Amazon ECR now offers a new archive storage class to reduce storage costs for large volumes of rarely accessed container images. The new archive storage class helps you meet your compliance and retention requirements while optimizing storage cost. As part of this launch, ECR lifecycle policies now support archiving images based on last pull time, allowing you to use lifecycle rules to automatically archive images based on usage patterns.

To get started, you can archive images by configuring lifecycle rules to automatically archive images based on criteria such as image age, count, or last pull time, or using the ECR Console or API to archive images individually. You can archive an unlimited number of images. Archived images do not count against your image per repository limit. Once the images are archived, they are no longer accessible for pulls, but can be easily restored via ECR Console, CLI, or API within 20 minutes. Once restored, images can be pulled normally. All archival and restore operations are logged through CloudTrail for auditability.

The new ECR archive storage class is available in all AWS Commercial and AWS GovCloud (US) Regions. For pricing, visit the pricing page. To learn more, visit the documentation.

 

​Amazon ECR now offers a new archive storage class to reduce storage costs for large volumes of rarely accessed container images. The new archive storage class helps you meet your compliance and retention requirements while optimizing storage cost. As part of this launch, ECR lifecycle policies now support archiving images based on last pull time, allowing you to use lifecycle rules to automatically archive images based on usage patterns. To get started, you can archive images by configuring lifecycle rules to automatically archive images based on criteria such as image age, count, or last pull time, or using the ECR Console or API to archive images individually. You can archive an unlimited number of images. Archived images do not count against your image per repository limit. Once the images are archived, they are no longer accessible for pulls, but can be easily restored via ECR Console, CLI, or API within 20 minutes. Once restored, images can be pulled normally. All archival and restore operations are logged through CloudTrail for auditability. The new ECR archive storage class is available in all AWS Commercial and AWS GovCloud (US) Regions. For pricing, visit the pricing page. To learn more, visit the documentation.  

Today, AWS announces the general availability of Amazon Elastic Compute Cloud (Amazon EC2) P6-B300 instances, accelerated by NVIDIA Blackwell Ultra B300 GPUs. Amazon EC2 P6-B300 instances provide 8x NVIDIA Blackwell Ultra GPUs with 2.1 TB high bandwidth GPU memory, 6.4 Tbps EFA networking, 300 Gbps dedicated ENA throughput, and 4 TB of system memory. 

P6-B300 instances deliver 2x networking bandwidth, 1.5x GPU memory size, and 1.5x GPU TFLOPS (at FP4, without sparsity) compared to P6-B200 instances, making them well suited to train and deploy large trillion-parameter foundation models (FMs) and large language models (LLMs) with sophisticated techniques. The higher networking and larger memory deliver faster training times and more token throughput for AI workloads. 

P6-B300 instances are now available in the p6-b300.48xlarge size through Amazon EC2 Capacity Blocks for ML and Savings Plans in the following AWS Region: US West (Oregon). For on-demand reservation of P6-B300 instances, please reach out to your account manager.

To learn more about P6-B300 instances, visit Amazon EC2 P6 instances.

 

​Today, AWS announces the general availability of Amazon Elastic Compute Cloud (Amazon EC2) P6-B300 instances, accelerated by NVIDIA Blackwell Ultra B300 GPUs. Amazon EC2 P6-B300 instances provide 8x NVIDIA Blackwell Ultra GPUs with 2.1 TB high bandwidth GPU memory, 6.4 Tbps EFA networking, 300 Gbps dedicated ENA throughput, and 4 TB of system memory. 
P6-B300 instances deliver 2x networking bandwidth, 1.5x GPU memory size, and 1.5x GPU TFLOPS (at FP4, without sparsity) compared to P6-B200 instances, making them well suited to train and deploy large trillion-parameter foundation models (FMs) and large language models (LLMs) with sophisticated techniques. The higher networking and larger memory deliver faster training times and more token throughput for AI workloads. 
P6-B300 instances are now available in the p6-b300.48xlarge size through Amazon EC2 Capacity Blocks for ML and Savings Plans in the following AWS Region: US West (Oregon). For on-demand reservation of P6-B300 instances, please reach out to your account manager.
To learn more about P6-B300 instances, visit Amazon EC2 P6 instances.  

Today, Amazon Bedrock introduces two new inference service tiers to optimize costs and performance for different AI workloads. The new Flex tier offers cost-effective pricing for non-time-critical applications like model evaluations and content summarization while the Priority tier provides premium performance and preferential processing for mission-critical applications. For most models that support Priority Tier, customers can realize up to 25% better output tokens per second (OTPS) latency compared to standard tier. These join the existing Standard tier for everyday AI applications with reliable performance.

These service tiers address key challenges that organizations face when deploying AI at scale. The Flex tier is designed for non-interactive workloads that can tolerate longer latencies, making it ideal for model evaluations, content summarization, labeling and annotation, and multistep agentic workflow, and it’s priced at a discount relative to the Standard tier. During periods of high demand, Flex requests receive lower priority relative to the Standard tier. The Priority tier is an ideal fit for mission critical applications, real-time end-user interactions, and interactive experiences where consistent, fast responses are essential. During periods of high demand, Priority requests receive processing priority, at a premium price, over other service tiers. These new service tiers are available today for a range of leading foundation models, including OpenAI (gpt-oss-20b, gpt-oss-120b), DeepSeek (DeepSeek V3.1), Qwen3 (Coder-480B-A35B-Instruct, Coder-30B-A3B-Instruct, 32B dense, Qwen3-235B-A22B-2507), and Amazon Nova (Nova Pro and Nova Premier). With these new options, Amazon Bedrock helps customers gain greater control over balancing cost efficiency with performance requirements, enabling them to scale AI workloads economically while ensuring optimal user experiences for their most critical applications.

For more information about the AWS Regions where Amazon Bedrock Priority and Flex inference service tiers are available, see the AWS Regions table

Learn more about service tiers in our News Blog and documentation.

 

​Today, Amazon Bedrock introduces two new inference service tiers to optimize costs and performance for different AI workloads. The new Flex tier offers cost-effective pricing for non-time-critical applications like model evaluations and content summarization while the Priority tier provides premium performance and preferential processing for mission-critical applications. For most models that support Priority Tier, customers can realize up to 25% better output tokens per second (OTPS) latency compared to standard tier. These join the existing Standard tier for everyday AI applications with reliable performance.
These service tiers address key challenges that organizations face when deploying AI at scale. The Flex tier is designed for non-interactive workloads that can tolerate longer latencies, making it ideal for model evaluations, content summarization, labeling and annotation, and multistep agentic workflow, and it’s priced at a discount relative to the Standard tier. During periods of high demand, Flex requests receive lower priority relative to the Standard tier. The Priority tier is an ideal fit for mission critical applications, real-time end-user interactions, and interactive experiences where consistent, fast responses are essential. During periods of high demand, Priority requests receive processing priority, at a premium price, over other service tiers. These new service tiers are available today for a range of leading foundation models, including OpenAI (gpt-oss-20b, gpt-oss-120b), DeepSeek (DeepSeek V3.1), Qwen3 (Coder-480B-A35B-Instruct, Coder-30B-A3B-Instruct, 32B dense, Qwen3-235B-A22B-2507), and Amazon Nova (Nova Pro and Nova Premier). With these new options, Amazon Bedrock helps customers gain greater control over balancing cost efficiency with performance requirements, enabling them to scale AI workloads economically while ensuring optimal user experiences for their most critical applications.
For more information about the AWS Regions where Amazon Bedrock Priority and Flex inference service tiers are available, see the AWS Regions table
Learn more about service tiers in our News Blog and documentation.  

AWS Lambda now supports creating serverless applications using Python 3.14. Developers can use Python 3.14 as both a managed runtime and a container base image, and AWS will automatically apply updates to the managed runtime and base image as they become available.

Python 3.14 is the latest long-term support release of Python. This release provides Lambda customers access to the latest Python 3.14 language features. You can use Python 3.14 with Lambda@Edge (in supported Regions), allowing you to customize low-latency content delivered through Amazon CloudFront. Powertools for AWS Lambda (Python), a developer toolkit to implement serverless best practices and increase developer velocity, also supports Python 3.14. You can use the full range of AWS deployment tools, including the Lambda console, AWS CLI, AWS Serverless Application Model (AWS SAM), AWS CDK, and AWS CloudFormation to deploy and manage serverless applications written in Python 3.14.

The Python 3.14 runtime is available in all Regions, including the AWS GovCloud (US) Regions and China Regions.

For more information, including guidance on upgrading existing Lambda functions, read our blog post. For more information about AWS Lambda, visit the product page. 

 

​AWS Lambda now supports creating serverless applications using Python 3.14. Developers can use Python 3.14 as both a managed runtime and a container base image, and AWS will automatically apply updates to the managed runtime and base image as they become available. Python 3.14 is the latest long-term support release of Python. This release provides Lambda customers access to the latest Python 3.14 language features. You can use Python 3.14 with Lambda@Edge (in supported Regions), allowing you to customize low-latency content delivered through Amazon CloudFront. Powertools for AWS Lambda (Python), a developer toolkit to implement serverless best practices and increase developer velocity, also supports Python 3.14. You can use the full range of AWS deployment tools, including the Lambda console, AWS CLI, AWS Serverless Application Model (AWS SAM), AWS CDK, and AWS CloudFormation to deploy and manage serverless applications written in Python 3.14. The Python 3.14 runtime is available in all Regions, including the AWS GovCloud (US) Regions and China Regions. For more information, including guidance on upgrading existing Lambda functions, read our blog post. For more information about AWS Lambda, visit the product page.   

Amazon OpenSearch Serverless now supports detailed audit logging of data plane requests via AWS CloudTrail. This feature enables customers to record user actions on their collections, helping meet compliance regulations, improve security posture, and provide evidence for security investigations. Customers can now track user activities such as authorization attempts, index modifications, and search queries.

Customers can use CloudTrail to configure filters for OpenSearch Serverless collections with read-only and write-only options, or use advanced event selectors for more granular control over logged data events. All OpenSearch Serverless data events are delivered to an Amazon S3 bucket and optionally to Amazon CloudWatch Events, creating a comprehensive audit trail. This enhanced visibility into when and who made API calls helps security and operations teams monitor data access and respond to events in real-time.

Once configured with CloudTrail, audit logs will be continuously streamed with no additional customer action required. Audit Logs will be continuously streamed to CloudTrail and can be further analyzed there.

Please refer to the AWS Regional Services List for more information about Amazon OpenSearch Service availability. To learn more about OpenSearch Serverless, see the documentation. 

 

​Amazon OpenSearch Serverless now supports detailed audit logging of data plane requests via AWS CloudTrail. This feature enables customers to record user actions on their collections, helping meet compliance regulations, improve security posture, and provide evidence for security investigations. Customers can now track user activities such as authorization attempts, index modifications, and search queries. Customers can use CloudTrail to configure filters for OpenSearch Serverless collections with read-only and write-only options, or use advanced event selectors for more granular control over logged data events. All OpenSearch Serverless data events are delivered to an Amazon S3 bucket and optionally to Amazon CloudWatch Events, creating a comprehensive audit trail. This enhanced visibility into when and who made API calls helps security and operations teams monitor data access and respond to events in real-time. Once configured with CloudTrail, audit logs will be continuously streamed with no additional customer action required. Audit Logs will be continuously streamed to CloudTrail and can be further analyzed there. Please refer to the AWS Regional Services List for more information about Amazon OpenSearch Service availability. To learn more about OpenSearch Serverless, see the documentation.   

Today, EC2 Auto Scaling is launching a new API, LaunchInstances, which gives customers more control and flexibility over how EC2 Auto Scaling provisions instances while providing instant feedback on capacity availability.

Customers use EC2 Auto Scaling for automated fleet management. With scaling policies, EC2 Auto Scaling can automatically add instances when demand spikes and remove them when traffic drops, ensuring customers’ applications always have the right amount of compute. EC2 Auto Scaling also offers the ability to monitor and replace unhealthy instances. In certain use cases, customers may want to specify exactly where EC2 Auto Scaling should launch additional instances and need immediate feedback on capacity availability. The new LaunchInstances API allows customers to precisely control where instances are launched by specifying an override for any Availability Zone and/or subnet in an Auto Scaling group, while providing immediate feedback on capacity availability. This synchronous operation gives customers real-time insight into scaling operations, enabling them to quickly implement alternative strategies if needed. For additional flexibility, the API includes optional asynchronous retries to help reach the desired capacity.

This feature is now available in US East (N. Virginia), US West (Oregon), Europe (Ireland), and Asia Pacific (Singapore), at no additional cost beyond standard EC2 and EBS usage. To get started, visit the AWS Command Line Interface (CLI) and the AWS SDKs. To learn more about this feature, visit the AWS documentation. 

 

​Today, EC2 Auto Scaling is launching a new API, LaunchInstances, which gives customers more control and flexibility over how EC2 Auto Scaling provisions instances while providing instant feedback on capacity availability. Customers use EC2 Auto Scaling for automated fleet management. With scaling policies, EC2 Auto Scaling can automatically add instances when demand spikes and remove them when traffic drops, ensuring customers’ applications always have the right amount of compute. EC2 Auto Scaling also offers the ability to monitor and replace unhealthy instances. In certain use cases, customers may want to specify exactly where EC2 Auto Scaling should launch additional instances and need immediate feedback on capacity availability. The new LaunchInstances API allows customers to precisely control where instances are launched by specifying an override for any Availability Zone and/or subnet in an Auto Scaling group, while providing immediate feedback on capacity availability. This synchronous operation gives customers real-time insight into scaling operations, enabling them to quickly implement alternative strategies if needed. For additional flexibility, the API includes optional asynchronous retries to help reach the desired capacity. This feature is now available in US East (N. Virginia), US West (Oregon), Europe (Ireland), and Asia Pacific (Singapore), at no additional cost beyond standard EC2 and EBS usage. To get started, visit the AWS Command Line Interface (CLI) and the AWS SDKs. To learn more about this feature, visit the AWS documentation.   

Amazon Relational Database Service (RDS) now supports R8gd and M8gd database instances for Optimized Reads on Amazon Aurora PostgreSQL and RDS for PostgreSQL, MySQL, and MariaDB. R8gd and M8gd database instances offer improved price-performance. For example, Optimized Reads on R8gd instances deliver up to 165% better throughput and up to 120% better price-performance over R6g instances for Aurora PostgreSQL.

Optimized Reads uses local NVMe-based SSD block storage available on these instances to store ephemeral data, such as temporary tables, reducing data access to/from network-based storage and improving read latency and throughput. The result is improved query performance for complex queries and faster index rebuild operations. Aurora PostgreSQL Optimized Reads instances using the I/O-Optimized configuration additionally use the local storage to extend their caching capacity. Database pages that are evicted from the in-memory buffer cache are cached in local storage to speed subsequent retrieval of that data.

Customers can get started with Optimized Reads through the AWS Management Console, CLI, and SDK by modifying their existing Aurora and RDS databases or creating a new database using R8gd or M8gd instances. These instances are available in the US East (N. Virginia, Ohio), US West (Oregon), Europe (Spain, Frankfurt), and Asia Pacific (Tokyo) Regions. For complete information on pricing and regional availability, please refer to the pricing page. For information on specific engine versions that support these DB instance types, please see the Aurora and RDS documentation.

 

​Amazon Relational Database Service (RDS) now supports R8gd and M8gd database instances for Optimized Reads on Amazon Aurora PostgreSQL and RDS for PostgreSQL, MySQL, and MariaDB. R8gd and M8gd database instances offer improved price-performance. For example, Optimized Reads on R8gd instances deliver up to 165% better throughput and up to 120% better price-performance over R6g instances for Aurora PostgreSQL. Optimized Reads uses local NVMe-based SSD block storage available on these instances to store ephemeral data, such as temporary tables, reducing data access to/from network-based storage and improving read latency and throughput. The result is improved query performance for complex queries and faster index rebuild operations. Aurora PostgreSQL Optimized Reads instances using the I/O-Optimized configuration additionally use the local storage to extend their caching capacity. Database pages that are evicted from the in-memory buffer cache are cached in local storage to speed subsequent retrieval of that data. Customers can get started with Optimized Reads through the AWS Management Console, CLI, and SDK by modifying their existing Aurora and RDS databases or creating a new database using R8gd or M8gd instances. These instances are available in the US East (N. Virginia, Ohio), US West (Oregon), Europe (Spain, Frankfurt), and Asia Pacific (Tokyo) Regions. For complete information on pricing and regional availability, please refer to the pricing page. For information on specific engine versions that support these DB instance types, please see the Aurora and RDS documentation.  

AWS Builder Center now provides access to the catalog of AWS Workshops, offering step-by-step instructions crafted by AWS experts that explain how to deploy and use AWS services effectively. These workshops cover a wide range of AWS services and use cases, allowing builders to follow guided tutorials within their own AWS accounts. Workshops are designed for builders of all skill levels to gain practical experience and develop solutions tailored to their specific business needs using AWS services.

The AWS Workshops Catalog features hundreds of workshops with advanced filtering capabilities to quickly find relevant content by category (Machine Learning, Security, Serverless), AWS service (EC2, Lambda, S3), and complexity level (100-Beginner through 400-Expert). Real-time search with partial matching across workshop titles, descriptions, services, and categories helps surface the most relevant content. Catalog content automatically localized based on your Builder Center language preference.

Builders can navigate to the Workshops catalog at builder.aws.com/build/workshops and filter by specific needs—whether you have 1 hour or 8 hours, are a beginner or expert, or want to focus on specific services like Amazon Bedrock and SageMaker. Seamless navigation from Builder Center discovery to the full workshops experience enables hands-on, step-by-step guided learning in your own AWS account.

You can begin exploring Workshops in AWS Builder Center immediately with a free Builder ID. To get started with Workshops, visit AWS Builder Center.

 

​AWS Builder Center now provides access to the catalog of AWS Workshops, offering step-by-step instructions crafted by AWS experts that explain how to deploy and use AWS services effectively. These workshops cover a wide range of AWS services and use cases, allowing builders to follow guided tutorials within their own AWS accounts. Workshops are designed for builders of all skill levels to gain practical experience and develop solutions tailored to their specific business needs using AWS services.
The AWS Workshops Catalog features hundreds of workshops with advanced filtering capabilities to quickly find relevant content by category (Machine Learning, Security, Serverless), AWS service (EC2, Lambda, S3), and complexity level (100-Beginner through 400-Expert). Real-time search with partial matching across workshop titles, descriptions, services, and categories helps surface the most relevant content. Catalog content automatically localized based on your Builder Center language preference.
Builders can navigate to the Workshops catalog at builder.aws.com/build/workshops and filter by specific needs—whether you have 1 hour or 8 hours, are a beginner or expert, or want to focus on specific services like Amazon Bedrock and SageMaker. Seamless navigation from Builder Center discovery to the full workshops experience enables hands-on, step-by-step guided learning in your own AWS account.
You can begin exploring Workshops in AWS Builder Center immediately with a free Builder ID. To get started with Workshops, visit AWS Builder Center.  

Amazon Web Services (AWS) is launching flat-rate pricing plans with no overages for website delivery and security. The flat-rate plans, available with Amazon CloudFront, combine global content delivery with AWS WAF, DDoS protection, Amazon Route 53 DNS, Amazon CloudWatch Logs ingestion, and serverless edge compute into a simple monthly price with no overage charges. Each plan also includes monthly Amazon S3 storage credits to help offset your storage costs.

CloudFront flat-rate plans allow you to deliver your websites and applications without calculating costs across multiple AWS services. You won’t face the risk of overage charges, even if your website or application goes viral or faces a DDoS attack. Security features like WAF and DDoS protection are enabled by default, and additional configurations are simple to set up. When you serve your AWS applications through CloudFront instead of directly to the internet, your flat-rate plan covers the data transfer costs between your applications and your viewers for a simple monthly price without the worry of overages. This simplified pricing model is available alongside pay-as-you-go pricing for each CloudFront distribution, giving you the flexibility to choose the right pricing model and feature set for each application.

Plans are available in Free ($0/month), Pro ($15/month), Business ($200/month), and Premium ($1,000/month) tiers for new and existing CloudFront distributions. Select the plan tier with the features and usage allowances matching your application’s needs. To learn more, refer to the Launch Blog, Plans and Pricing, or CloudFront Developer Guide. To get started, visit the CloudFront console.

 

​Amazon Web Services (AWS) is launching flat-rate pricing plans with no overages for website delivery and security. The flat-rate plans, available with Amazon CloudFront, combine global content delivery with AWS WAF, DDoS protection, Amazon Route 53 DNS, Amazon CloudWatch Logs ingestion, and serverless edge compute into a simple monthly price with no overage charges. Each plan also includes monthly Amazon S3 storage credits to help offset your storage costs. CloudFront flat-rate plans allow you to deliver your websites and applications without calculating costs across multiple AWS services. You won’t face the risk of overage charges, even if your website or application goes viral or faces a DDoS attack. Security features like WAF and DDoS protection are enabled by default, and additional configurations are simple to set up. When you serve your AWS applications through CloudFront instead of directly to the internet, your flat-rate plan covers the data transfer costs between your applications and your viewers for a simple monthly price without the worry of overages. This simplified pricing model is available alongside pay-as-you-go pricing for each CloudFront distribution, giving you the flexibility to choose the right pricing model and feature set for each application. Plans are available in Free ($0/month), Pro ($15/month), Business ($200/month), and Premium ($1,000/month) tiers for new and existing CloudFront distributions. Select the plan tier with the features and usage allowances matching your application’s needs. To learn more, refer to the Launch Blog, Plans and Pricing, or CloudFront Developer Guide. To get started, visit the CloudFront console.  

AWS Transfer Family Terraform module now supports deployment of automated malware scanning workflows for files transferred using Transfer Family resources. This release streamlines centralized provisioning of threat detection workflows using Amazon GuardDuty S3 Protection, helping you meet data security requirements by identifying potential threats in transferred files.

AWS Transfer Family provides fully managed file transfers over SFTP, AS2, FTPS, FTP, and web browser-
based interfaces for AWS storage services. Using the new module, you can programmatically provision workflows to scan incoming files, dynamically route files based on scan results, and generate threat notifications, in a single deployment. You can granularly implement threat detection for specific S3 prefixes while preserving folder structures post scanning, and ensure that only verified clean files reach your business applications and data lakes. This eliminates the overhead and risks associated with manual configurations, and provides a scalable deployment option for data security compliance.

Customers can get started by using the new module from the Terraform Registry. To learn more about Transfer Family, visit the product page and user guide. To see all the regions where Transfer Family is available, visit the AWS Region table.

 

​AWS Transfer Family Terraform module now supports deployment of automated malware scanning workflows for files transferred using Transfer Family resources. This release streamlines centralized provisioning of threat detection workflows using Amazon GuardDuty S3 Protection, helping you meet data security requirements by identifying potential threats in transferred files. AWS Transfer Family provides fully managed file transfers over SFTP, AS2, FTPS, FTP, and web browser- based interfaces for AWS storage services. Using the new module, you can programmatically provision workflows to scan incoming files, dynamically route files based on scan results, and generate threat notifications, in a single deployment. You can granularly implement threat detection for specific S3 prefixes while preserving folder structures post scanning, and ensure that only verified clean files reach your business applications and data lakes. This eliminates the overhead and risks associated with manual configurations, and provides a scalable deployment option for data security compliance. Customers can get started by using the new module from the Terraform Registry. To learn more about Transfer Family, visit the product page and user guide. To see all the regions where Transfer Family is available, visit the AWS Region table.