Kategorie: Amazon AWS

Amazon Cognito now available as a skill in the Agent Toolkit for AWS

Amazon Cognito is now available as a core skill (aws-auth) in the Agent Toolkit for AWS. AI coding agents using the toolkit can now set up, configure, secure, and troubleshoot Amazon Cognito using best-practice workflows, helping developers implement secure sign-in flows for users, AI agents, and microservices faster.
 
The Amazon Cognito (aws-auth) skill covers user pool and app client configuration, managed login and OAuth 2.0 flows, token management, JWT authorizers, passkey/WebAuthn enrollment, threat protection, Lambda trigger wiring, and identity pools. When paired with the AWS MCP Server, agents execute AWS CLI commands with IAM-based guardrails and CloudTrail audit logging. The skill also works standalone via the AWS CLI.
 
The Amazon Cognito (aws-auth) skill is available as part of the Agent Toolkit for AWS. To get started, see the Amazon Cognito aws-auth skill on GitHub or browse the Agent Toolkit Quick Start guide. For more information about Amazon Cognito, see the Amazon Cognito Developer Guide.
Quelle: aws.amazon.com

Amazon Timestream for InfluxDB now supports backup and restore

Amazon Timestream for InfluxDB now lets you create and manage your own backups and restore your data on demand. You can trigger one-time, on-demand backups, schedule automated recurring backups at the frequency and retention you choose, and restore a backup to a new resource or in place of an existing one. This capability is available for both the InfluxDB 2 and InfluxDB 3 engines through the AWS Management Console, the AWS CLI, and the Timestream for InfluxDB API. With this capability, you control your data protection strategy. You can take an on-demand backup before a risky migration or configuration change. You can also define up to four automated backup configurations per resource using hourly, daily, weekly, monthly, or custom schedules, each with its own retention period. The first backup captures a full copy of your database, and subsequent backups are incremental, reducing the performance impact of ongoing backups. When you restore a backup, you can create a new resource that inherits the source configuration or replace an existing resource. If the source resource uses a Customer Managed key (KMS), its backups use the same key.
Customer-driven backup and restore is available in all AWS Regions where Amazon Timestream for InfluxDB is available. To get started, open the Amazon Timestream console. For more information, see the Amazon Timestream for InfluxDB documentation and pricing page.
Quelle: aws.amazon.com

Amazon ElastiCache now supports Graviton4-based M8g, R8g, and C8gn nodes

Amazon ElastiCache now supports Graviton4-based M8g, R8g, and C8gn node families for Valkey and Memcached. Graviton4-based nodes provide up to 47% higher throughput, up to 43% lower P99 latency, and up to 31% better price-performance for on-demand pricing over Graviton3-based nodes of equivalent sizes on Amazon ElastiCache for Valkey, depending on node family, size, and workload configuration.
Graviton4-based nodes also offer more memory per node compared to equivalent Graviton3-based nodes. As an example, an m8g.8xlarge provides 124.65 GiB versus 103.68 GiB on m7g.8xlarge, up to 20% more memory at the same node size. C8gn nodes offer up to 200 Gbps of network bandwidth, enabling you to scale performance and throughput while optimizing the cost of running network-intensive workloads.
M8g, R8g, and C8gn nodes are available in sizes from large to 16xlarge in over 30 AWS Regions, including the AWS GovCloud (US) Regions and the China Regions. For complete information on pricing and regional availability, please refer to the Amazon ElastiCache pricing page. To get started, create a new cluster or modify an existing cluster using the AWS Management Console, AWS SDK, or AWS CLI. To work with ElastiCache using AI coding agents, see Agent tools for ElastiCache. To learn more, see Supported node types in the Amazon ElastiCache User Guide.
Quelle: aws.amazon.com

AgentCore runtime instances are now generally available

Today, AWS announces runtime instances, a new feature in Amazon Bedrock AgentCore that lets you run agents on your own Amazon EC2 instances without managing infrastructure. AgentCore runtime provides purpose-built infrastructure to deploy and operate AI agents securely at scale. Runtime instances complement the existing microVM-based option in AgentCore runtime and give teams running sustained, resource-intensive, or specialized-hardware agents access to the breadth of EC2 instance types, while AgentCore handles provisioning, patching, scaling, and lifecycle management.
Using the AWS Management Console, CLI, SDKs, or APIs, you create a capacity provider that specifies the EC2 instance types your agents need, including GPU-accelerated, memory-optimized, and compute-optimized families, and attach your agents to it. Runtime instances support long-running agent sessions of up to 14 days, while the default serverless, microVM-based runtime is designed for sessions of up to 8 hours that need fast startup . You can choose the right compute for each agent, or run a mix, without changing how you deploy or invoke your agents.
You can use runtime instances in the following AWS Regions: US East (N. Virginia), US East (Ohio), US West (Oregon), Asia Pacific (Mumbai), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), Europe (Frankfurt), and Europe (Ireland). You are charged for the management of the compute provisioned, in addition to your Amazon EC2 costs. To get started, visit AWS News Blog or AgentCore documentation. To learn about pricing, visit AgentCore pricing.
Quelle: aws.amazon.com

Amazon EC2 G7 instances are now available in the AWS Europe (Spain) Region

Amazon Elastic Compute Cloud (Amazon EC2) G7 instances powered by NVIDIA RTX PRO 4500 Blackwell Server Edition GPUs are now available in Europe (Spain) 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.
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 (EFA) 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 four AWS Regions: US East (N. Virginia and Ohio), US West (Oregon), and Europe (Spain). 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.
Quelle: aws.amazon.com

AWS Lambda console extends console-to-IDE integration to Kiro and Cursor

AWS Lambda console now extends its console-to-IDE integration to support Kiro and Cursor IDEs. This expansion builds on the existing console-to-IDE transition for Visual Studio Code (VS Code), providing a seamless transition between cloud and local development environments for serverless developers using these popular IDEs.
With the expanded console-to-IDE integration, developers can start from the Lambda console and follow a guided setup to begin local development in Kiro or Cursor while preserving their existing code and configurations. This capability also enables developers to easily convert their applications to an AWS Serverless Application Model (AWS SAM) template using Kiro and Cursor, simplifying their Infrastructure as Code (IaC) practices and CI/CD pipeline integration.
This feature is available in all commercial AWS Regions where Lambda is available, at no additional cost.
To get started, click the “Open in Kiro” or “Open in Cursor” button in the Lambda console’s Code tab or in the Getting Started popup when creating a new function. This will automatically open your function in the selected IDE on your local device. To learn more about this experience, visit the Lambda developer guide.
Quelle: aws.amazon.com

Amazon ECS now supports fractional GPU scheduling with Amazon EC2 G6f instances

Amazon Elastic Container Service (Amazon ECS) now supports fractional GPU scheduling with Amazon EC2 G6f instances, enabling you to run your workloads on GPU partitions as small as one-eighth of an NVIDIA L4 Tensor Core GPU with 3 GB of GPU memory. Fractional GPUs give you the flexibility to right-size your containers for small-model AI inference, model experimentation, graphics rendering, and other workloads that do not require a full GPU, helping reduce infrastructure costs compared to provisioning a full GPU instance.
You can request a fractional GPU by setting GPU=0.125, GPU=0.25, or GPU=0.5 in the container definition of your Amazon ECS task definition. Amazon ECS then places the task on a G6f instance that satisfies the request. Fractional GPU configuration is supported on both Amazon ECS Managed Instances and Amazon ECS on EC2. With ECS Managed Instances, you get a fully managed experience where ECS automatically handles instance provisioning, scaling, patching, and lifecycle management, so you can focus on your GPU workloads rather than infrastructure operations. ECS Managed Instances also include capabilities built specifically for accelerated workloads, such as GPU metrics through Amazon CloudWatch Container Insights, and automatic health monitoring that detects GPU hardware failures and replaces unhealthy instances to minimize workload disruption.
This capability is available in all AWS Regions where Amazon EC2 G6f instances are available. To get started, use the AWS Management Console, AWS CLI, AWS SDKs, AWS CloudFormation, or other infrastructure-as-code tools to configure a capacity provider with G6f instances and specify a fractional GPU value in the container definition of your ECS task definition. To learn more, visit the Amazon ECS fractional GPU documentation and the Amazon EC2 G6 instance page.
Quelle: aws.amazon.com

Amazon WorkSpaces now publishes enhanced observability metrics

Amazon WorkSpaces now publishes additional performance and session health metrics to Amazon CloudWatch, enabling IT administrators to gain deeper visibility into their virtual desktop workloads. These new metrics span network performance, compute and storage resource utilization, and session lifecycle events — all available at no additional cost. With these metrics, administrators can proactively identify and troubleshoot issues that impact end-user experience. For example, TCP retransmission rate and congestion window help pinpoint network degradation, GPU usage and CPU queue length surface compute bottlenecks, and storage metrics like disk I/O queue lengths and memory page hard faults provide visibility into disk saturation and memory pressure. Administrators can set CloudWatch alarms for rapid detection of performance issues, build custom dashboards for fleet-wide visibility, and reduce mean time to resolution. These metrics are available in all AWS Regions where Amazon WorkSpaces is supported. To get started, navigate to the Amazon CloudWatch console and observe these metrics or update your WorkSpaces custom dashboards. You can also monitor these metrics through WorkSpaces automatic dashboard. To learn more, visit the Amazon WorkSpaces documentation and the CloudWatch metrics reference.
Quelle: aws.amazon.com

Amazon WorkSpaces Applications now publishes enhanced observability metrics

Amazon WorkSpaces Applications now publishes additional performance and session health metrics to Amazon CloudWatch, enabling IT administrators to gain deeper visibility into their application streaming workloads. These new metrics span network performance, compute resource utilization, and session lifecycle events — all available at no additional cost. With these metrics, administrators can proactively identify and troubleshoot issues that impact end-user experience. For example, metrics such as TCP retransmission rate and congestion window help pinpoint network degradation, while GPU utilization and memory page hard faults surface resource bottlenecks before they affect session quality. Session lifecycle metrics like connection failures and connection duration enable teams to set CloudWatch alarms for rapid detection of connectivity issues, build custom dashboards for fleet-wide visibility, and reduce mean time to resolution. These metrics are available in all AWS Regions where Amazon WorkSpaces Applications is supported. To get started, navigate to the Amazon CloudWatch console and observe these metrics or update your WorkSpaces Applications custom dashboards. You can also monitor these metrics through WorkSpaces Applications automatic dashboard. To learn more about metric availability by operating system, visit the Amazon WorkSpaces Applications documentation and the CloudWatch metrics reference.
Quelle: aws.amazon.com

Amazon RDS now provides visibility into storage volume initialization status

Amazon RDS now provides visibility into the initialization status of database storage volumes created from snapshots. You can use this status to determine when your storage is fully initialized after a restore and is ready to support latency-sensitive database workloads at fully provisioned performance. When you restore a database instance to a point-in-time, or create a read replica creation, or convert from Single-AZ to Multi-AZ conversion, Amazon RDS creates storage volumes from a snapshot. These volumes undergo initialization, during which storage blocks are downloaded from Amazon S3 and written to the volume before they can be accessed. The initialization rate varies depending on the workload and which blocks are accessed and during this period you may notice increased I/O latency. Previously, Amazon RDS reported the instance as available throughout initialization, giving you no direct signal for when performance would stabilize. The new StorageOperationStatus and StorageOperationPercentProgress fields on the RDS Console and DescribeDBInstances API let you monitor your storage initialization progress in real time, so you can validate when all blocks have been written. You can use the information to time your workloads to align with its completion. The fields also report storage optimization progress so you can plan for full provisioned performance after a storage modification. Storage volume initialization status is accessible by default for all Amazon RDS database instances in all commercial AWS Regions and US GovCloud Regions. You can start using it today through the Amazon RDS Management Console, the AWS Command Line Interface (CLI), or the AWS SDKs. To learn more, see Amazon RDS storage in the Amazon RDS User Guide.
Quelle: aws.amazon.com