AWS Lambda response streaming is now available in all commercial AWS Regions, bringing full regional parity for this capability. Customers in newly supported Regions can use the InvokeWithResponseStream API to progressively stream response payloads back to clients as data becomes available.

Response streaming enables functions to send partial responses to clients incrementally rather than buffering the entire response before transmission. This reduces time-to-first-byte (TTFB) latency and is well suited for latency-sensitive workloads such as LLM-based applications as well as web and mobile applications where users benefit from seeing responses appear incrementally. Response streaming supports payloads up to a default maximum of 200 MB.

With this expansion, customers in all commercial Regions can stream responses using the InvokeWithResponseStream API through a supported AWS SDK, or through Amazon API Gateway REST APIs with response streaming enabled. Response streaming supports Node.js managed runtimes as well as custom runtimes.

Streaming responses incur an additional cost for network transfer of the response payload. You are billed based on the number of bytes generated and streamed out of your Lambda function over the first 6 MB. To get started with Lambda response streaming, visit the AWS Lambda documentation. 

 

​AWS Lambda response streaming is now available in all commercial AWS Regions, bringing full regional parity for this capability. Customers in newly supported Regions can use the InvokeWithResponseStream API to progressively stream response payloads back to clients as data becomes available.
Response streaming enables functions to send partial responses to clients incrementally rather than buffering the entire response before transmission. This reduces time-to-first-byte (TTFB) latency and is well suited for latency-sensitive workloads such as LLM-based applications as well as web and mobile applications where users benefit from seeing responses appear incrementally. Response streaming supports payloads up to a default maximum of 200 MB.
With this expansion, customers in all commercial Regions can stream responses using the InvokeWithResponseStream API through a supported AWS SDK, or through Amazon API Gateway REST APIs with response streaming enabled. Response streaming supports Node.js managed runtimes as well as custom runtimes.
Streaming responses incur an additional cost for network transfer of the response payload. You are billed based on the number of bytes generated and streamed out of your Lambda function over the first 6 MB. To get started with Lambda response streaming, visit the AWS Lambda documentation.   

Starting today, Amazon Lightsail is available in the Asia Pacific (Malaysia) Region. This expansion brings the power and simplicity of Lightsail to customers in Malaysia and surrounding regions.

With this launch, customers in Malaysia and nearby countries can now enjoy lower latency and better performance for their applications while meeting local data residency requirements. The new Region provides access to Lightsail’s full range of features including instances that meet your compute needs—from general purpose to compute-optimized and memory-optimized bundles—as well as managed databases, containers, load balancers and more, all with the same simple, predictable pricing that Lightsail customers love.

Lightsail is available in these AWS Regions: US East (Ohio, N. Virginia), US West (Oregon), Canada (Central), Europe (Frankfurt, Ireland, London, Paris, Stockholm), Asia Pacific (Malaysia, Jakarta, Mumbai, Seoul, Singapore, Sydney, Tokyo). To learn more about Regions and Availability Zones for Lightsail, please refer to the documentation.

You can use this Region through the Lightsail Console, AWS Command Line Interface (CLI) and AWS SDKs.

 

​Starting today, Amazon Lightsail is available in the Asia Pacific (Malaysia) Region. This expansion brings the power and simplicity of Lightsail to customers in Malaysia and surrounding regions. With this launch, customers in Malaysia and nearby countries can now enjoy lower latency and better performance for their applications while meeting local data residency requirements. The new Region provides access to Lightsail’s full range of features including instances that meet your compute needs—from general purpose to compute-optimized and memory-optimized bundles—as well as managed databases, containers, load balancers and more, all with the same simple, predictable pricing that Lightsail customers love. Lightsail is available in these AWS Regions: US East (Ohio, N. Virginia), US West (Oregon), Canada (Central), Europe (Frankfurt, Ireland, London, Paris, Stockholm), Asia Pacific (Malaysia, Jakarta, Mumbai, Seoul, Singapore, Sydney, Tokyo). To learn more about Regions and Availability Zones for Lightsail, please refer to the documentation. You can use this Region through the Lightsail Console, AWS Command Line Interface (CLI) and AWS SDKs.  

AWS Cost Explorer now brings Amazon Q Developer’s generative AI capabilities directly into your cost analysis workflows. You can now use natural language queries to ask Amazon Q questions about your AWS cost and usage data. In addition to providing answers to your question, you now also receive automatically updated visualizations in Cost Explorer. This enables faster cost analysis, reduces time to insights, and makes cost visibility accessible to every team member.

With this launch, you can start your cost analysis with the new suggested prompts in Cost Explorer. These prompts include commonly asked cost questions like «Show me my top spending services for this month.» Amazon Q provides detailed insights while Cost Explorer simultaneously updates with the corresponding visualization, filters, and groupings. You can also ask custom questions in your own words using the new ‘Ask Question’ button, exploring your spending patterns conversationally. Cost Explorer automatically updates charts and tables when analysis is based on your cost and usage data. When Amazon Q compiles insights from additional datasets such as pricing or anomaly detection, visualizations are displayed in Amazon Q’s new artifacts panel. You can continue the conversation with follow-up questions while maintaining full context, allowing you to go from a quick cost check to a deep investigation without switching tools or breaking your workflow.

Natural language cost analysis for AWS Cost Explorer is available today in all commercial AWS Regions at no additional charge. To learn more, visit AWS Cost Explorer. To get started, see the user guide.

 

​AWS Cost Explorer now brings Amazon Q Developer’s generative AI capabilities directly into your cost analysis workflows. You can now use natural language queries to ask Amazon Q questions about your AWS cost and usage data. In addition to providing answers to your question, you now also receive automatically updated visualizations in Cost Explorer. This enables faster cost analysis, reduces time to insights, and makes cost visibility accessible to every team member.
With this launch, you can start your cost analysis with the new suggested prompts in Cost Explorer. These prompts include commonly asked cost questions like «Show me my top spending services for this month.» Amazon Q provides detailed insights while Cost Explorer simultaneously updates with the corresponding visualization, filters, and groupings. You can also ask custom questions in your own words using the new ‘Ask Question’ button, exploring your spending patterns conversationally. Cost Explorer automatically updates charts and tables when analysis is based on your cost and usage data. When Amazon Q compiles insights from additional datasets such as pricing or anomaly detection, visualizations are displayed in Amazon Q’s new artifacts panel. You can continue the conversation with follow-up questions while maintaining full context, allowing you to go from a quick cost check to a deep investigation without switching tools or breaking your workflow.
Natural language cost analysis for AWS Cost Explorer is available today in all commercial AWS Regions at no additional charge. To learn more, visit AWS Cost Explorer. To get started, see the user guide.  

Amazon SageMaker Unified Studio notebooks now support import/export capabilities, enabling migration from JupyterLab and other notebook platforms. This release also introduces developer acceleration features including cell reordering, keyboard shortcuts, cell renaming, and multi-line SQL support, designed to enhance productivity for data engineers and data scientists professionals working with notebook-based workflows.

The new import/export functionality supports .ipynb, .json, and .py formats while preserving cell types and metadata, making platform migration straightforward. You can export notebooks in four formats including Jupyter notebook with requirements (.zip), standard .ipynb, Python scripts (.py), and SageMaker Unified Studio native format (.json). Developer acceleration features enable you to reorder cells without copy-paste duplication, assign custom names to cells for improved navigation in large notebooks, use familiar keyboard shortcuts for faster development, and execute multiple SQL statements in a single cell with results displayed in separate tabs for easy comparison and analysis.

This feature is available in all AWS Regions where Amazon SageMaker Unified Studio is available. To learn more, visit the Amazon SageMaker Unified Studio marketing page and user guide. 

 

​Amazon SageMaker Unified Studio notebooks now support import/export capabilities, enabling migration from JupyterLab and other notebook platforms. This release also introduces developer acceleration features including cell reordering, keyboard shortcuts, cell renaming, and multi-line SQL support, designed to enhance productivity for data engineers and data scientists professionals working with notebook-based workflows.
The new import/export functionality supports .ipynb, .json, and .py formats while preserving cell types and metadata, making platform migration straightforward. You can export notebooks in four formats including Jupyter notebook with requirements (.zip), standard .ipynb, Python scripts (.py), and SageMaker Unified Studio native format (.json). Developer acceleration features enable you to reorder cells without copy-paste duplication, assign custom names to cells for improved navigation in large notebooks, use familiar keyboard shortcuts for faster development, and execute multiple SQL statements in a single cell with results displayed in separate tabs for easy comparison and analysis.
This feature is available in all AWS Regions where Amazon SageMaker Unified Studio is available. To learn more, visit the Amazon SageMaker Unified Studio marketing page and user guide.   

We’re excited to announce the launch of a new Greengrass component SDK for AWS IoT Greengrass applications. This new SDK addresses the challenge of deploying sophisticated applications on edge devices with limited resources, enabling industries such as automotive, industrial IoT, robotics, and smart buildings to run more complex AI and ML workloads at the edge. Moreover, the new SDK maintains full compatibility with both AWS IoT Greengrass nucleus and nucleus lite capabilities.

The new Greengrass component SDK offers significant memory footprint reduction, with a footprint of less than 0.5MB compared to 30MB, enabling deployment on resource-constrained devices. It provides native C, C++, and Rust bindings, optimized for performance and cost-critical embedded applications. This SDK opens new possibilities for edge computing applications where memory constraints have previously been a limiting factor.

The new Greengrass component SDK is available in all AWS Regions where AWS IoT Greengrass is available.

 

​We’re excited to announce the launch of a new Greengrass component SDK for AWS IoT Greengrass applications. This new SDK addresses the challenge of deploying sophisticated applications on edge devices with limited resources, enabling industries such as automotive, industrial IoT, robotics, and smart buildings to run more complex AI and ML workloads at the edge. Moreover, the new SDK maintains full compatibility with both AWS IoT Greengrass nucleus and nucleus lite capabilities. The new Greengrass component SDK offers significant memory footprint reduction, with a footprint of less than 0.5MB compared to 30MB, enabling deployment on resource-constrained devices. It provides native C, C++, and Rust bindings, optimized for performance and cost-critical embedded applications. This SDK opens new possibilities for edge computing applications where memory constraints have previously been a limiting factor.
The new Greengrass component SDK is available in all AWS Regions where AWS IoT Greengrass is available.  

As announced on November 19, 2025, Amazon S3 is now deploying a new default bucket security setting which will automatically disable server-side encryption with customer-provided keys (SSE-C) for all new general purpose buckets. For existing buckets in AWS accounts with no SSE-C encrypted objects, S3 will also disable SSE-C for all new write requests. For AWS accounts with SSE-C usage, S3 will not change the bucket encryption configuration on any of the existing buckets in those accounts. To learn more about this change, visit the S3 User Guide.

Amazon S3 will deploy this new default to both new and existing general purpose buckets in 37 AWS Regions including the AWS China and AWS GovCloud (US) Regions over the next few weeks.

 

​As announced on November 19, 2025, Amazon S3 is now deploying a new default bucket security setting which will automatically disable server-side encryption with customer-provided keys (SSE-C) for all new general purpose buckets. For existing buckets in AWS accounts with no SSE-C encrypted objects, S3 will also disable SSE-C for all new write requests. For AWS accounts with SSE-C usage, S3 will not change the bucket encryption configuration on any of the existing buckets in those accounts. To learn more about this change, visit the S3 User Guide. Amazon S3 will deploy this new default to both new and existing general purpose buckets in 37 AWS Regions including the AWS China and AWS GovCloud (US) Regions over the next few weeks.  

Amazon Relational Database Service (Amazon RDS) for Oracle now supports Oracle Management Agent (OMA) version 24.1.0.0.v1 for Oracle Enterprise Manager (OEM) Cloud Control 24ai Release 1. OEM 24ai offers web-based tools to monitor and manage your Oracle databases. Amazon RDS for Oracle installs OMA, which communicates with your Oracle Management Service (OMS) to provide monitoring information. Customers running OMS version 24.1 Release 1 or later can now manage databases by installing OMA 24.1.0.0.v1

To enable the version 24.1.0.0.v1 of OMA for OEM 24aiR1 or later, navigate to «Option Groups» in the AWS Management Console and add the «OEM_AGENT» option to a new or existing option group and set AGENT_VERSION to “24.1.0.0.v1”. You will also need to configure option settings including OMS hostname (or IP), port, agent registration password, and minimum TLS version of TLSv1.2 to allow OMA on your Amazon RDS for Oracle database instances to communicate with your existing Oracle Management Service (OMS) stack. To learn more, please refer to Amazon RDS for Oracle documentation.

For more information on enabling and configuring OEM agents, refer to the Amazon RDS for Oracle documentation.

 

​Amazon Relational Database Service (Amazon RDS) for Oracle now supports Oracle Management Agent (OMA) version 24.1.0.0.v1 for Oracle Enterprise Manager (OEM) Cloud Control 24ai Release 1. OEM 24ai offers web-based tools to monitor and manage your Oracle databases. Amazon RDS for Oracle installs OMA, which communicates with your Oracle Management Service (OMS) to provide monitoring information. Customers running OMS version 24.1 Release 1 or later can now manage databases by installing OMA 24.1.0.0.v1 To enable the version 24.1.0.0.v1 of OMA for OEM 24aiR1 or later, navigate to «Option Groups» in the AWS Management Console and add the «OEM_AGENT» option to a new or existing option group and set AGENT_VERSION to “24.1.0.0.v1”. You will also need to configure option settings including OMS hostname (or IP), port, agent registration password, and minimum TLS version of TLSv1.2 to allow OMA on your Amazon RDS for Oracle database instances to communicate with your existing Oracle Management Service (OMS) stack. To learn more, please refer to Amazon RDS for Oracle documentation. For more information on enabling and configuring OEM agents, refer to the Amazon RDS for Oracle documentation.  

Customers can now create Amazon FSx for OpenZFS file systems in the AWS Asia Pacific (Melbourne) Region, providing fully managed shared file storage built on the OpenZFS file system.

Amazon FSx makes it easier and more cost effective to launch, run, and scale feature-rich, high-performance file systems in the cloud. It supports a wide range of workloads with its reliability, security, scalability, and broad set of capabilities. Amazon FSx for OpenZFS provides fully managed, cost-effective, shared file storage powered by the popular OpenZFS file system, and is designed to deliver sub-millisecond latencies and multi-GB/s throughput along with rich ZFS-powered data management capabilities (like snapshots, data cloning, and compression).

To learn more about Amazon FSx for OpenZFS, visit our product page, and see the AWS Region Table for complete regional availability information.

 

​Customers can now create Amazon FSx for OpenZFS file systems in the AWS Asia Pacific (Melbourne) Region, providing fully managed shared file storage built on the OpenZFS file system. Amazon FSx makes it easier and more cost effective to launch, run, and scale feature-rich, high-performance file systems in the cloud. It supports a wide range of workloads with its reliability, security, scalability, and broad set of capabilities. Amazon FSx for OpenZFS provides fully managed, cost-effective, shared file storage powered by the popular OpenZFS file system, and is designed to deliver sub-millisecond latencies and multi-GB/s throughput along with rich ZFS-powered data management capabilities (like snapshots, data cloning, and compression). To learn more about Amazon FSx for OpenZFS, visit our product page, and see the AWS Region Table for complete regional availability information.  

AWS today announced the general availability of Smithy-Java, an open-source Java framework for generating type-safe clients and standalone classes from Smithy models. Smithy-Java addresses one of the most consistently requested capabilities from enterprise Smithy users: production-grade Java SDK generation. The framework allows you to generate clients from models and async patterns that increase cognitive load and maintenance burden for developers building modern Java applications.

Built on Java 21’s virtual threads, Smithy-Java provides a blocking-style API that is both simpler to use and competitive in performance with complex async alternatives. Key benefits include auto-generated type-safe clients from Smithy, protocol flexibility with runtime protocol swapping for gradual migration paths. The GA release includes the Java client code generator, support for AWS SigV4 and all major AWS protocols (AWS JSON, REST-JSON, REST-XML, AWS Query, and Smithy RPCv2-CBOR), standalone type code generation for sharing types across multiple services or data modeling, and a dynamic client that can call Smithy services without a codegen step.

The framework pioneers two architectural innovations: schema-driven serialization that reduces SDK size while improving performance, and binary decision diagrams (BDD) for endpoint rules resolution delivering significant latency improvements. Internal Amazon teams have already built complete services in weeks rather than months using Smithy-Java, with service teams depending on it internally. The framework is ideal for organizations invested in the Smithy ecosystem, teams requiring protocol-agnostic development, and developers building new services with generated server stubs.

To learn more, visit our blog post and follow the Smithy Java Quickstart guide.

 

​AWS today announced the general availability of Smithy-Java, an open-source Java framework for generating type-safe clients and standalone classes from Smithy models. Smithy-Java addresses one of the most consistently requested capabilities from enterprise Smithy users: production-grade Java SDK generation. The framework allows you to generate clients from models and async patterns that increase cognitive load and maintenance burden for developers building modern Java applications.
Built on Java 21’s virtual threads, Smithy-Java provides a blocking-style API that is both simpler to use and competitive in performance with complex async alternatives. Key benefits include auto-generated type-safe clients from Smithy, protocol flexibility with runtime protocol swapping for gradual migration paths. The GA release includes the Java client code generator, support for AWS SigV4 and all major AWS protocols (AWS JSON, REST-JSON, REST-XML, AWS Query, and Smithy RPCv2-CBOR), standalone type code generation for sharing types across multiple services or data modeling, and a dynamic client that can call Smithy services without a codegen step.
The framework pioneers two architectural innovations: schema-driven serialization that reduces SDK size while improving performance, and binary decision diagrams (BDD) for endpoint rules resolution delivering significant latency improvements. Internal Amazon teams have already built complete services in weeks rather than months using Smithy-Java, with service teams depending on it internally. The framework is ideal for organizations invested in the Smithy ecosystem, teams requiring protocol-agnostic development, and developers building new services with generated server stubs.
To learn more, visit our blog post and follow the Smithy Java Quickstart guide.  

Today, AWS announces support for policy store aliases and named policies and policy templates in Amazon Verified Permissions, simplifying multi-tenant deployments and day-to-day policy management. Amazon Verified Permissions is a fine-grained authorization service that helps you manage and enforce permissions across your applications using Cedar policies. These new capabilities eliminate the need to maintain separate mapping tables for associating tenant identifiers with policy store IDs or tracking individual policy and template IDs.

With policy store aliases, multi-tenant application developers can assign a human-readable alias based on a tenant identifier and use it in any API call, removing the need for a lookup table. Similarly, named policies and policy templates let you reference policies by meaningful names instead of system-generated IDs, making it easier to manage authorization logic as your application grows.

Amazon Verified Permissions policy store aliases and named policies and templates are available in all AWS Regions where Amazon Verified Permissions is available. For a full list of supported Regions, see Amazon Verified Permissions endpoints and quotas.

To get started, see Policy store aliases and Creating static policies in the Amazon Verified Permissions User Guide, or visit the Amazon Verified Permissions API Reference.

 

​Today, AWS announces support for policy store aliases and named policies and policy templates in Amazon Verified Permissions, simplifying multi-tenant deployments and day-to-day policy management. Amazon Verified Permissions is a fine-grained authorization service that helps you manage and enforce permissions across your applications using Cedar policies. These new capabilities eliminate the need to maintain separate mapping tables for associating tenant identifiers with policy store IDs or tracking individual policy and template IDs.
With policy store aliases, multi-tenant application developers can assign a human-readable alias based on a tenant identifier and use it in any API call, removing the need for a lookup table. Similarly, named policies and policy templates let you reference policies by meaningful names instead of system-generated IDs, making it easier to manage authorization logic as your application grows.
Amazon Verified Permissions policy store aliases and named policies and templates are available in all AWS Regions where Amazon Verified Permissions is available. For a full list of supported Regions, see Amazon Verified Permissions endpoints and quotas.
To get started, see Policy store aliases and Creating static policies in the Amazon Verified Permissions User Guide, or visit the Amazon Verified Permissions API Reference.