9/11/2026, 12:00:00 AM ~ 9/14/2026, 12:00:00 AM (UTC)
Recent Announcements
Amazon EC2 X2idn instances are now available in Asia Pacific (Hong Kong)
Memory-optimized Amazon Elastic Compute Cloud (Amazon EC2) X2idn instances are now available in Asia Pacific (Hong Kong) Region. These instances, powered by 3rd generation Intel Xeon Scalable Processors and built with AWS Nitro System, are designed for memory-intensive workloads and deliver improvements in performance compared to previous generation X1 instances. These instances are SAP-certified for running Business Suite on HANA, SAP S/4HANA, Data Mart Solutions on HANA, Business Warehouse on HANA, SAP BW/4HANA, and SAP NetWeaver workloads on any database.
Amazon SageMaker HyperPod now supports model caching, an inference optimization that pre-loads model weights and container images onto cluster nodes so pods start in seconds instead of minutes.\n When running LLM inference at scale for workloads like chat assistants, agentic pipelines, RAG, and document analysis, cold start is a real bottleneck. Deployments and scale-out events spend most of their time downloading container images and model weights. As model size increases, this gets worse, with large models taking tens of minutes before they can serve traffic.
Model caching solves this with two independent capabilities. The weights cache stores model weights on local NVMe so pods read from fast local storage instead of pulling from S3 or FSx over the network. The image cache pre-pulls the container image so pods skip the ECR download entirely. If a pod lands on a node without a warm cache, it falls back to pulling from the original source automatically, so there is no risk of pods getting stuck or failing.
Benchmarks across models from 57 GB to 145 GB show around 60% faster scale-out, and the image cache cuts over two minutes of image-pull time (97% reduction). The benefit grows with model size while retaining the reliability of the original source path.
Customers enable model caching through the HyperPod Inference Operator by adding a modelCacheConfig section to their InferenceEndpointConfig or JumpStartModel resource. The operator handles the full lifecycle with no manual setup or cleanup.
Model caching is now generally available in all regions where SageMaker HyperPod is available. To get started, see the SageMaker HyperPod documentation.
AWS announces the availability of TwelveLabs Marengo 3.0 as an embedding model in Amazon Bedrock Managed Knowledge Base, enabling customers to create multimodal embeddings for video, audio, and image content. Amazon Bedrock Managed Knowledge Base already supports media search by transcribing audio and video to text and generating text-based embeddings—Marengo 3.0 goes further by encoding visual scenes, speech, and video cues directly into multimodal embeddings, capturing meaning that transcription alone cannot. Simply upload your media assets from data sources such as Amazon S3, sync, and search using natural language—with no infrastructure to manage.\n Marengo 3.0 produces compact 512-dimensional vectors, delivering state-of-the-art retrieval accuracy. Results include segment start and end times, enabling applications to jump directly to the relevant moment in a video. This unlocks use cases across sports analytics, media and entertainment, security, education, and retail—from finding specific plays across seasons of game footage to locating lecture segments by concept rather than keywords. The model offers configurable segmentation options to match your content structure. To learn more, see TwelveLabs Marengo 3.0 embedding model integration in the Amazon Bedrock Knowledge Base User Guide. For more information, visit the Amazon Bedrock Knowledge Bases product page.
AWS HealthOmics now publishes real-time run metrics to Amazon CloudWatch
AWS HealthOmics now publishes real-time run metrics to Amazon CloudWatch, giving you visibility into workflow resource utilization as runs execute. The 14 new run metrics span CPU and GPU usage, memory usage, file system usage and I/O metrics, network throughput, and ephemeral storage usage. AWS HealthOmics is a HIPAA-eligible service that helps healthcare and life sciences customers accelerate scientific breakthroughs at scale with fully managed bioinformatics workflows.\n Real-time run metrics can help you identify CPU or GPU bottlenecks, detect memory or storage exhaustion before a task fails, and track file system throughput, all without opening a support case. By comparing actual usage against allocated resources, you can right-size the compute and storage configurations for your workflows. These metrics are emitted using the Amazon CloudWatch OpenTelemetry standard, so you can integrate them with third-party observability tools in addition to native CloudWatch dashboards and alarms.
Real-time run metrics are now available in the following AWS HealthOmics Regions: US East (N. Virginia, Ohio), US West (Oregon), Europe (Frankfurt, Ireland, London), and Asia Pacific (Singapore, Seoul, Tokyo). To learn more, visit the Monitoring run metrics with CloudWatch documentation. Amazon CloudWatch charges for these run metrics based on the volume of metric data ingested. For more information on pricing, visit Amazon CloudWatch pricing.
AWS DevOps Agent adds support for bidirectional Slack communication
AWS DevOps Agent now enables engineers to manage production operations across AWS, multicloud, and on-premises environments with the complete investigation lifecycle directly within Slack. Previously, on-call engineers and DevOps teams faced fragmented workflows during high-severity incidents, having to context-switch between communication and investigation platforms. This update consolidates both surfaces into a single, unified location where incident response already happens.\n Engineering teams can now initiate and steer investigations by simply @mentioning AWS DevOps Agent in any connected Slack private channel. All investigation activity, including team-contributed context, agent findings, and recommended actions, is captured within a single thread. Ask questions about AWS resources, system metrics, alarm status, deployment history, and incident patterns, reducing cognitive load during critical moments and making production operations significantly more efficient.
This capability is available in all commercial AWS Regions where AWS DevOps Agent is currently supported.
To get started, read the user guide. See all the latest AWS DevOps Agent features on the release history page.
Amazon RDS for Oracle now supports Supplemental Patch Bundle for July 2026 Release Update
Amazon Relational Database Service (Amazon RDS) for Oracle now supports the Supplemental Patch Bundle (SPB) for the July 2026 Release Update (RU) for Oracle Database version 19c and 26ai. \n Starting with April 2026 releases, the Oracle Spatial Patch Bundle has been renamed to Supplemental Patch Bundle (SPB). The SPB includes additional database patches recommended by Oracle for specific use cases, such as Oracle Spatial, Oracle Data Pump, and Oracle GoldenGate. For details, see Release Updates (RUs) and Supplemental Patch Bundles (SPBs).
You can apply the Supplemental Patch Bundle update for new database instances, or upgrade existing instances to engine version 19.0.0.0.ru-2026-07.spb-1.r1 or 26.0.0.0.ru-2026-07.spb-1.r1 by selecting the “Supplemental Patch Bundle Engine Versions” checkbox in the AWS Console. To learn more about Oracle SPBs supported on Amazon RDS for each engine version, see the Amazon RDS for Oracle Release notes.
You can also use AWS Organizations upgrade rollout policy to stagger automatic minor version upgrades for your Amazon RDS database instances. This feature allows you to automatically apply updates to non-production environments, validate the updates, and then automatically apply the same update to production environments. For additional details about using AWS Organizations upgrade rollout policy for automatic minor version upgrades, refer to Amazon RDS for Oracle documentation.
AWS Blogs
AWS Japan Blog (Japanese)
AWS Architecture Blog
AWS Database Blog
AWS for Industries
- Mirroring Your Organization with Multi-Agent AI for FSI Risk Assessment
- From Days to Minutes: How we built Multi-Agent KYC/KYB on AWS
Artificial Intelligence
- Monitoring production agent lifecycle with AWS DevOps Agent and AgentCore Evaluations
- Beyond the price per token: Choosing the right OpenAI model on Amazon Bedrock for your workload
- Build interactive MCP Apps using Amazon Bedrock AgentCore
AWS Storage Blog
- Build AI-powered file classification with AWS Transfer Family
- Enable zero-copy access to AWS services on Amazon FSx for NetApp ONTAP with Amazon S3 Access Points