6/23/2026, 12:00:00 AM ~ 6/24/2026, 12:00:00 AM (UTC)

Recent Announcements

Amazon CloudWatch Logs supports managed syslog ingestion

Amazon CloudWatch Logs supports managed syslog ingestion, enabling customers to send syslog messages from firewalls, routers, switches, and Linux servers directly into CloudWatch Logs.\n With today’s launch, customers can configure their network devices and servers to send syslog messages over TCP, TCP+TLS, or UDP to a VPC endpoint in their account - without installing or managing any agents. Amazon CloudWatch Logs supports RFC 5424, RFC 3164, and Cisco FTD/ASA syslog formats, making it compatible with a wide range of infrastructure. Amazon CloudWatch Logs automatically parses incoming syslog messages and extracts structured fields such as facility, severity, hostname, and application name, thereby eliminating the need for custom parsing pipelines. For example, customers can ingest syslog from their network firewalls and immediately query by severity or hostname using Logs Analytics to investigate security events or troubleshoot connectivity issues. This feature helps teams centralize infrastructure log visibility, simplify operational workflows, and reduce the overhead of deploying and maintaining log collection agents across distributed environments. Available in all commercial AWS Regions except Middle East (UAE), Middle East (Bahrain), and Israel (Tel Aviv). To get started, see the Amazon CloudWatch Logs documentation.

SageMaker Notebook Instances now support G6e instance types

We are pleased to announce general availability of Amazon EC2 G6e instances on SageMaker notebook instances.\n Amazon EC2 G6e instances are powered by up to 8 NVIDIA L40s Tensor Core GPUs with 48 GB of memory per GPU and third generation AMD EPYC processors. G6e instances deliver up to 2.5x better performance compared to EC2 G5 instances. Customers can use G6e instances to interactively test model deployment and for interactive model training use cases such as generative AI fine-tuning. You can use G6e instances to deploy large language models (LLMs) with up to 13B parameters and diffusion models for generating images, video, and audio.

Amazon EC2 G6e instances are available on SageMaker notebook instances in the AWS US East (N. Virginia and Ohio), US West (Oregon), Asia Pacific (Tokyo), Middle East (Dubai) and Europe (Frankfurt, Sweden, Spain) regions.

Visit developer guides for instructions on setting up and using JupyterLab and CodeEditor applications on SageMaker Studio and SageMaker notebook instances.

Amazon Bedrock AgentCore Memory now supports cross-account access

Amazon Bedrock AgentCore Memory now enables cross-account access, allowing you to build multi-account architectures where memory resources and consuming agents span multiple AWS accounts. You can grant principals in one account permission to call memory data plane APIs against resources in another account using resource-based policies, and configure memory delivery destinations (Amazon S3, Amazon SNS, Amazon Kinesis Data Streams) that reside in a separate account.\n Cross-account access is configured by attaching a resource-based policy to your memory resource. Once configured, principals in the consuming account can create events, write memory records, retrieve records, and perform semantic search by referencing the full memory ARN. Cross-account delivery destinations allow your memory resource to deliver payloads and stream events to S3 buckets, SNS topics, and Kinesis Data Streams in other accounts.

To get started, see Cross-account memory access in the Amazon Bedrock AgentCore Developer Guide. Amazon Bedrock AgentCore Memory cross-account access is available in all AWS Regions where Amazon Bedrock AgentCore Memory is supported.

AWS HealthOmics now supports ephemeral storage for private workflows

AWS HealthOmics adds ephemeral storage for private workflows, giving bioinformatics workloads dedicated scratch space that delivers more consistent run performance and lower costs. Each workflow task now receives a dedicated local volume mounted at /tmp, and workflows that generate significant scratch data, such as genomic sequence alignment, BAM sorting, and variant calling, can experience faster run times. AWS HealthOmics is a HIPAA-eligible service that helps healthcare and life sciences customers accelerate scientific breakthroughs with fully managed bioinformatics workflows.\n With this launch, workflow tasks can write temporary data to their own local volume, keeping scratch I/O isolated from shared run storage that hosts the working directory. By default, each task includes 16 GiB of ephemeral storage at no additional charge. You can increase the amount of ephemeral storage allocated to individual tasks, up to a maximum of 3,072 GiB per task, using the appropriate directive in your WDL, Nextflow, or CWL workflow definition. You can enable ephemeral storage at runtime with the StartRun API. All ephemeral storage volumes are encrypted and deleted when a task terminates.

You can use ephemeral storage in all AWS Regions where AWS HealthOmics is available: US East (N. Virginia), US West (Oregon), Europe (Frankfurt, Ireland, London), Israel (Tel Aviv), and Asia Pacific (Singapore, Seoul). To learn more about ephemeral storage, visit the AWS HealthOmics User Guide. For more information on pricing, visit AWS HealthOmics pricing.

Amazon Cognito now supports customer managed key for encryption at rest

Amazon Cognito now supports customer managed keys in AWS Key Management Service (KMS) for encrypting user pool data at rest. While AWS owned keys are used by default to protect your data, customer managed keys give you full control over the encryption keys, helping you achieve your organization’s data governance objectives. \n  

With customer managed keys, you can define organizational policies and revoke access to encrypted data by disabling or deleting your key. You create and manage the customer managed key lifecycle and usage permissions in AWS KMS. You can configure a customer managed key when creating a new user pool or update an existing user pool to use one. You can also use AWS CloudTrail to monitor and audit all usage of your customer managed keys, giving you visibility into when and how your identity data is accessed.

 

Customer managed keys are available in user pools in Essentials and Plus tiers at no additional costs. Standard AWS KMS charges apply. To get started, configure your customer managed keys using the AWS Management Console, AWS CLI, or AWS SDKs. Visit the developer guide for instructions.

Automated Reasoning checks in Amazon Bedrock Guardrails add new policy refinement workflows

Today, AWS announces new automated refinement workflows for Automated Reasoning checks in Amazon Bedrock Guardrails. Automated Reasoning checks use formal logic to mathematically validate the accuracy of generative AI responses against a policy you define, helping detect hallucinations and provide verifiable explanations. The quality of validation results depends on how well a policy is defined. The new workflows help customers improve their policies with less manual effort, leading to more reliable Guardrail validation results.\n The launch introduces two refinement workflows. With the iterative policy improvement workflow, customers who have created natural language tests for a policy can start an iterative refinement run, letting the system deduce the changes needed for the policy to pass those tests. With the ambiguity reduction workflow, customers who frequently encounter ambiguous translation results can run the resolve policy ambiguities workflow to automatically refine variable descriptions and type definitions, reducing how often ambiguous translations occur. Both workflows are available through the Amazon Bedrock APIs and in the AWS Management Console, where customers can start a workflow by choosing Refine policy on the policy page.

These workflows are available in all AWS Regions where Automated Reasoning checks in Amazon Bedrock Guardrails are available. To learn more, visit the Amazon Bedrock Guardrails product page and the Automated Reasoning checks User Guide.

Amazon CloudWatch launches OTel Container Insights for Amazon EKS

CloudWatch OTel Container Insights for Amazon EKS collects infrastructure metrics at 30-second granularity using open-source receivers including cAdvisor, Kube State Metrics, and NVIDIA DCGM. Each metric carries OpenTelemetry semantic conventions and Kubernetes labels, making it straightforward to correlate across nodes, pods, and workloads in a single PromQL query.\n Pre-built dashboards give you immediate visibility into cluster health, node performance, and pod-level resource usage. The CloudWatch PromQL endpoint lets you connect existing Prometheus and Grafana dashboards directly to CloudWatch.

Enable it from the EKS console or via the CloudWatch Observability add-on (v6.2.0+), Helm, or CloudFormation.

Available in all commercial AWS Regions except Middle East (UAE), Middle East (Bahrain), and Israel (Tel Aviv). For pricing details, see the Amazon CloudWatch pricing page. To get started, see the OTel Container Insights documentation.

Claude Tag is now available in beta via Claude Enterprise in AWS Marketplace

Anthropic is launching Claude Tag — bringing Claude directly into the channels where your team already works, starting with Slack. Claude Tag is available today in beta to AWS customers who access Claude Enterprise through AWS Marketplace.\n Claude Tag is a new way for teams to work with Claude. Grant Claude access to selected channels, and connect it to whichever tools, data—and even codebases—you choose.. It’s multiplayer, so anyone in the channel can tag @Claude in, and delegate tasks to it while they focus on other work. Claude builds context by remembering relevant information from the channels it’s in, and can plan out tasks to complete in the future. And, for security and governance teams, Claude Tag operates under its own identity, scoped per channel, with spend controls and ambient mode off by default.

Getting started with Claude Enterprise on AWS Marketplace

The experience for Claude Enterprise in AWS Marketplace customers is identical to first-party Claude Enterprise: same setup, same capabilities, same controls. Consumption-based pricing tracks usage rather than headcount, with org-wide budget visibility and per-channel limits. Customers use their existing Claude Enterprise on AWS entitlement — an admin provisions the agent identity in the Claude admin console (approximately one hour) and scopes it per channel.

To learn more, see the Claude Enterprise in AWS Marketplace

Amazon OpenSearch Service now offers AI-assisted migrations

Migration Assistant for Amazon OpenSearch Service now includes an AI-assisted experience that simplifies moving your self-managed Apache Solr, Elasticsearch, or OpenSearch deployments to OpenSearch Serverless or Managed Clusters. With the new assistant, you can use your preferred AI tools like Kiro, Claude Code, and others to plan a migration, deploy necessary infrastructure, and execute both historical and live traffic migration.\n Migrations are often complex and require weeks of planning before any data movement can begin and even then, the process can be error-prone. We launched Migration Assistant in December 2023 to simplify migrating existing and live data from self-managed clusters to Amazon OpenSearch Service by automating manual migration tasks. The new AI-assisted experience takes this further: it provides an agent-guided workflow that helps you structure, execute, and validate your data migration faster and more reliably. Additionally, Migration Assistant for Amazon OpenSearch Service now supports live traffic capture and replay for Solr. To get started, see Migration Assistant documentation.

Migration Assistant supports migrations to OpenSearch Serverless and Managed Clusters from various Solr, Elasticsearch, and OpenSearch versions. For more details about the versions supported, see the documentation. Migration Assistant is available in all commercial AWS Regions and AWS GovCloud (US) Regions where Amazon OpenSearch Service is available.

Amazon SageMaker Studio notebooks now support G7e instance types

Amazon G7e instances feature up to 8 NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs, with 96 GB of memory per GPU, and 5th Generation Intel Xeon processors. They support up to 192 virtual CPUs (vCPUs) and up to 1600 Gbps of Elastic Fabric Adapter networking bandwidth. G7e instances support NVIDIA GPUDirect Peer to Peer (P2P) that boosts performance for multi-GPU workloads. Multi-GPU G7e instances also support NVIDIA GPUDirect Remote Direct Memory Access (RDMA) with EFAv4 in EC2 UltraClusters, reducing latency for small-scale multi-node workloads. Customers can use G7e instances to deploy large language models (LLMs), agentic AI models, multimodal generative AI models, and physical AI models. G7e instances offer the highest performance for spatial computing workloads as well as workloads that require both graphics and AI processing capabilities.\n Amazon EC2 G7e instances are available for SageMaker Studio notebooks in the AWS US East (N. Virginia and Ohio) and US West (Oregon) regions.

Visit developer guides for instructions on setting up and using JupyterLab and CodeEditor applications on SageMaker Studio. For pricing information on these instances, please visit our pricing page.

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