using the new AddressNamesMode parameter — choosing between matched (echoing input), normalized (canonical names), or administrative (government hierarchy names) — and override behavior per component with AddressNamesVariant. The new AddressTranslations parameter returns place name translations in 50+ languages, making it easier to build multilingual applications. A TravelMode parameter optimizes Suggest and SearchText results for users on the move, improving relevance for navigation and in-vehicle scenarios. Additionally, GetPlace, Suggest, SearchNearby, and SearchText now return a DriveThrough attribute indicating whether a place offers drive-through service — useful for logistics, food delivery, and navigation applications. The Geocode API also now supports a new Parsing.AdditionalInfo response field with additional detail about how the input address was interpreted. Amazon Location Service is available in the following AWS Regions: US East (Ohio), US East (N. Virginia), US West (Oregon), Asia Pacific (Mumbai), Asia Pacific (Sydney), Asia Pacific (Tokyo), Canada (Central), Europe (Frankfurt), Europe (Ireland), Europe (London), Europe (Spain), Europe (Stockholm), South America (São Paulo), and AWS GovCloud (US-West). To get started, see the Amazon Location Service Places API reference, or learn more about Places in the Amazon Location Service Developer Guide.
Amazon EC2 G7 instances are now available in the AWS US East (N. Virginia) Region
Starting today, Amazon Elastic Compute Cloud (Amazon EC2) G7 instances powered by NVIDIA RTX PRO 4500 Blackwell Server Edition GPUs are now available in US East (N. Virginia) Region. G7 instances deliver up to 4.6x AI inference performance and up to 2.1 graphics performance compared to G6 instances. G7 instances also deliver faster performance for GPU-accelerated data analytics workloads.\n Customers can use G7 instances for deploying AI models for language translation, video and image analysis, and speech recognition. They also accelerate graphics workloads such as creating and rendering real-time, cinematic-quality graphics and game streaming. Additionally, G7 instances support video transcoding, spatial computing, and data analytics workloads such as recommender systems, Retrieval Augmented Generation (RAG) inference, and real-time data pipelines. G7 instances feature up to 8 NVIDIA RTX PRO 4500 Blackwell Server Edition GPUs with 32 GB of memory per GPU and custom Intel Xeon 6 processors. They support up to 192 virtual CPUs (vCPUs) and up to 700 Gbps of Elastic Fabric Adapter networking bandwidth. They also support up to 768 GiB of system memory, and up to 7.6 TB of local NVMe SSD storage.
You can start using Amazon EC2 G7 instances today in three AWS Regions: US East (N. Virginia and Ohio), and US West (Oregon). You can purchase G7 instances as On-Demand Instances, Spot Instances, or as part of Savings Plans.
To get started, visit the AWS Management Console, AWS Command Line Interface (CLI), and AWS SDKs. To learn more, visit the G7 instance page.
AWS DMS Schema Conversion now supports offline SQL Server conversion
AWS Database Migration Service (DMS) Schema Conversion now supports offline source conversion for Microsoft SQL Server, enabling you to convert SQL Server schemas and code without direct connectivity to your source databases. You extract metadata using standard database commands in your own environment, then upload it to DMS Schema Conversion for processing. This eliminates security reviews, firewall changes, and VPN setup that delay migration projects, while delivering the same conversion results as the connected approach.\n Offline Source is ideal for organizations with security policies that restrict external tool access to production SQL Server databases. Database administrators generate human-readable metadata files within their existing environment, and security teams can review the commands and output before uploading, making approval straightforward. By removing the connectivity requirement, Offline Source transforms weeks of security reviews into a simple command-and-upload workflow.
Offline Source supports all DMS Schema Conversion targets at no additional conversion charge. For regional availability, see the Supported AWS Regions page. To get started, see Using Offline Source in the DMS Schema Conversion documentation.
Amazon EC2 I7ie instances now available in AWS Asia Pacific (Hyderabad) region
AWS is announcing starting today, Amazon EC2 I7ie instances are now available in AWS Asia Pacific (Hyderabad) region. Designed for large storage I/O intensive workloads, I7ie instances are powered by 5th Gen Intel Xeon Processors with an all-core turbo frequency of 3.2 GHz, offering up to 40% better compute performance and 20% better price performance over existing I3en instances. I7ie instances offer up to 120TB local NVMe storage density for storage optimized instances and offer up to twice as many vCPUs and memory compared to prior generation instances. Powered by 3rd generation AWS Nitro SSDs, I7ie instances deliver up to 65% better real-time storage performance, up to 50% lower storage I/O latency, and 65% lower storage I/O latency variability compared to I3en instances.\n I7ie are high density storage optimized instances, ideal for workloads requiring fast local storage with high random read/write performance at very low latency consistency to access large data sets. These instances are available in 9 different virtual sizes and deliver up to 100Gbps of network bandwidth and 60Gbps of bandwidth for Amazon Elastic Block Store (EBS). To learn more, visit the I7ie instances page.
Amazon DocumentDB (with MongoDB compatibility) now supports R8g.24xlarge and R8g.48xlarge instances
Amazon DocumentDB (with MongoDB compatibility) now supports R8g.24xlarge and R8g.48xlarge database instances. R8g instances are powered by AWS Graviton4 processors and feature DDR5 memory, enabling customers to achieve higher throughput and support larger working sets in memory. With R8g.24xlarge (96 vCPUs, 768 GiB memory) and R8g.48xlarge (192 vCPUs, 1,536 GiB memory), customers can run more demanding workloads such as high-concurrency transactional applications, large-scale document processing, and memory-intensive operational workloads.\n Customers can get started with R8g.24xlarge and R8g.48xlarge instances through the AWS Management Console, CLI, and SDK by modifying their existing Amazon DocumentDB database cluster or creating a new one. R8g instances are available for Amazon DocumentDB 5.0+ on both Standard and IO-Optimized cluster storage configurations. For more information including region availability, visit our pricing page and documentation.
AWS DMS Schema Conversion now supports AI agent automation
AWS Database Migration Service (DMS) Schema Conversion now supports AI agent automation through the AWS MCP Server. You can connect AI coding agents, including Kiro, Claude Code, and Cursor, to DMS Schema Conversion and run complete migration workflows using natural language directly from your IDE. Agents create projects, browse source metadata, convert schemas, generate assessment reports, and export results autonomously.\n The DMS Schema Conversion skill, dms-schema-conversion, loads on demand and provides agents with predefined procedures, including API patterns, schema exclusions, and operational sequencing rules. Agents follow these procedures rather than improvising from general knowledge, reducing trial-and-error loops. They can also help convert remaining code objects such as stored procedures, functions, and triggers, building on the generative AI capabilities launched at re:Invent 2024.
AI agent automation is available for all existing DMS Schema Conversion source and target engine pairs at no additional charge. For regional availability, see the Supported AWS Regions page. To get started, see Using AI agents with DMS Schema Conversion in the documentation.
AWS Organizations now automatically applies security controls by default when you create a new organization via AWS Organizations console, simplifying initial security configuration by automatically applying core security controls. This approach safeguards multi-account environments by protecting against unintended member account departures from your organization. CloudOps administrators and central security teams get immediate protection in their new organizations from day one.\n When you create a new organization using the AWS Organizations console, the service automatically applies service control policies (SCPs) that prevent member accounts from leaving the organization or closing themselves. These controls help enterprises migrating to AWS or starting a new organization establish strong governance patterns without requiring deep security expertise. The security defaults are intentionally lightweight to provide protection without impeding legitimate operations. You maintain full control to modify or disable these settings at any time.
This feature is available in US East (N. Virginia), AWS GovCloud (US-East), AWS GovCloud (US-West), China (Beijing), and China (Ningxia). To learn more, visit the AWS Organizations documentation.
AWS Blogs
AWS Japan Blog (Japanese)
- [For the public] Claude Mythos Era Threat Countermeasures Workshop Event Report & Information on the Resilience Lifecycle Practice Workshop
- [Event Report & Material Release] AWS Migration Responding to Changes in the IT Infrastructure Environment
- Imuraya Group: AI demand forecasting for chilled products using Amazon SageMaker Canvas reduces work man-hours by 90% and achieves prediction accuracy equivalent to that of skilled workers
- AWS Cloud WAN Routing Policy: A Collection of Global Network Scenarios in Actual Operation (Part 2)
- Introducing Kiro’s overuse limit settings and prepayment features
AWS Compute Blog
- Architecting for IOPS and throughput performance on AWS Outposts racks
- Announcing Lambda MicroVMs: serverless compute environments with VM-level isolation and near-instant startup
- Secure code execution for AI agents with AWS Lambda MicroVMs
Artificial Intelligence
- Fine-tune NVIDIA Nemotron 3 models with Amazon SageMaker AI serverless model customization
- Real-time dental image verification with Amazon SageMaker AI at Henry Schein One
- Build a semantic layer for agentic AI on AWS with Stardog and Amazon Bedrock AgentCore
- Scaling agentic workflows with native case management in Amazon Quick Automate
- Deploying quantized models on Amazon SageMaker AI with Unsloth
- How KTern.AI built agentic AI for SAP on Amazon Bedrock AgentCore
- Disaggregated prefill and decode for LLM inference on SageMaker HyperPod