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</p> <p>With this launch, if you have enabled recording for all resource types, then AWS Config will automatically track these new additions. The newly supported resource types are also available in Config rules and Config aggregators. </p> <p>You can now use AWS Config to monitor the following newly supported resource types in all <a href="https://docs. aws. amazon. com/config/latest/developerguide/what-is-resource-config-coverage.

com/build/capabilities/explore">AWS Capabilities by Region</a> in AWS Builder Center. You can now set availability notifications for AWS service, or to an individual feature of an AWS service, in the AWS Regions you are interested in, and receive an in-app notification and a weekly email digest covering all the capabilities that became available since you enabled availability notifications. Setting availability notifications for a service covers every feature within it, and an 'All regions' option covers every Region, including AWS Regions that launch in the future. </p> <p>In addition to the feature level availability notifications , you can now filter API operations and CloudFormation resources by availability status and compare Regions to show all capabilities, only matching ones, or only differences. With over 19,000 API operations and more than 5,000 CloudFormation resource types, these filters quickly narrow a full listing down to the exact differences between Regions, working alongside search and existing display preferences.

5 family, built for subagents and high-volume, cost-sensitive work. According to Anthropic, it costs around 75% less than Claude Haiku 4. 5 for most tasks. </p> <p>Claude Haiku 5. 5 is Anthropic's most capable Haiku yet, a significant step up from Haiku 4. 5 across coding, tool use, computer use, and agents. It's also the first Haiku with effort controls, so teams can tune cost against intelligence for each task. Haiku 5. 5 fits work where speed and cost matter most: real-time experiences like voice agents, live support, and in-app assistants, and high-volume tasks like classification, summarization, and pulling key fields from documents. It's also a fast, cost-efficient subagent. A more capable model like Claude Opus 5. 5 can plan the work and hand off well-defined coding, tool use, and browser automation tasks to Haiku, making it practical to run many agents in parallel.

5 family, built for subagents and high-volume, cost-sensitive work. According to Anthropic, it costs around 75% less than Claude Haiku 4. 5 for most tasks. </p> <p>Claude Haiku 5. 5 is Anthropic's most capable Haiku yet, a significant step up from Haiku 4. 5 across coding, tool use, computer use, and agents. It's also the first Haiku with effort controls, so teams can tune cost against intelligence for each task. Haiku 5. 5 fits work where speed and cost matter most: real-time experiences like voice agents, live support, and in-app assistants, and high-volume tasks like classification, summarization, and pulling key fields from documents. It's also a fast, cost-efficient subagent. A more capable model like Claude Opus 5. 5 can plan the work and hand off well-defined coding, tool use, and browser automation tasks to Haiku, making it practical to run many agents in parallel. </p> <p>Customers have two ways to access Claude Haiku 5. 5: Amazon Bedrock and Claude Platform on AWS.

</p> <p>AWS Batch automatically publishes job state transition and duration metrics to the AWS/Batch namespace in Amazon CloudWatch, with JobQueueName dimension. State transition metrics track how many jobs entered a given state, such as submitted, running, succeeded, or failed. Duration metrics track how long jobs spent between states, such as time from submission to runnable or total execution time. You can view these metrics in the AWS Batch console, the Amazon CloudWatch console, or through the AWS CLI and AWS SDKs. </p> <p>AWS Batch CloudWatch metrics are now published in all <a href="https://aws. amazon. com/about-aws/global-infrastructure/regional-product-services/">AWS Regions</a> where AWS Batch is available. For more information, see CloudWatch Metrics page in the <a href="https://docs. aws. amazon. com/batch/latest/userguide/using_cloudwatch_metrics. html">AWS Batch User Guide</a>.

</p> <p>If you're already using ACME with ACM, setup requires no changes to your ACME clients. After you create your managed ACME endpoint in ACM, you create a standard VPC interface endpoint using the VPC console, AWS CLI, or AWS CloudFormation. Private DNS resolves your existing ACME directory URL to the interface endpoint inside your VPC automatically, so the same client configuration and directory URL continue to work with no reconfiguration. Issuance operations—account creation, order creation, domain validation, finalization, and certificate retrieval—then flow over PrivateLink. All activity remains visible in the ACM console with AWS CloudTrail logging and Amazon CloudWatch metrics for auditability. </p> <p>AWS PrivateLink support for ACME certificate issuance is available in all commercial AWS Regions.

Materialized views are created using familiar SQL — CREATE MATERIALIZED VIEW ... USING ICEBERG and the results are instantly queryable by any Iceberg-compatible engine, including Amazon Athena, Apache Spark on Amazon EMR and AWS Glue, and third-party engines such as Trino, or Snowflake. Redshift keeps them current by recomputing only what has changed with manual incremental refresh, and because the results are Iceberg tables in the Glue Data Catalog, they are governed and discovered like any other catalog table. </p> <p>Data teams often build analytics in stages, stitching together different engines to clean and transform raw data before serving it. This adds pipeline orchestration overhead and can introduce semantic differences between engines. Iceberg materialized views deliver value in two ways.

ai, giving you a powerful new option for agentic coding and long-horizon software engineering work. Customers using GLM 5. 3 on Amazon Bedrock have access to the latest open weight model with the AWS security and compliance posture. </p> <p>GLM 5. 3 is Z. ai’s flagship model, a mixture-of-experts architecture with 753B total parameters and roughly 40B active per token, built on the same base model as GLM 5. 2 with all improvements driven by scaled post-training. It combines a 1-million-token context window with up to 128K output tokens, and reasoning is always enabled with selectable effort levels so you can trade latency and token consumption against task performance. </p> <p>Bedrock support for GLM 5. 3 includes explicit prompt caching with cache points on system prompts and messages, helping you reduce latency and input costs when reusing context across model calls. </p> <p>GLM 5. 3 is available to eligible enterprise customers, and is accessible through the US and Global cross-Region inference profiles. To get started with GLM 5. 3, you can access the model in the<a href="https://us-east-1. console. aws. amazon. com/bedrock/home?

You can restrict access to the Identity Store API and the SCIM API based on the network that requests originate from. Your custom applications and user provisioning workflows use the Identity Store API to manage and look up users and groups, and your external identity provider uses the SCIM API to synchronize users and groups. </p> <p>For the Identity Store API, you can require that requests arrive only through allowed VPC endpoints in your account or organization, or from specific source VPCs. For both APIs, you can allow requests only from specific IP ranges. Within the same configuration, you can apply different restrictions to each API. For example, you can require that Identity Store API requests arrive only through VPC endpoints, while allowing SCIM requests from your external identity provider's published IP ranges. </p> <p>Network access controls are optional and turned off by default.

With a maximum frequency of 4. 5GHz, Hpc8a instances deliver up to 40% higher performance and up to 25% better price performance compared to Hpc7a instances, helping customers accelerate compute-intensive workloads while optimizing costs. Compared to Hpc7a instances, Hpc8a instances also provide up to 42% higher memory bandwidth, further improving performance for memory-intensive simulations and scientific computing workloads. <br> <br> Built on the latest sixth-generation <a href="https://aws. amazon. com/ec2/nitro/">AWS Nitro Cards</a>, Hpc8a instances are designed for compute-intensive, latency-sensitive HPC workloads. They are ideal for tightly coupled applications such as computational fluid dynamics (CFD), weather forecasting, explicit finite element analysis (FEA), and multiphysics simulations that require fast inter-node communication and consistent high performance. <br> <br> To get started, sign in to the <a href="https://console. aws. amazon. com/ec2/?