10/21/2026, 12:00:00 AM ~ 10/22/2026, 12:00:00 AM (UTC)
Recent Announcements
AWS Transform adds storage migration assessments for Amazon S3, EBS, EFS, and FSx
Migration assessments in AWS Transform now evaluate on-premises storage for migration to AWS. Instead of manually analyzing thousands of volumes across protocols, capacity, and performance, you upload a storage inventory and AWS Transform does the mapping for you. It matches each workload to the best-fit service, sizes the recommendation, and projects costs to produce a total cost of ownership business case. Bring NAS shares, SAN LUNs, direct-attached disks, or backup repositories via a NetApp Digital Advisor export, RVTools file, or other inventory. AWS Transform routes workloads across Amazon S3, Amazon EBS, Amazon EFS, and Amazon FSx (NetApp ONTAP, Windows File Server, OpenZFS, Lustre). Run what-if scenarios to compare deployment options such as Single-AZ vs. Multi-AZ or cost-optimized vs. performance-optimized, then ask follow-up questions about any result in chat.\n Storage migration assessments are available in all AWS Regions where AWS Transform is available. To learn more, see the AWS Transform User Guide.
AWS Direct Connect announces 100G expansion in Manchester, England
Today, AWS announced the expansion of 10 Gbps and 100 Gbps dedicated connections with MACsec encryption capabilities at the existing AWS Direct Connect location in the Equinix MA3 data center near Manchester, England. You can now establish private, direct network access to all public AWS Regions (except those in China), AWS GovCloud Regions, and AWS Local Zones from this location. This is the third AWS Direct Connect location in England to provide 100 Gbps connections with MACsec encryption capabilities.\n The Direct Connect service enables you to establish a private, physical network connection between AWS and your data center, office, or colocation environment. These private connections can provide a more consistent network experience than those made over the public internet.
For more information on the over 150 Direct Connect locations worldwide, visit the locations section of the Direct Connect product detail pages. Or, visit our getting started page to learn more about how to purchase and deploy Direct Connect.
Amazon Aurora and Amazon RDS now support On-Demand Capacity Reservations
Starting today, you can reserve compute capacity for your Amazon Aurora and Amazon RDS DB instances in one or more Availability Zones for as long as you need it. Reserving capacity helps ensure that DB instance create, restore, scale-compute, and start operations don’t fail because of insufficient capacity.\n On-Demand Capacity Reservations are designed for workloads that need assurance that specific DB instance capacity will be available when they need it for business-critical events, such as migrations, product launches, seasonal peaks, high-availability deployments, or disaster recovery. You choose the DB instance class, engine, Availability Zones, and reserved capacity per Availability Zone, and RDS holds that capacity for you. Capacity can be reserved in two or more Availability Zones to support Multi-AZ deployments. To use the reserved capacity, reference the reservation when you create, restore, modify, or start a DB instance.
You can reserve capacity for immediate use with no commitment and cancel at any time, or schedule a reservation to begin 5 to 120 days in the future with a minimum commitment. Reserved capacity is billed at On-Demand rates while the reservation is active, whether or not DB instances within the reservation are running. Discounts from your existing Reserved DB Instances or Database Savings Plans apply automatically.
On-Demand Capacity Reservations are available for Amazon Aurora and Amazon RDS in all AWS commercial Regions. To learn more about On-Demand Capacity Reservations, visit the documentation for Amazon Aurora and Amazon RDS.
Amazon Cognito extends multi-Region replication to AWS GovCloud (US) Regions
Amazon Cognito multi-Region replication (MRR) now supports AWS GovCloud (US) Regions, extending the resilience benefits of MRR to more workloads. MRR lets you synchronize user and machine identity data, including credentials, user pool configurations, and federation setups, to a secondary user pool in a standby Region you designate, in near real-time. With user data in the standby Region, you can redirect traffic and keep authentication available if there is a disruption in your primary Region.\n Previously, MRR was available in 16 commercial Regions. You can now also use MRR to replicate between additional AWS Regions, helping you meet resilience, data residency, and compliance requirements in the Regions that best align with your business needs. MRR is available as an add-on for user pools in the Essentials or Plus feature tiers, and requires a multi-Region AWS KMS customer managed key (CMK). For the full list of AWS Regions where Amazon Cognito is available, see AWS Capabilities by Region. To get started, visit the developer guide for instructions and the pricing page for pricing details.
Beam model by Reflection is now available on Amazon SageMaker JumpStart and AWS Trainium
Beam, the first open weight model offered by US-based Reflection, is now available in Amazon SageMaker JumpStart, the model hub in Amazon SageMaker AI. According to Beam, they operate at the frontier of open weight models in its class, with strong performance across reasoning, coding, and agentic benchmarks. Beam is for teams who build on and with an open weight model, not just call one. Customers who want to adapt a frontier model to their own domain can fine-tune it, inspect and modify its weights, and embed it deep in their agents and workflows, all while keeping full control of their data and infrastructure. Beam runs alongside other models that customers already use in SageMaker AI and scales to meet production demand with the same controls, audit trails, and monitoring built into every SageMaker AI deployment. Additionally, Beam was ported and optimized for AWS Trainium3, giving customers a high-performance, cost-efficient option for deploying and scaling the model from day one. “Reflection is delivering frontier AI that developers can actually control, not just rent through an API. Bringing Beam to SageMaker AI puts that capability in front of millions of developers on infrastructure they already trust, running in their own AWS account, on their own data, fully in their control," said Misha Laskin, co-founder and CEO, Reflection.\n Teams building on Beam can take it well beyond calling an API: fine-tune it on proprietary code and data, specialize it for their own domains, and wire it into multi-step agentic workflows across their tools. In practice that means resolving real software issues, generating and reviewing code, automating operations work, and tackling advanced math and science reasoning on a model they can shape rather than one they simply consume. Because Beam is released under the Apache 2.0 license, customers can inspect and customize the model, while also running it in their own AWS accounts, so data stays private and under their control, protected by the same AWS security and compliance capabilities they already rely on. “Advanced AI models like Beam, now available on Amazon SageMaker, let us run open weight frontier AI entirely inside our own secure environments, with full visibility into the weights, the data, and the pipeline. In the age of multi-model AI, the pairing of openness and managed infrastructure is an essential addition to the AI ecosystem. The freedom to deploy and adapt advanced AI, tailored to mission problems with client-specific security and controls, gives more agencies greater flexibility to deploy advanced AI across their enterprise,” said Garrett Berntsen, Chief AI Officer, Accenture Federal Services.
Beam is initially available in Amazon SageMaker JumpStart in US East (N. Virginia) and US East (Ohio). The model is available in BF16, FP8, and NVFP4 checkpoint variants, and can be deployed through a fully managed SageMaker JumpStart endpoint. To start, search for “Beam” in SageMaker JumpStart within Amazon SageMaker Studio, or deploy it directly on Sagemaker AI. To learn more, read the AWS Machine Learning blog: Introducing Reflection Beam, SageMaker AI documentation and SageMaker JumpStart documentation for deployment guidance.
AWS Neuron 2.33 adds native PyTorch support, Neuron Observer, and other new features
AWS Neuron 2.33 is now available, introducing TorchNeuron, the native PyTorch backend for AWS Trainium, in public beta. You can run models written with the standard PyTorch APIs on Amazon EC2 Trn2 and Trn3 instances, in eager mode or with torch.compile. This release also introduces Neuron Observer in public beta, which detects Trainium health issues in under a second and identifies the affected node or device.\n The vLLM Neuron plugin upgrades to vLLM 0.29.0, and the vLLM Omni Neuron plugin for Wan2.2 video generation is now part of the Neuron release. Both support native PyTorch compilation through TorchNeuron. A new Neuron Deep Learning AMI and Deep Learning Container for PyTorch training include TorchNeuron, PyTorch 2.14, and TorchTitan. Neuron Explorer opens profiles captured with torch.profiler. From a PyTorch memory snapshot, it now attributes allocations to the Python source lines that made them, for debugging out-of-memory errors in eager mode.
Kernels written with the Neuron Kernel Interface (NKI) can now use the full Python language for metaprogramming, including generators and class inheritance. The NKI Library adds 25 new experimental kernels, including kernels for DeepSeek-V3.2, Qwen3.5, and Kimi Delta Attention.
To get started, see the following resources:
AWS Neuron 2.33.0 Release Notes
AWS TorchNeuron documentation
AWS Neuron Observer documentation
AWS Neuron Kernel Interface documentation
AWS Neuron
Amazon Connect Customer launches two new layouts for agent workspace
Amazon Connect Customer now offers two different layouts that optimize screen space on the agent workspace. Contact center agents spend hours each day in the agent workspace, which includes all of the contact controls and applications they need to resolve customer issues. Agents who handle different channels and workflows want the workspace arranged differently, so that they can quickly read, respond, and access their tools. Now, administrators can choose the best layout for each team, maximizing agent efficiency and satisfaction.\n On the Modern layout, contact controls are at the top of the workspace, maximizing screen space for the applications beneath during voice calls. When agents handle chat, email, and task contacts on the Modern layout, they now have a taller, wider area to read and respond. The Classic layout offers a familiar experience for agents already using the agent workspace, now with contact controls that expand and contract by channel and a resizable agent assist application. Administrators can assign the Modern layout to a team migrating from another agent desktop with controls at the top, while teams already familiar with the agent workspace can use the Classic layout.
You can use the new layouts for the Amazon Connect Customer agent workspace in the following AWS regions: US East (N. Virginia), US West (Oregon), Africa (Cape Town), Asia Pacific (Seoul), Asia Pacific (Singapore), Asia Pacific (Sydney), Canada (Central), Europe (Frankfurt), and Europe (London).
To learn more and get started, see the agent workspace in the Amazon Connect Customer administrator guide.
Amazon S3 server access log delivery to Amazon CloudWatch Logs is now available in the AWS GovCloud (US) Regions, AWS European Sovereign Cloud, and AWS China Regions. With your access logs in CloudWatch Logs, you can query them as they arrive, set alarms, and encrypt them with AWS Key Management Service (AWS KMS) keys. This new delivery path complements the existing delivery of server access logs to S3 general purpose buckets, giving you more flexibility in how you monitor and analyze access to your data.\n With delivery to CloudWatch Logs, you can set alarms on error rates, monitor traffic patterns, investigate access incidents, and correlate S3 access activity with the rest of your operational data.
S3 server access log delivery to CloudWatch Logs is now available in all AWS Regions. Amazon CloudWatch Logs charges for vended logs apply. For pricing details, visit Amazon CloudWatch pricing. To learn more, visit the Amazon S3 User Guide.
Amazon SageMaker AI now supports custom sequence lengths for serverless model customization jobs
Today, Amazon SageMaker AI announces the ability to configure the sequence length used during your serverless model customization job. Amazon SageMaker AI serverless model customization supports fine-tuning for many open-weight models, including models from popular families including gpt-oss, Gemma, Llama, Nemotron, and Qwen. Previously, each fine-tuning recipe ran at a fixed sequence length that customers could not modify. Customers sometimes need to modify these parameters to achieve better results for their use case. Customers also asked for longer sequence lengths than the static defaults we supported on some models.\nNow, you can set sequence length parameters as part of the customization job’s hyperparameters, anywhere up to the published maximum for your chosen model and technique. You can set the dataset_max_len hyperparameter for SFT and DPO jobs or set max_prompt_length and max_response_length hyperparameters for RFT jobs. This gives you more control to match your training data characteristics, whether you are working with short question-answer pairs or multi-page documents. Published maximums are up to 16K tokens for many models depending on model and technique. Serverless model customization on SageMaker AI is available in US East (N. Virginia), US West (Oregon), Asia Pacific (Tokyo), and Europe (Ireland). To get started, navigate to the Models page in Amazon SageMaker Studio to launch a customization job, use the SageMaker Python SDK or use our Agent skills with your choice of coding assistant. To learn more, and see the list of models and supported sequence lengths per technique, see the SageMaker AI documentation.
AWS Network Firewall now supports Route 53 Resolved IP Validation
AWS Network Firewall now supports IP validation, extending domain-based filtering beyond HTTP/HTTPS to protocols such as SFTP and SSH. This new capability validates that a connection’s destination IP address was resolved through your VPC’s Amazon Route 53 VPC Resolver, closing security gaps where clients could bypass domain rules.\n This feature enables Network Firewall customers to implement domain filtering rules for any protocol, not just HTTP/HTTPS, providing more flexible security policies than IP-based rules alone. It simplifies protection against Server Name Identifier (SNI) spoofing by validating the IPs resolved by the Route 53 VPC Resolver against the SNI, ensuring traffic only reaches legitimately resolved destinations. Organizations can now enforce consistent domain-based security policies across all outbound traffic types, from web applications to file transfer protocols, without complex workarounds. Additionally, Route 53 Resolver DNS Firewall can be used with your VPC Resolver for extended DNS security.
Resolved IP validation is available in all AWS commercial regions where AWS Network Firewall and Route 53 Resolver DNS Firewall are available, at no additional charge. For a full list of supported regions, visit the AWS Capabilities by Region page.
To learn more, visit AWS Network Firewall product page and service documentation.
AWS Blogs
AWS Japan Blog (Japanese)
- Optimizing Security Operations with AWS Security Incident Response
- How did Kiro amend himself and how does he guarantee the correctness of those revisions
- Kiro IDE and CLI now have memory features
- Proactively control research costs with the AWS DevOps Agent
- Budget/rate limit management for OpenAI ChatGPT Codex with Amazon Bedrock and LitellM
- Balancing speed and safety: a control framework for AI coding agents
- How to safely update packages for npm and pip on Amazon Linux
- Software supply chain security best practices based on the AWS Well-Architected Framework
AWS Architecture Blog
AWS Cloud Financial Management
AWS Cloud Operations Blog
AWS Contact Center
- How to build international two-way SMS with Amazon Connect Customer, Part 1
- Routing Screen Real Estate: Your Agent Workspace Is the Next Routing Decision
Containers
Artificial Intelligence
- Securing Quick access from corporate networks with AWS PrivateLink
- 5 production challenges in multi-agent systems and how Deep Insight solved them
- Adaptive agentic intelligence framework using Strands Agents
- Train Qwen3 on AWS Trainium with torchtitan
- How U.S. Hunger connects leaders and data using Amazon Quick
AWS Messaging Blog
AWS Quantum Technologies Blog
AWS Security Blog
- Amazon Threat Intelligence identifies PHP2Shell exploitation prior to public disclosure in MadPot
- Best practices for integrating IAM Roles Anywhere with your PKI
AWS Storage Blog
- How Proscia built unlimited observability with Amazon S3 and the Altinity Kubernetes Operator
- Updating encryption on existing Amazon S3 objects with S3 Batch Operations
- Preserve FlexClone space efficiency when migrating to Amazon FSx for NetApp ONTAP