6/30/2026, 12:00:00 AM ~ 7/1/2026, 12:00:00 AM (UTC)

Recent Announcements

Amazon CloudWatch Logs enriches log events with AWS resource tags

Amazon CloudWatch Logs now enriches log events with resource tags, making it easier to filter, search, and analyze logs by the metadata that matters most to your organization, such as  team ownership, environment, cost center, or application name, without requiring changes to your logging instrumentation.\n With tag enrichment, Amazon CloudWatch Logs adds resource tags directly to your log events at ingestion time. You can immediately use tags in log queries, to scope your analysis without building custom pipelines or manually adding context to your application logs. For example, you can quickly filter all logs from production resources owned by a specific team, or filter by cost center during an incident investigation.

Tag enrichment for logs is available in all commercial AWS Regions except Middle East (UAE), Middle East (Bahrain), and Israel (Tel Aviv). To get started, enable resource tags on telemetry in the Amazon CloudWatch Settings, or through the AWS Command Line Interface (AWS CLI), and AWS SDKs to use your existing AWS resource tags to enrich your log events. Tag enrichment is available for no additional cost. Learn more on the Amazon CloudWatch documentation page.

AWS CloudFormation and CDK accelerate development feedback loops with pre-deployment validation on all stack operations

AWS CloudFormation customers can now get immediate feedback on deployment errors in seconds, eliminating the need to wait through a full provision-and-rollback cycle to discover preventable failures. CloudFormation now runs pre-deployment validation on Create Stack and Update Stack operations, catching common deployment errors before resource provisioning begins. This accelerates development velocity across all deployment workflows, from manual iteration to CI/CD pipelines to AI agents provisioning infrastructure.\n Previously, pre-deployment validation was available during change set creation, covering property syntax errors, resource name conflicts, and S3 bucket emptiness constraints. With this release, the same validations now run automatically on Create Stack and Update Stack operations. Additionally, three new validation checks are now available as warnings during change set creation. Service quota limits validation warns when creating resources would exceed your account’s service quotas. AWS Config Recorder conflict detection warns when your template adds Config rules to an account that does not have Config recording enabled, or defines a Config Recorder in an account where one is already active. ECR repository delete readiness validation warns when an ECR repository targeted for deletion still contains images. When validation detects an issue, you can view errors using the DescribeEvents API with the operation ID, or in the CloudFormation console by navigating to your stack’s Events tab and clicking the operation ID (or the link in the banner or status reason column) to open the Operation view page, which opens directly on the Deployment validations tab. Each error includes the logical resource ID and property path, so you can pinpoint and fix the problem before any resources are provisioned. In CDK, both cdk deploy and cdk validate surface validation results with construct-level tracing in a unified report, so AI agents and automation tools can parse structured responses and self-correct immediately. Pre-deployment validation is enabled by default on all stack operations with no configuration required. If you need to skip validation for a specific operation, use the new DisableValidation parameter on CreateStack, UpdateStack and CreateChangeSet API calls, or the –disable-validation flag in the CLI. Visit the Validate stack deployments User Guide to learn more. This feature is available in all AWS Regions where CloudFormation is supported, excluding China. Refer to the AWS Region table for service availability details.

AWS CloudFormation and CDK express mode speeds up infrastructure deployments by up to 4x

AWS CloudFormation and CDK express mode reduces deployment time by up to 4x for developers and AI agents building infrastructure, based on internal benchmarks. Express mode completes stack operations when CloudFormation confirms resource configuration is applied, rather than waiting for extended stabilization checks such as traffic readiness, region propagation, and resource cleanup. This enables faster iteration cycles for developers and AI agents building infrastructure.\n When iterating on infrastructure in development environments, developers and AI agents need faster iteration cycles to build infrastructure incrementally. Previously, every deployment waited for full resource stabilization regardless of whether the workflow required it. For example, creating a CloudFront distribution required waiting 5-10 minutes for propagation to all edge locations before the deployment completed, even when the developer only needed the distribution domain name to continue. With express mode, deployments complete in seconds once configuration is applied, and propagation continues in the background. CloudFormation still processes resources in dependency order and handles dependent resource failures within the same stack. Express mode disables rollback by default, enabling immediate fix-and-retry without waiting for rollback operations. To get started, set –deployment-config ‘{“mode”: “EXPRESS”}’ when creating, updating, and deleting stacks or creating a change set through the AWS CLI, AWS SDKs, or the AWS Management Console. For AWS CDK users, activate express mode with cdk deploy –express. No template changes are required. Express mode works with all existing CloudFormation templates, and nested stacks. Visit the CloudFormation Express mode documentation to learn more. This feature is available in all AWS Regions where CloudFormation is supported. Refer to the AWS Region table for service availability details.

Amazon RDS Enhances IAM Database Authentication with Connection Rate Scaling

Amazon Relational Database Service (Amazon RDS) now offers dynamic connection scaling for IAM database authentication, allowing connection rates to scale with instance resources.\n IAM database authentication performance now scales with available instance resources, enabling enterprise workloads to leverage IAM authentication for high-volume connection patterns. The number of new IAM authentication requests your instance can handle depends on available resources and workload characteristics. For optimal performance, we recommend reusing IAM user or IAM assumed role principals to generate authentication tokens, or reusing the authentication tokens themselves, when possible. This update is available in all AWS Regions, including the AWS GovCloud (US) Regions, where IAM database authentication is supported for Amazon Aurora and Amazon RDS database engines including PostgreSQL, MySQL, and MariaDB. To learn more, visit the IAM database authentication documentation.

AWS Parallel Computing Service supports in-place Slurm major version upgrades

AWS Parallel Computing Service (PCS) now supports managed in-place Slurm version upgrades for existing clusters. You can move your clusters up to three Slurm major versions ahead with no disruption to running jobs.\n To upgrade, update your Cluster configuration with your target Slurm version using the AWS Management Console, AWS CLI, or UpdateCluster API. PCS handles the upgrade of all managed Slurm components — the controller, accounting database, and REST API. Running jobs continue uninterrupted during the upgrade, queued jobs resume once the operation completes, and any accounting data is preserved in the database. You can then update your compute nodes to the new Slurm version at your convenience. Refer to the PCS User Guide for more information on the steps to follow and considerations to review based on your cluster configuration. AWS PCS is a managed service that simplifies running and scaling HPC workloads on AWS using Slurm. You can build complete, elastic environments that integrate compute, storage, networking, and visualization tools, while the service handles cluster operations with managed updates and built-in observability features. This feature is available in all AWS Regions where PCS is available. To get started, see the PCS User Guide.

Amazon ElastiCache T4g nodes now available in additional AWS Regions

Amazon ElastiCache now supports T4g node types in the following AWS Regions: Africa (Cape Town), Asia Pacific (Jakarta), Asia Pacific (Osaka), AWS GovCloud (US-East), and AWS GovCloud (US-West). T4g nodes are powered by AWS Graviton2 processors and provide a baseline level of CPU performance with the ability to burst CPU usage at any time, making them ideal for applications that experience temporary spikes in usage.\n 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 CLI, or API. To learn more, see Supported node types in the Amazon ElastiCache User Guide.

Amazon Neptune announces dual stack support with IPv6

Amazon Neptune now supports dual-stack mode, enabling database clusters to accept connections over IPv4, IPv6, or both protocols simultaneously. This allows organizations to adopt IPv6 while maintaining backward compatibility with existing IPv4 deployments.\n Neptune dual-stack mode supports two configurations. Private dual-stack mode provides IPv6 endpoints that remain isolated from the internet, suitable for internal applications and private graph databases. Public dual-stack mode enables IPv6 endpoints accessible from the internet, supporting internet-facing applications and hybrid network environments. Clients connect seamlessly using their preferred protocol with no application changes required. Dual-stack mode is available in all AWS Regions where Amazon Neptune is supported. To get started, see the Neptune setup documentation.

AWS End User Messaging RCS now supports rich media and interactive messaging

AWS End User Messaging now supports rich media and interactive messaging for RCS across all 22 supported countries. With the new SendRcsMessage API, you can send rich cards, carousels, images, videos, and interactive suggestion buttons that let recipients take action directly inside their messaging app.\n RCS message recipients can tap to confirm an appointment, browse a product catalog, complete a payment in a webview, share their location, or interact with an AI agent, all without leaving their phone’s messaging app. Behind each of these interactions is the same AWS infrastructure you already use to build applications. RCS becomes the interface layer that connects your backend services, your data, and your AI directly to your end users through your conversation with them.

With this release AWS now supports four RCS message types (text, files, rich cards, and carousels). These message types can be used with any combination of six actions (replies, URLs, webviews, phone calls, maps, and calendar events) to bring web and mobile app experiences directly into conversations.. Each message supports configurable SMS or MMS fallback for recipients without RCS.

AWS End User Messaging also introduces RCS Conversation pricing for 21 countries consisting of one flat rate for unlimited messages within a 24-hour session, so you can build back-and-forth workflows without per-message cost pressure.

RCS messaging is available in all AWS Regions where AWS End User Messaging is available. To learn more, see sending rich RCS messages in the AWS End User Messaging User Guide.

Amazon Time Sync Service adds support for Microsecond accurate time on 26 additional EC2 instance types in all commercial regions

Amazon Time Sync Service introduces support for microsecond accurate time on 26 additional EC2 instance types in all commercial regions. Built on Amazon’s proven network infrastructure and the AWS Nitro System, microsecond accurate time and nanosecond precision hardware timestamps leverage the reference clocks running in the Nitro System directly, enabling customers to easily order application events, measure 1-way network latency, and increase distributed application transaction speed. \n   Starting today, customers can access microsecond accurate time on these additional instance types by creating a Precision Time Placement Group (PTPG), a new placement strategy that allows customers to launch instances with Precision Time Protocol hardware clock (PHC) enabled. Customers that require both low network latency as well as precision time can associate a PTPG with their Cluster Placement Group (CPG), so that their low-latency workloads also benefit from microsecond accurate time.    For more information, refer to the Amazon Time Sync Service documentation.

Announcing general availability of Amazon WorkSpaces for AI agents

Amazon WorkSpaces for agents is now generally available, enabling AI agents to securely access and operate desktop applications through managed WorkSpaces environments. Enterprises run critical business processes on desktop applications (ERP systems, CRMs, mainframes, and proprietary tools) where years of customization, undocumented logic, and strict compliance requirements make them too critical to abandon and costly to modernize. WorkSpaces for agentsnow gives AI agents a managed cloud workspace where they can see the screen and operate these applications the way humans do, without requiring application modernization or custom integrations.\n WorkSpaces uses the same infrastructure for agents as organizations have trusted for over a decade to deliver secure, managed desktops at scale. Agents inherit the same identity controls, network isolation, and compliance boundaries as human users, so organizations gain automation without giving up governance. Organizations can automate workflows such as claims processing, patient record updates, trade settlement, and back-office operations. The service works with any agent framework using Model Context Protocol (MCP), and pricing scales based on active session time.

Since launching in Preview, customer and partner feedback has shaped new capabilities. MCP tool forwarding allows agents to interact with applications and the desktop operating system through direct MCP calls rather than using computer use tools, improving accuracy, reducing latency, and lowering cost. Real-time session control gives operators live visibility into agent activity with the ability to revoke access mid-session. Domain-joined fleet support lets agents operate under existing Active Directory identities, extending the same access policies and audit attribution that apply to employees.

To learn more, visit Amazon WorkSpaces for AI agents. To get started building, see the documentation and sample code on GitHub.

Amazon EC2 Auto Scaling now supports reservations then balanced Availability Zone distribution

Amazon EC2 Auto Scaling now offers reservations-then-balanced, a new Availability Zone (AZ) distribution strategy that prioritizes launching instances into your capacity reservations before distributing remaining capacity evenly across Availability Zones. This enables you to maximize utilization of pre-purchased capacity such as On-Demand Capacity Reservations (ODCRs), Capacity Blocks, and Interruptible Capacity Reservations, while retaining the operational simplicity and resilience of Auto Scaling.\n Starting today, you can configure reservations-then-balanced by setting the capacity distribution strategy in the AvailabilityZoneDistribution configuration of your Auto Scaling group and targeting reservations by Capacity Reservation Group ARN or by individual Capacity Reservation IDs. There is no additional charge to use reservations-then-balanced; you continue to pay standard EC2 pricing for your reservations and any On-Demand or Spot instances launched by the group.

Reservations-then-balanced is available today in all AWS commercial Regions. To learn more, visit the Amazon EC2 Auto Scaling User Guide.

Announcing Capability Insights for AWS, an open-source solution for regional capabilities

Today, AWS announces the launch of Capability Insights, an open-source solution that enables you to deploy regional capabilities data inside your own Amazon Virtual Private Cloud (VPC). This self-hosted dashboard addresses the needs of teams building multi-Region architectures requiring regional capabilities data deployed as infrastructure they own, inside their network, and under their governance. The solution is designed for organizations with data residency requirements, compliance teams needing internal reporting, and teams planning regional expansion or multi-Region recovery strategies.\n The dashboard auto-refreshes every 24 hours with AWS capabilities data across all Regions, covering services, features, API operations, and CloudFormation resource types. The Workload Analysis component scans your AWS CloudTrail logs and AWS CloudFormation stacks to filter 200+ services down to the number of services your account actually uses, reducing multi-week gap analysis to quick reviews. All data remains within your VPC perimeter, supporting compliance and data residency requirements while providing full ownership and control over the infrastructure hosting the regional capabilities data.

Amazon SageMaker AI cuts generative AI inference scale-out time by up to half with automatic container image caching

Amazon SageMaker Inference now supports container image caching, enabling up to 2x faster end-to-end scaling for generative AI models during scale-out events. When your endpoint scales out, the service pre-caches your container image so new instances can start serving traffic faster, without waiting for large container images to be pulled from Amazon ECR.\n Generative AI workloads typically use large container images (10 GB or more) for deep learning frameworks and model serving. Previously, every new instance launched during scale-out had to pull the full image from ECR, adding several minutes of cold-start latency. Container image caching eliminates this bottleneck by pre-pulling the image so new instances launch with the container already available locally. Customers don’t need to make any changes. The service automatically caches whatever image URI is specified in your endpoint or inference component configuration. This capability supports accelerator instance types, single-model endpoints, and inference component-based endpoints. With this launch, SageMaker Inference now offers a comprehensive scaling optimization suite for generative AI: sub-minute concurrency metrics for up to 6x faster load detection, instance-store container caching for faster scaling on existing instances, and container image caching for up to 2x faster scaling on new instances. Container image caching is available in all AWS commercial regions where SageMaker Inference is supported. To learn more, visit the launch blog.

AWS Security Hub CSPM launches AI Security Best Practices standard with 31 automated controls

Today, AWS Security Hub CSPM announces the AI Security Best Practices standard, a set of 31 automated security controls that detect when your deployed AI resources do not align with security best practices. Developed by AWS security experts, this standard helps you continuously evaluate your Amazon Bedrock, Amazon Bedrock AgentCore, and Amazon SageMaker workloads against recommended security configurations—without requiring manual assessments or custom rule authoring.\n The AI Security Best Practices standard covers critical security domains including but not limited to network isolation, encryption at rest and in transit, VPC placement, KMS key usage, private container registry requirements, and authorization controls. Controls span the breadth of AI infrastructure: from Bedrock AgentCore runtimes, gateways, memory stores, and custom browsers to SageMaker notebook instances, endpoints, models, monitoring jobs, and feature groups. Each control is assigned a security category and generates findings when resources deviate from best practices, enabling security teams to quickly identify and remediate misconfigurations across their AI workloads. The AI Security Best Practices standard is available in all AWS Regions where Security Hub CSPM is currently available, including AWS GovCloud (US) and the China Regions. The standard identifier is standards/ai-security-best-practices/v/1.0.0. To learn more, see the AWS Security Hub CSPM User Guide. You can also try Security Hub CSPM at no cost for 30 days with the AWS Free Tier.

IAM Identity Center now enables programmatic AWS account access for customer managed applications

IAM Identity Center now enables customer managed applications to programmatically access AWS accounts on behalf of their users, including the ability to discover accounts and roles assigned to a user and retrieve temporary credentials required for AWS account access.\n If you have a customer managed application that authenticates users through an external identity provider (IdP), you can configure that IdP as a trusted token issuer (TTI) in IAM Identity Center. With this launch, you can now enable AWS account access for this application. Users who have already signed in through the IdP can access their assigned AWS accounts and obtain temporary security credentials for their authorized roles without a separate authentication flow. This eliminates redundant sign-in prompts that previously required users to re-authenticate even after signing in through their external identity provider.

This feature is available for organization instances of IAM Identity Center. IAM Identity Center administrators must explicitly enable AWS account access for each customer managed application. Only management account administrators or delegated administrators can enable this capability, ensuring centralized governance over which applications can access account-level resources.

This feature is available in all commercial AWS Regions, the AWS GovCloud (US) Regions, and the China Regions. To get started, navigate to the IAM Identity Center console, select your customer managed application, and enable AWS account access. For more information, see Enable AWS account access for customer managed applications in the IAM Identity Center User Guide.

Claude Sonnet 5 now available on AWS

AWS now offers Claude Sonnet 5 - Anthropic’s most capable Sonnet model and the first Sonnet model of Anthropic’s latest generation - bringing top-tier intelligence at Sonnet pricing for coding, agents, and everyday professional work at scale.\n Claude Sonnet 5 delivers strong performance across coding, professional work, and agentic tasks while maintaining the balance of capability, cost, and speed that teams get from Sonnet. For coding, it navigates large codebases, lands multi-file changes, and carries debugging and refactoring tasks through to completion with fewer rounds of correction. For agents, it calls tools precisely, holds state across many steps, and recovers from errors so more runs finish correctly the first time. For knowledge work, it builds spreadsheets, drafts documents, and turns unstructured material into structured analysis. 

Customers have two ways to access Claude Sonnet 5: Amazon Bedrock and Claude Platform on AWS.

Amazon Bedrock keeps your data within AWS infrastructure and provides access to Claude Sonnet 5 through a unified service with AWS-managed features like Guardrails, Knowledge Bases, and regional data residency. To learn more, see the Amazon Bedrock documentation and regional availability. 

Claude Platform on AWS gives you direct access to Anthropic’s native platform experience and capabilities via the AWS Console. Build, test, and deploy with the same APIs, features, and console experience you’d get working with Anthropic directly, unified with AWS billing and authentication. To get started, see the Claude Platform on AWS documentation.

Amazon SageMaker AI now supports serverless model customization for Gemma 4 models

Amazon SageMaker AI now supports serverless model customization for Gemma 4 E4B and 31B models using supervised fine-tuning (SFT), direct preference optimization (DPO), and reinforcement fine-tuning (RFT). Gemma is a family of open models built by Google DeepMind. In addition to deploying these models on SageMaker AI, you can now adapt them to your specific domains and workflows. This launch also extends the variety of models available for serverless customization on SageMaker AI, including models from the Nova, Nemotron 3, Qwen, Llama, gpt-oss, and DeepSeek families.\n Model customization enables you to tailor these foundation models with your proprietary data, whether that’s improving accuracy on domain-specific tasks, aligning outputs with your organization’s tone, or enhancing performance on new tasks using your labeled data. With serverless customization, SageMaker AI handles all infrastructure provisioning and training orchestration, so you can focus on your data and evaluation rather than cluster management, and only pay for what you use. Serverless model customization on SageMaker AI is available in US East (N. Virginia), US West (Oregon), Asia Pacific (Tokyo), and EU (Ireland). To get started, navigate to the Models page in Amazon SageMaker Studio to launch a customization job, or use the SageMaker Python SDK for programmatic access. To learn more, see the Amazon SageMaker AI model customization documentation.

Amazon EC2 C9g and C9gd compute optimized instances are now available

Starting today, Amazon Elastic Compute Cloud (Amazon EC2) C9g and C9gd instances, powered by AWS Graviton5 processors, are generally available. AWS Graviton5 processors are the fifth generation of custom-designed CPUs, delivering the best price performance for compute-intensive workloads running on Amazon EC2.\n C9g instances are ideal for workloads such as high-performance computing (HPC), batch processing, gaming, video encoding, scientific modeling, distributed analytics, CPU-based machine learning (ML) inference, real time analytics, and ad serving. C9gd instances offer local NVMe-based SSD block-level storage for customers running compute-intensive workloads that also require high-speed, low-latency local storage for scratch space, temporary files, and caches.

C9g and C9gd instances deliver up to 25% better compute performance compared to AWS Graviton4-based C8g and C8gd instances. They are up to 30% faster for databases, up to 35% faster for web applications, and up to 35% faster for machine learning. They feature 5x larger cache and the fastest memory of any processor instances in the cloud. These instances are built on the sixth-generation AWS Nitro System and are the first to feature the Nitro Isolation Engine, harnessing formal verification to provide mathematical assurance that customer workloads are isolated from each other and AWS operators, pioneering a new standard for mathematically proven cloud security. 

C9g and C9gd instances are available in US East (N. Virginia, Ohio), US West (Oregon), and EU (Frankfurt) regions. C9g and C9gd instances are available for purchase via Savings Plans, On-Demand, Spot instances, Dedicated instances, or Dedicated hosts.

Level up your compute with AWS Graviton and get started today.

Claude Opus 4.8 is now available in AWS GovCloud (US)

AWS GovCloud (US)  now offers Claude Opus 4.8 – Anthropic’s most capable generally available model to date – delivering meaningful advances across agentic coding, professional knowledge work, and long-running autonomous tasks for developers and enterprises building production AI applications.\n Claude Opus 4.8 can perform longer autonomous runs, deeper reasoning, and consistency to be trusted with production work. For coding, the Opus 4.8 reads codebases like an engineer, plans before it edits, and holds context across long sessions in real repositories. For agentic tasks, it is better at finding paths around obstacles instead of stalling, recovering from its own errors, and knowing when to ask for help versus when to keep going. For knowledge work, it better synthesizes across long documents and complex sources, self-checks its output, and delivers structured deliverables that hold up to review.

Amazon Bedrock keeps your data within AWS infrastructure and provides access to Claude Opus 4.8 through a unified service with AWS-managed features like Guardrails, Knowledge Bases, and regional data residency. To learn more, see Amazon Bedrock documentation  and regional availability.

OpenAI GPT-5.4 and NVIDIA Nemotron 3 Super 120B now available on Kiro in AWS GovCloud (US-West) Region

Two new models are now available in the Kiro IDE and CLI for the AWS GovCloud (US-West) Region.\n OpenAI GPT-5.4 is now available in Kiro for complex reasoning, coding, document analysis, and multi-step agentic workflows. It helps developers build AI applications and production workflows that can interpret context, interact with tools, operate software environments, and verify outputs across multiple steps. GPT-5.4 runs on Amazon Bedrock’s next-generation inference engine with isolated queues and durable execution for resilient workloads. Available with a 272K context window and 1.2x credit multiplier. NVIDIA Nemotron 3 Super 120B is now available in Kiro as an open weight model option. A hybrid mixture-of-experts model activating only 12B of its 120B parameters for high compute efficiency and fast inference on agentic tasks. 256K context window with 32K max output. Available with a 0.25x credit multiplier. Ensure your IDE or CLI is updated to the latest version, then restart it to access the new models from the model selector. For more details about Kiro in AWS GovCloud (US), visit the GovCloud documentation or contact your AWS account team for more information. To learn more about Kiro, visit the Kiro product page.

Amazon GameLift Servers announces DDoS Protection client SDKs for C# and Unity

Amazon GameLift Servers now offers DDoS Protection client SDKs for C# and Unity, helping game developers protect session-based multiplayer games against denial-of-service and distributed denial-of-service attacks. This feature co-locates a relay network directly alongside your game servers and uses access token-based authentication to ensure only authorized client traffic reaches your servers. Game developers building multiplayer experiences can now defend against targeted disruptions to specific players or entire game sessions.\n DDoS Protection provides proactive UDP-based traffic protection with negligible latency and is available at no additional cost to Amazon GameLift Servers customers. The feature enforces per-player traffic limits to prevent disruptions even from seemingly legitimate sources, eliminating the need for manual byte matching. The new client SDKs for C# and Unity join existing support for C++ and Unreal Engine, giving developers flexibility to implement protection regardless of their game engine or language.

Amazon GameLift Servers DDoS Protection is available in US East (N. Virginia), US West (Oregon), Europe (Frankfurt), Europe (Ireland), Asia Pacific (Sydney), Asia Pacific (Tokyo), and Asia Pacific (Seoul). To learn more, visit the Amazon GameLift Servers documentation.

AWS Service Availability Updates

We’re announcing availability changes to the following AWS services and features.\n Services moving to Maintenance

Services moving to maintenance will no longer be accessible to new customers starting July 30, 2026. Customers already using these services and features can continue to do so. AWS will continue to operate and support these services and features. We recommend that customers learn about the changes in the product pages and documentation.

·       Amazon Bedrock Agents (launched November 2023) is now Amazon Bedrock Agents Classic

·       Amazon Cognito Sync

·       Amazon Kendra

·       Amazon Q Business

·       AWS Directory Service – Simple AD

·       AWS IoT Device Defender – Detect (feature will no longer be accessible to new customers starting August 31, 2026)

·       AWS Mainframe Modernization – Self-Managed Experience

·       AWS Management Console – myApplications

·       AWS Resource Groups – Group Lifecycle Events

·       AWS Service Catalog – Application Registry

·       AWS Systems Manager – Application Manager

 Amazon SageMaker AI Features

o   A2I

o   Clarify

o   Debugger

o   GeoSpatial

o   Ground Truth

o   Mechanical Turk

o   Model Monitor

o   Profiler

o   Role Manager

o   Studio Lab

 

Services entering Sunset

The following services are entering sunset, and we are announcing the date upon which we will end operations and support of the service. Customers using these services should click on the links below to understand the sunset timeline and begin planning migration to alternatives as recommended in the updated service web pages and documentation.

·       Amazon WorkSpaces – PCoIP

·       Amazon WorkSpaces - Pool

·       AWS Managed Services (AMS) Advanced

·       AWS re:Post Private

 

Services reaching End of Support

The following services have reached end of support and are no longer available as of June 30, 2026.

·       Amazon Chime SDK – Carrier Voice Focus

·       Amazon SageMaker AI – Ground Truth Plus

        AWS Elemental MediaLive and MediaPackage (Select Regions only)

 

For customers affected by these changes, we’ve prepared comprehensive migration guides, and our support teams are ready to assist with your transition. Visit AWS Product Lifecycle Page to learn more, and subscribe to the RSS feed for future updates.

AWS Clean Rooms now supports intermediate tables for SQL

AWS Clean Rooms now supports intermediate tables for SQL queries, offering increased flexibility for organizations running complex, multi-step analytical workflows with their partners.  With this launch, customers can write the results of a SQL query to an intermediate table within a collaboration for reuse in subsequent analyses. Intermediate tables enable multi-step analytical workflows — from reusing complex joins to building shared ID mapping tables for downstream analyses — all within the privacy boundary of the collaboration. For example, a publisher and an advertiser can join their first-party data to build an ID mapping table in a collaboration, then reuse it across reach, frequency, and attribution analyses, reducing costs and optimizing performance for the subsequent analyses.\n AWS Clean Rooms helps companies and their partners easily analyze and collaborate on their collective datasets without revealing or copying one another’s underlying data. For more information about the AWS Regions where AWS Clean Rooms is available, see the AWS Regions table. To learn more about collaborating with AWS Clean Rooms, visit AWS Clean Rooms.

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