10/15/2026, 12:00:00 AM ~ 10/16/2026, 12:00:00 AM (UTC)

Recent Announcements

Web Search on Amazon Bedrock AgentCore expands to AWS GovCloud and 3 Regions

Web Search on Amazon Bedrock AgentCore is now available in four additional AWS Regions: US East (Ohio), US West (Oregon), Asia Pacific (Sydney), and AWS GovCloud (US-West). Web Search is a fully managed tool that provides AI agents with current grounding from the web, enabling more accurate and up-to-date responses for tasks such as research, summarization, and question answering.\nWith this expansion, customers can build agents that retrieve web information closer to their applications and end users with lower latency. Customers with government workloads can now use Web Search in the AWS GovCloud (US-West) Region to meet compliance and data residency requirements.  To learn more about Web Search on Amazon Bedrock AgentCore, see the Web Search tool documentation. To learn about pricing, visit Amazon Bedrock AgentCore pricing. For Region availability, visit Supported AWS Regions.

Amazon SageMaker Studio simplifies getting started for new AI/ML builders with new sign-in options

Amazon SageMaker Studio now lets new customers sign up with their Google, GitHub, or Apple credentials and land inside a fully configured Studio environment - no traditional AWS account setup and no console navigation required.\nPreviously, new customers who discovered a model on Hugging Face or an AWS product page had to complete a multi-step AWS account creation process, provide a credit card, navigate the AWS Console to find SageMaker AI, configure an environment, and set up IAM permissions before they could begin working. With earlier launches, we reduced environment setup to twenty seconds, pre-configured model customization permissions, and introduced deep links from Hugging Face and AWS product pages so customers could land directly inside Studio with a model pre-loaded. Now, with new sign-in options, customers complete a simplified sign-up using an existing identity, receive free credits as part of the AWS Free Tier with no credit card required in most cases, and arrive inside a ready-to-use SageMaker Studio environment where they can immediately fine-tune, evaluate, and deploy foundation models. Customers can invite team members by email, and collaborators sign in with their own email credentials to access the shared Studio environment, including shared models, notebooks, and endpoints without configuring IAM users or permissions. When a project grows beyond free credits, customers can upgrade to a paid plan and set a spend limit to stay within budget. Verified customers continue to receive default GPU access to G5, G6, G6e, and G4dn instances across endpoint deployments, training jobs, and notebooks without requesting quota increases. This feature is available in all AWS Commercial Regions where Amazon SageMaker Studio is supported. To get started, visit any supported model on Hugging Face and select “Customize and Deploy on SageMaker AI” or “Deploy on SageMaker AI,” or click Get Started from the SageMaker Studio page. To learn more, see the AWS sign-up documentation and SageMaker Studio setup guide.

Amazon EC2 T8i instances are now available in South America (São Paulo)

Amazon Elastic Compute Cloud (Amazon EC2) T8i instances are now available in South America (São Paulo). These instances are powered by custom sixth generation Intel Xeon 6 processors, available exclusively on AWS, and built on the latest sixth generation AWS Nitro cards. T8i instances deliver up to 30% better price performance compared to previous generation T3 instances, and offer four sizes: nano, micro, small, and medium. These instances are designed for workloads requiring low to moderate CPU utilization, including data processing, login gateways, small databases, batch processing, event driven functions, CI/CD pipelines, microservices, and low traffic websites.\n AWS Free Tier. T8i instances are available in US East (N. Virginia, Ohio), US West (Oregon, N. California), Europe (Frankfurt, Ireland, London, Paris), Asia Pacific (Hyderabad, Malaysia, Mumbai, Seoul, Singapore, Sydney, Tokyo), Canada (Central) and South America (São Paulo) regions. T8i instances are available to purchase via On-Demand instances, and Spot instances with Savings Plan option coming soon. To learn more, visit the Amazon EC2 T8i instances page.

Amazon DynamoDB Streams now supports timestamp-based stream processing

Amazon DynamoDB Streams now supports timestamp-based stream processing, enabling you to begin reading stream records from a specific point in time. Previously, you could only start from the oldest retained record (up to 24 hours old) or from new records written after you start consuming the stream. Now, you can supply a timestamp and begin processing at the first record written at or after that time using the AT_TIMESTAMP shard iterator type.\nTimestamp-based stream processing can be used with the Amazon Kinesis Client Library (KCL) through the DynamoDB Streams Kinesis adapter. It helps you avoid reprocessing entire retained streams or missing records when resuming stream consumption, supporting common use cases such as recovery after an interruption, data replay for debugging and auditing by reading from a specific point in the past, skipping a backlog by resuming from a recent timestamp near the latest records, and synchronizing after a restore by beginning at the export timestamp following a point-in-time recovery (PITR) export. Timestamp-based stream processing is available in all commercial AWS Regions, the AWS GovCloud (US) Regions, and the China Regions. There is no additional charge for this feature beyond standard GetRecords API request charges. To get started, see Amazon DynamoDB Streams timestamp documentation in the DynamoDB developer guide.

AWS Lambda Durable Execution SDK for Go is now generally available

Today, AWS announces the general availability of the AWS Lambda Durable Execution SDK for Go, enabling Go developers to build resilient, long-running workflows using Lambda durable functions. With this SDK, developers can create multi-step applications like infrastructure automation, deployment rollouts with approval gates, and pipelines that call AI models and pause for human review directly in their Go code without implementing custom progress tracking.\nLambda durable functions extend Lambda’s event-driven programming model with operations that checkpoint progress automatically and pause execution for up to an year when waiting on external events. The AWS Lambda Durable Execution SDK for Go provides a native Go experience for building with Lambda durable functions. It includes steps for progress tracking, callback integration for human and agent-in-the-loop workflows, durable invocation for reliable function chaining, and waits for efficient suspension. The SDK works with the Go tools developers already use. Developers add it to a Go module with go get and deploy their function as a single static binary on the provided.al2023 runtime, like any other Go function on Lambda. Before deploying, they can locally test complete workflows on their own machine with the durabletest package, without requiring network access or AWS credentials, and use the durablelint analyzer to catch code that could break a workflow when it resumes. To get started, see the Lambda durable functions developer guide and the AWS Lambda Durable Execution SDK for Go on GitHub. For Regional availability and pricing details for Lambda durable functions, see the AWS Capabilities by Region page and AWS Lambda Pricing.

Muse Spark Open model now available on Amazon SageMaker JumpStart

Meta’s Muse Spark Open is now available on Amazon SageMaker JumpStart, expanding the portfolio of foundation models available to AWS customers. This coding-optimized open-weight model from Meta is purpose-built for agentic coding workflows, bringing multi-step reasoning and tool calling to enterprise software engineering on AWS infrastructure.\nMuse Spark Open is designed for long-running coding agents and multimodal engineering workflows. Built on a Mixture-of-Experts (MoE) architecture with 816B total parameters and 42B active per token, it delivers the capacity of a very large network while keeping per-token compute closer to that of a much smaller dense model. The model holds a 1M-token context window, enabling it to ingest an entire project—including dependencies, legacy code, and thousands of files—without splitting tasks into smaller pieces. It accepts text, images, documents, audio, and audio-video as input and generates text output. Trained on real developer workflows such as multi-file refactors and long debugging sessions, it plans, acts, observes results, and self-corrects over long horizons rather than making single tool calls. SageMaker JumpStart offers two variants: BF16 (Muse-Spark-Open-816B-A42B) and NVFP4 quantized (Muse-Spark-Open-816B-A42B-NVFP4), with the NVFP4 variant requiring roughly a quarter of the memory for cost-efficient deployment. With SageMaker JumpStart, customers can deploy Muse Spark Open with just a few clicks, with the endpoint running in their own AWS account for full control over source code and prompts. To get started with this model, navigate to the SageMaker JumpStart model catalog in the SageMaker console or use the SageMaker Python SDK to deploy the model to your AWS account. For more information about deploying and using foundation models in SageMaker JumpStart, see the Amazon SageMaker JumpStart documentation.

AWS HealthOmics now supports shared VPCs

AWS HealthOmics now supports sharing VPCs across accounts, enabling your workflows to access resources in Amazon Virtual Private Clouds (VPCs) managed by other AWS accounts with AWS Resource Access Manager (RAM). AWS HealthOmics is a HIPAA-eligible service that helps healthcare and life sciences customers accelerate scientific breakthroughs at scale with fully managed bioinformatics workflows.\nWith shared VPCs, customers can centralize networking administration by sharing subnets and security groups from a single VPC across multiple accounts in their organization, and HealthOmics then places each account’s workflows directly into those shared subnets. With cross-account VPC sharing, multiple accounts run inside one centrally managed VPC, instead of duplicating endpoints across VPCs. Workflows in participating accounts can access data privately without separate VPC peering connections or duplicated VPC endpoints. Previously, customers with centralized networking had to create separate AWS PrivateLink VPC endpoints in each VPC and maintain individual route tables using VPC peering. Cross-account VPC sharing reduces infrastructure overhead and simplifies network management for organizations with centralized networking while allowing researchers and developers to run bioinformatics workflows in their own accounts. Shared VPCs is available in all Regions where AWS HealthOmics is available: US East (N. Virginia, Ohio), US West (Oregon), Europe (Frankfurt, Ireland, London), Israel (Tel Aviv), and Asia Pacific (Singapore, Seoul, Tokyo). To get started, see the AWS HealthOmics User Guide.

Amazon Time Sync Service adds support for microsecond-accurate time on 135 additional EC2 instance types in all commercial regions

Amazon Time Sync Service introduces support for microsecond-accurate time on 135 additional EC2 instance types—spanning 12 instance families—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.\nThe newly supported families include eight Gen8 families (C8a, C8i, C8i-flex, C8id, M8i, M8i-flex, R8a, and X8i) and four Gen9 families (C9g, C9gd, M9g, and M9gd). 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.

AWS Transform for full-stack Windows modernization now supports Aurora PostgreSQL cross-region deployment in 7 more Regions

Today, AWS Transform for full-stack Windows modernization adds support for cross-region deployment of converted Amazon Aurora PostgreSQL databases and is available in seven additional AWS Regions. Together these changes give customers more control over where their modernized databases are deployed after SQL Server modernization, supporting data residency, latency, and disaster recovery requirements.\nCustomers can upload SQL Server Database Design Language (DDL) files and set a target AWS Region in the transformation plan to convert the database and deploy the resulting Aurora PostgreSQL cluster to a different AWS Region. The same workflow makes dependent .NET applications PostgreSQL compatible, updating connection strings and ADO.NET and Entity Framework data-access calls. With this expansion, AWS Transform for full-stack Windows modernization is now available in the following Regions: US East (N. Virginia), Europe (Frankfurt), Europe (London), Asia Pacific (Mumbai), Asia Pacific (Seoul), Asia Pacific (Tokyo), Asia Pacific (Sydney), and Canada (Montreal). To get started, you can go to AWS Transform product page or see AWS Transform for full-stack Windows documentation.

Amazon MSK Connect and AWS Glue Schema Registry are now available in the AWS European Sovereign Cloud (Germany) region

Starting today, Amazon MSK Connect and the AWS Glue Schema Registry are available in the AWS European Sovereign Cloud (Germany) region. Together with Amazon MSK Standard brokers, already available in the region, these launches give customers running streaming workloads on the AWS European Sovereign Cloud a more complete set of tools to build, connect, and govern their Apache Kafka applications.\nMSK Connect lets you run fully managed Kafka Connect clusters with Amazon MSK - deploy, monitor, and scale connectors that move data between Apache Kafka and external systems like databases, file systems, and search indices, with no infrastructure to provision, automatic scaling, and pay-as-you-go pricing. Complementing it, the AWS Glue Schema Registry - a serverless, free feature of AWS Glue - lets you validate and control the evolution of streaming data using Apache Avro, JSON, and Protobuf schemas. Acting as a centralized repository for data format and structure across decoupled applications, it reduces data validation logic, cross-team coordination, and downstream failures, and integrates with C# and Java applications built for Amazon MSK, Amazon Kinesis Data Streams, Amazon Managed Service for Apache Flink, and AWS Lambda. To get started, visit the Amazon MSK Developer Guide and the AWS Glue Schema Registry documentation.

Amazon CloudFront announces dual-stack support for VPC origins

Amazon CloudFront now supports end-to-end IPv6 delivery to origins inside your Amazon Virtual Private Cloud (Amazon VPC), with the new dual-stack VPC origins. With VPC origins, customers keep their Application Load Balancers (ALB), Network Load Balancers (NLB), and EC2 instances in private subnets that only their CloudFront distributions can reach. With dual-stack support, customers can now send IPv6 traffic all the way to these private origins to meet their IPv6 architecture and regulatory requirements, without exposing origins to the internet.\nPreviously, CloudFront supported dual-stack for public origins but connected to VPC origins over IPv4 only. Customers faced a tradeoff: move origins to public subnets for IPv6 and give up VPC origin security or keep them private and stay on IPv4. Now, with this launch, customers can configure their VPC origins as dual-stack and get end-to-end IPv6 without sacrificing security. Dual-stack VPC origins are available in all AWS Commercial Regions where VPC origins are supported. The full list of supported Regions is available here. There is no additional cost for using dual-stack VPC origins. To learn more, visit CloudFront VPC origins.

PostgreSQL 19 RC1 is now available in Amazon RDS Database Preview Environment

Starting today, Amazon RDS for PostgreSQL 19 Release Candidate 1 (RC1) is available in the Amazon RDS Database Preview Environment, allowing you to evaluate the pre-release of PostgreSQL 19 on Amazon RDS for PostgreSQL.\nPostgreSQL 19 RC1 focuses on stability, with bug fixes and improvements from the Beta 4 testing period. This release improves error handling for complex text-search expressions, hardens parallel autovacuum, and improves reliability of GIN index maintenance. Refer to the PostgreSQL community announcement for more details.. Refer to the PostgreSQL community announcement for more details. Amazon RDS Database Preview Environment database instances are retained for a maximum period of 60 days and are automatically deleted after the retention period. Amazon RDS database snapshots that are created in the Preview Environment can only be used to create or restore database instances within the Preview Environment. You can use the PostgreSQL dump and load functionality to import or export your databases from the Preview Environment. Amazon RDS Database Preview Environment database instances are priced as per the pricing in the US East (Ohio) Region.

Amazon Redshift long-term system table retention with Amazon S3 Tables is now available in additional AWS Regions

Amazon Redshift long-term retention for system table data through native integration with Amazon S3 Tables is now available in additional AWS Regions. With this feature, you can configure your Redshift system table data retention beyond the current 7-day limit to meet your compliance, auditing, and observability requirements. Once enabled, AWS automatically writes system table data to S3 Tables in Apache Iceberg format and manages partitioning, compaction, and retention.\nCustomers use Redshift system tables to monitor query performance, audit data warehouse activity, and meet compliance requirements. With this feature, system table data is replicated automatically, eliminating the need for custom extract transform load (ETL) pipelines and any resource contention with your production workloads. If you operate multiple data warehouses, you can consolidate their system table data into a single location for cross-warehouse observability and analysis. Because the data is stored in the open Apache Iceberg format, you can query it through Redshift, Amazon Athena, DuckDB, or any Iceberg-compatible engine, and build observability dashboards using AWS services or third-party tools without additional operations overhead. Additionally, the AWS Agent Toolkit provides skills for querying system table data to surface performance insights and optimization recommendations. This feature is available for Amazon Redshift Provisioned RG and RA3 instances and Amazon Redshift Serverless in the following additional AWS Regions: Asia Pacific (New Zealand), Canada West (Calgary), AWS GovCloud (US-East), and AWS GovCloud (US-West). To learn more, visit our documentation or the blog.

Amazon S3 Metadata now available in six additional AWS Regions

Amazon S3 Metadata is now available in six additional AWS Regions: Asia Pacific (Hyderabad), Asia Pacific (Malaysia), Asia Pacific (New Zealand), Asia Pacific (Taipei), Asia Pacific (Thailand), and Mexico (Central).\nS3 Metadata is a fast and easy way to instantly discover and understand your S3 data with automated, easily queried metadata that updates in near real time. This helps you curate, identify, and use your S3 data for business analytics, real-time inference applications, and more. S3 Metadata automatically populates metadata for both new and existing objects, providing you with a comprehensive, queryable view of your data. Annotations extend S3 Metadata, letting you attach rich business context directly to your objects in JSON, XML, or YAML, with up to 1 GB per object. To query annotations at scale, you can surface them in S3 Metadata annotation tables in these Regions. With this launch, S3 Metadata is now generally available in 38 AWS Regions. For pricing details, visit the S3 pricing page. To learn more, visit the product page and documentation.

AWS Security Hub CSPM now supports the Cyber Risk Institute profile version 2.2

AWS Security Hub CSPM now supports automated security checks that align with the Cyber Risk Institute (CRI) profile. The CRI Profile is a cybersecurity framework developed by the Cyber Risk Institute, a non-profit coalition of financial institutions and trade associations. The framework harmonizes cybersecurity and regulatory expectations that apply to the financial services sector into a common set of requirements, so that an organization can assess itself once and report against multiple regulations. AWS Security Hub CSPM provides 404 controls that support a subset of CRI Profile v2.2 requirements; the controls don’t support requirements that require manual checks.\nThe new standard is now available in all AWS Regions where Security Hub CSPM is currently available, including the AWS GovCloud (US) and the China Regions. To quickly enable the standard across your AWS environment, we recommend that you use Security Hub central configuration. With this approach, you can enable the standard in all or only some of your organization’s accounts and across all AWS Regions that are linked to Security Hub with a single action. To learn more, see CRI Profile v2.2 in the AWS Security Hub CSPM user guide. To receive notifications about new Security Hub CSPM features and controls subscribe to the Security Hub SNS topic. You can also try Security Hub CSPM at no cost for 30 days with the AWS Free Tier offering.

Amazon Managed Service for Prometheus Collector now available in Asia Pacific (New Zealand) Region

Amazon Managed Service for Prometheus collector, a fully-managed agentless collector for Prometheus metrics, is now available in Asia Pacific (New Zealand). Customers in New Zealand can now run their Prometheus-compatible monitoring workloads in-region, with lower latency for local workloads and the ability to keep their data in-country.\nAmazon Managed Service for Prometheus collector discovers and collects Prometheus metrics from your container workloads without the need to run and operate collection agents yourself. Customers ingest the collected metrics into an Amazon Managed Service for Prometheus workspace, where they query and analyze the metrics. This fully-managed approach simplifies operational overhead by eliminating the need to maintain and scale collection infrastructure. To learn more about Amazon Managed Service for Prometheus collector, visit the user guide or product page.

Amazon Managed Workflows for Apache Airflow now offers AI agent skills

Amazon Managed Workflows for Apache Airflow (MWAA) now integrates with the Agent Toolkit for AWS to provide built-in AI agent skills that let you author, debug, and test MWAA workflows and upgrade MWAA environments using any AI coding assistant that supports agent skills.\nThe four skills are authoring-mwaa-workflow, debugging-mwaa-workflow, testing-mwaa-workflow, and upgrading-mwaa-environments. Together they let your AI assistant interact directly with MWAA using natural language, reducing the manual effort of building and maintaining data pipelines. For example, you can describe an ETL pipeline in plain language and the authoring skill generates a working DAG complete with task dependencies and error handling. The Amazon MWAA AI agent skills work with both Amazon MWAA Provisioned and Amazon MWAA Serverless environments, and are available in all AWS Regions where Amazon MWAA is available. To get started, install the aws-data-analytics plugin and configure your AI coding assistant to use the MWAA agent skills. To learn more, see Using agent skills with Amazon MWAA in the Amazon MWAA User Guide and the Agent Toolkit for AWS documentation.

Launch AWS DevOps Agent to review findings and test results from AWS Resilience Hub

The next generation of AWS Resilience Hub now integrates with AWS DevOps agent allowing you to review failure mode assessment findings and implement mitigations to resolve findings and review resilience test results. AWS Resilience Hub is a central location in the AWS Management Console that helps platform engineering and site reliability teams assess and strengthen the resilience of their workloads running on AWS. It provides automated dependency discovery, generative AI-powered failure mode analysis, modular resilience policies, resilience testing, and organization-wide resilience posture reporting.\nResilience test investigations: When you run a resilience test, Resilience Hub now launches an AWS DevOps Agent investigation that observes the test and returns a structured analysis. You can uncover symptoms, root cause, and impact along with observed recovery actions, like ARC Region switch. Interactive agent analysis from failure mode assessment findings: You can now start AWS DevOps Agent on demand task directly from a failure mode assessment finding in the Resilience Hub console, with the finding’s context already in place. This allows for asking questions and investigating findings to create a mitigation plan. To get started, visit the AWS Resilience Hub console, select a service and configure DevOps agent in the service configuration. Then launch the DevOps agent directly from a failure mode assessment finding or a resilience test. To learn more, see the product page or visit the documentation. AWS Resilience Hub is available in the following AWS Regions: US East (N. Virginia), US East (Ohio), US West (Oregon), Canada (Central), Europe (Ireland), Europe (London), Europe (Frankfurt), Europe (Paris), Europe (Stockholm), Asia Pacific (Mumbai), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), Asia Pacific (Seoul), and South America (São Paulo).

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