You can now use AWS PrivateLink to privately access Amazon ElastiCache from your Amazon Virtual Private Cloud (Amazon VPC). AWS PrivateLink provides private connectivity between VPCs, AWS services, and on-premises networks, without exposing traffic to the public internet and securing your network traffic. The Amazon ElastiCache API supports AWS PrivateLink in AWS Asia Pacific (Jakarta) and Asia Pacific (Hyderabad) Regions.

To use AWS PrivateLink with Amazon ElastiCache, you create an interface VPC endpoint for Amazon ElastiCache in your VPC using the Amazon VPC console, AWS SDK, or AWS CLI. With an interface VPC endpoint, you can privately access the Amazon ElastiCache APIs from applications inside your Amazon VPC. You can also access the VPC endpoint from other VPCs using VPC Peering or your on-premises environments using AWS VPN or AWS Direct Connect. To learn more, read the documentation, or get started in the Amazon VPC Console.

 

​You can now use AWS PrivateLink to privately access Amazon ElastiCache from your Amazon Virtual Private Cloud (Amazon VPC). AWS PrivateLink provides private connectivity between VPCs, AWS services, and on-premises networks, without exposing traffic to the public internet and securing your network traffic. The Amazon ElastiCache API supports AWS PrivateLink in AWS Asia Pacific (Jakarta) and Asia Pacific (Hyderabad) Regions. To use AWS PrivateLink with Amazon ElastiCache, you create an interface VPC endpoint for Amazon ElastiCache in your VPC using the Amazon VPC console, AWS SDK, or AWS CLI. With an interface VPC endpoint, you can privately access the Amazon ElastiCache APIs from applications inside your Amazon VPC. You can also access the VPC endpoint from other VPCs using VPC Peering or your on-premises environments using AWS VPN or AWS Direct Connect. To learn more, read the documentation, or get started in the Amazon VPC Console.  

You can now use Amazon EBS gp3 and io1 volumes in AWS Dedicated Local Zones. Dedicated Local Zones are a type of AWS infrastructure that are fully managed by AWS, built for exclusive use by you or your community, and placed in a location or data center specified by you to help you comply with regulatory requirements. In Dedicated Local Zones, these volumes are purpose-built to store data in a specific data perimeter, helping to support your data isolation and data residency use cases.

The latest generation of General Purpose SSD volumes (gp3) enable customers to provision performance independently of storage capacity, providing up to 20% lower price point per GB than existing gp2 volumes. Provisioned IOPS SSD (io1) volumes are designed to meet the needs of I/O-intensive and latency-sensitive transactional workloads like databases.

You can manage gp3 and io1 volumes using the AWS Management Console, the AWS Command Line Interface (CLI), or the AWS SDKs. For more information on gp3 and io1 volumes, see the product overview page.
 

 

​You can now use Amazon EBS gp3 and io1 volumes in AWS Dedicated Local Zones. Dedicated Local Zones are a type of AWS infrastructure that are fully managed by AWS, built for exclusive use by you or your community, and placed in a location or data center specified by you to help you comply with regulatory requirements. In Dedicated Local Zones, these volumes are purpose-built to store data in a specific data perimeter, helping to support your data isolation and data residency use cases. The latest generation of General Purpose SSD volumes (gp3) enable customers to provision performance independently of storage capacity, providing up to 20% lower price point per GB than existing gp2 volumes. Provisioned IOPS SSD (io1) volumes are designed to meet the needs of I/O-intensive and latency-sensitive transactional workloads like databases. You can manage gp3 and io1 volumes using the AWS Management Console, the AWS Command Line Interface (CLI), or the AWS SDKs. For more information on gp3 and io1 volumes, see the product overview page.    

Starting today, you can build, train, and deploy machine learning (ML) models in Mexico (Central).

Amazon SageMaker AI is a fully managed platform that provides every developer and data scientist with the ability to build, train, and deploy machine learning (ML) models quickly. SageMaker AI removes the heavy lifting from each step of the machine learning process to make it easier to develop high quality models.

To learn more and get started, see SageMaker AI documentation and pricing page.
 

 

​Starting today, you can build, train, and deploy machine learning (ML) models in Mexico (Central). Amazon SageMaker AI is a fully managed platform that provides every developer and data scientist with the ability to build, train, and deploy machine learning (ML) models quickly. SageMaker AI removes the heavy lifting from each step of the machine learning process to make it easier to develop high quality models. To learn more and get started, see SageMaker AI documentation and pricing page.    

AWS CodeBuild now supports an enhanced S3 caching experience. You can now define custom cache keys for more granular cache management and improved cache persistence across your builds. You can also share the cache keys across projects to use a common dependency cache to speed up your builds. AWS CodeBuild is a fully managed continuous integration service that compiles source code, runs tests, and produces software packages ready for deployment.

Additionally, CodeBuild added support for fallback keys, which allows partial matches when an exact cache key is not found. This capability enables efficient caching sharing between similar builds, such as builds with common dependencies, without needing to rebuild everything. You can also specify an optional action to skip the cache save or restore step for a more flexible cache management.

These caching enhancements are available in all AWS Regions where CodeBuild is offered. To learn more, please visit our documentation. To get started with CodeBuild, visit the AWS CodeBuild product page.

 

​AWS CodeBuild now supports an enhanced S3 caching experience. You can now define custom cache keys for more granular cache management and improved cache persistence across your builds. You can also share the cache keys across projects to use a common dependency cache to speed up your builds. AWS CodeBuild is a fully managed continuous integration service that compiles source code, runs tests, and produces software packages ready for deployment. Additionally, CodeBuild added support for fallback keys, which allows partial matches when an exact cache key is not found. This capability enables efficient caching sharing between similar builds, such as builds with common dependencies, without needing to rebuild everything. You can also specify an optional action to skip the cache save or restore step for a more flexible cache management. These caching enhancements are available in all AWS Regions where CodeBuild is offered. To learn more, please visit our documentation. To get started with CodeBuild, visit the AWS CodeBuild product page.  

Starting today, Amazon Elastic Compute Cloud (Amazon EC2) C8g instances are available in AWS Asia Pacific (Tokyo) region. These instances are powered by AWS Graviton4 processors and deliver up to 30% better performance compared to AWS Graviton3-based instances. Amazon EC2 C8g instances are built for compute-intensive workloads, such as high performance computing (HPC), batch processing, gaming, video encoding, scientific modeling, distributed analytics, CPU-based machine learning (ML) inference, and ad serving. These instances are built on the AWS Nitro System, which offloads CPU virtualization, storage, and networking functions to dedicated hardware and software to enhance the performance and security of your workloads.

AWS Graviton4-based Amazon EC2 instances deliver the best performance and energy efficiency for a broad range of workloads running on Amazon EC2. These instances offer larger instance sizes with up to 3x more vCPUs and memory compared to Graviton3-based Amazon C7g instances. AWS Graviton4 processors are up to 40% faster for databases, 30% faster for web applications, and 45% faster for large Java applications than AWS Graviton3 processors. C8g instances are available in 12 different instance sizes, including two bare metal sizes. They offer up to 50 Gbps enhanced networking bandwidth and up to 40 Gbps of bandwidth to the Amazon Elastic Block Store (Amazon EBS).

To learn more, see Amazon EC2 C8g Instances. To explore how to migrate your workloads to Graviton-based instances, see AWS Graviton Fast Start program and Porting Advisor for Graviton. To get started, see the AWS Management Console.
 

 

​Starting today, Amazon Elastic Compute Cloud (Amazon EC2) C8g instances are available in AWS Asia Pacific (Tokyo) region. These instances are powered by AWS Graviton4 processors and deliver up to 30% better performance compared to AWS Graviton3-based instances. Amazon EC2 C8g instances are built for compute-intensive workloads, such as high performance computing (HPC), batch processing, gaming, video encoding, scientific modeling, distributed analytics, CPU-based machine learning (ML) inference, and ad serving. These instances are built on the AWS Nitro System, which offloads CPU virtualization, storage, and networking functions to dedicated hardware and software to enhance the performance and security of your workloads. AWS Graviton4-based Amazon EC2 instances deliver the best performance and energy efficiency for a broad range of workloads running on Amazon EC2. These instances offer larger instance sizes with up to 3x more vCPUs and memory compared to Graviton3-based Amazon C7g instances. AWS Graviton4 processors are up to 40% faster for databases, 30% faster for web applications, and 45% faster for large Java applications than AWS Graviton3 processors. C8g instances are available in 12 different instance sizes, including two bare metal sizes. They offer up to 50 Gbps enhanced networking bandwidth and up to 40 Gbps of bandwidth to the Amazon Elastic Block Store (Amazon EBS). To learn more, see Amazon EC2 C8g Instances. To explore how to migrate your workloads to Graviton-based instances, see AWS Graviton Fast Start program and Porting Advisor for Graviton. To get started, see the AWS Management Console.    

Starting today, you can build, train, and deploy machine learning (ML) models in Asia Pacific (Thailand).

Amazon SageMaker AI is a fully managed platform that provides every developer and data scientist with the ability to build, train, and deploy machine learning (ML) models quickly. SageMaker AI removes the heavy lifting from each step of the machine learning process to make it easier to develop high quality models.

To learn more and get started, see SageMaker AI documentation and pricing page.
 

 

​Starting today, you can build, train, and deploy machine learning (ML) models in Asia Pacific (Thailand). Amazon SageMaker AI is a fully managed platform that provides every developer and data scientist with the ability to build, train, and deploy machine learning (ML) models quickly. SageMaker AI removes the heavy lifting from each step of the machine learning process to make it easier to develop high quality models. To learn more and get started, see SageMaker AI documentation and pricing page.    

Amazon GameLift Servers now supports Amazon EC2 5th through 8th generation instances, offering enhanced price-performance, efficiency, and flexibility for game server hosting. This update allows developers to leverage the latest advancements in EC2 compute, memory, and networking across three main instance families:

1. General Purpose (M-series): Balanced CPU, memory, and networking for a wide range of game workloads.
2. Compute Optimized (C-series): High-performance compute instances with a 2:1 memory ratio, ideal for CPU-intensive game servers.
3. Memory Optimized (R-Series): Optimized for high-memory workloads with an 8:1 memory ratio, supporting complex simulations and large player sessions.

Each new EC2 generation brings significant improvements:

• 5th Gen: Proven reliability with Intel processors with balanced performance
• 6th Gen: Includes AWS Graviton2 ARM-based options alongside Intel and AMD variants offering enhanced price-performance efficiency.
• 7th Gen: The latest evolution featuring DDR5 memory, enhanced networking, and offering significant performance gains over previous generations.
• 8th Gen: Cutting-edge AWS Graviton4 and Intel Xeon-based instances for demanding workloads

Customers can also choose variants with local storage (d), enhanced networking (n), and different processor architectures (Intel, AMD, Graviton – i/a/g).

This update empowers developers with greater flexibility, scalability, and cost efficiency to optimize game server performance. Customers can now seamlessly transition workloads to newer EC2 generations, leveraging AWS’s continuous innovation for building, scaling, and operating multiplayer games globally.

These next-generation instances are available in Amazon GameLift Servers supported regions, except AWS China. For more information on launching fleets with next-generation EC2 instances, visit the Amazon GameLift Servers documentation and EC2 Instance Types overview.

 

​Amazon GameLift Servers now supports Amazon EC2 5th through 8th generation instances, offering enhanced price-performance, efficiency, and flexibility for game server hosting. This update allows developers to leverage the latest advancements in EC2 compute, memory, and networking across three main instance families:

General Purpose (M-series): Balanced CPU, memory, and networking for a wide range of game workloads.
Compute Optimized (C-series): High-performance compute instances with a 2:1 memory ratio, ideal for CPU-intensive game servers.
Memory Optimized (R-Series): Optimized for high-memory workloads with an 8:1 memory ratio, supporting complex simulations and large player sessions.

Each new EC2 generation brings significant improvements:

5th Gen: Proven reliability with Intel processors with balanced performance
6th Gen: Includes AWS Graviton2 ARM-based options alongside Intel and AMD variants offering enhanced price-performance efficiency.
7th Gen: The latest evolution featuring DDR5 memory, enhanced networking, and offering significant performance gains over previous generations.
8th Gen: Cutting-edge AWS Graviton4 and Intel Xeon-based instances for demanding workloads

Customers can also choose variants with local storage (d), enhanced networking (n), and different processor architectures (Intel, AMD, Graviton – i/a/g). This update empowers developers with greater flexibility, scalability, and cost efficiency to optimize game server performance. Customers can now seamlessly transition workloads to newer EC2 generations, leveraging AWS’s continuous innovation for building, scaling, and operating multiplayer games globally. These next-generation instances are available in Amazon GameLift Servers supported regions, except AWS China. For more information on launching fleets with next-generation EC2 instances, visit the Amazon GameLift Servers documentation and EC2 Instance Types overview.  

Amazon DynamoDB now supports percentile statistics for the SuccessfulRequestLatency Amazon CloudWatch metric. The percentile statistic enables you to understand the latency distribution of your successful requests to DynamoDB, complementing the existing average, minimum, and maximum statistics.

The SuccessfulRequestLatency metric only measures latency which is internal to the Amazon DynamoDB service – client side activity and network trip times are not included. It’s normal to see some variability in this metric. When analyzing your latency, it’s best to consider your end-to-end latency which includes client side activity. To factor in your client side activity, you can enable latency metric logging in your AWS SDK.

The new percentile statistic is available in all commercial AWS Regions, the AWS GovCloud (US) Regions, and the China Regions.

To learn more about the percentile statistic and troubleshooting latency on DynamoDB, see the following:

• DynamoDB Metrics and Dimensions in the DynamoDB Developer Guide
• Troubleshooting latency issues in Amazon DynamoDB in the DynamoDB Developer Guide

 

​Amazon DynamoDB now supports percentile statistics for the SuccessfulRequestLatency Amazon CloudWatch metric. The percentile statistic enables you to understand the latency distribution of your successful requests to DynamoDB, complementing the existing average, minimum, and maximum statistics. The SuccessfulRequestLatency metric only measures latency which is internal to the Amazon DynamoDB service – client side activity and network trip times are not included. It’s normal to see some variability in this metric. When analyzing your latency, it’s best to consider your end-to-end latency which includes client side activity. To factor in your client side activity, you can enable latency metric logging in your AWS SDK. The new percentile statistic is available in all commercial AWS Regions, the AWS GovCloud (US) Regions, and the China Regions. To learn more about the percentile statistic and troubleshooting latency on DynamoDB, see the following:

DynamoDB Metrics and Dimensions in the DynamoDB Developer Guide
Troubleshooting latency issues in Amazon DynamoDB in the DynamoDB Developer Guide  

AWS Network Manager and AWS Cloud WAN now support AWS PrivateLink and IPv6 based connectivity to the management endpoint of these services. Using PrivateLink, customers can now access AWS Network Manager or AWS Cloud WAN privately on the AWS network, without going through the public Internet. Additionally, customers can now access these services over IPv6 using dual-stack endpoints.

With AWS Cloud WAN, you can use a central dashboard and network policies to create a global network that spans multiple locations and networks, allowing you to configure and manage different networks using the same technology. The Cloud WAN central dashboard, powered by AWS Network Manager, generates a complete view of the network to help you monitor network health, security, and performance. AWS Network Manager reduces the operational complexity of managing global networks across AWS and on-premises locations. Previously, you could access AWS Cloud WAN and AWS Network Manager using public IPv4 endpoints only. With this launch, you can now access these services’ APIs/CLI privately, without going through the public Internet. Additionally, these services now support IPv6 endpoints.

To learn more about AWS Network Manager, refer documentation, and for AWS Cloud WAN, refer documentation.

 

​AWS Network Manager and AWS Cloud WAN now support AWS PrivateLink and IPv6 based connectivity to the management endpoint of these services. Using PrivateLink, customers can now access AWS Network Manager or AWS Cloud WAN privately on the AWS network, without going through the public Internet. Additionally, customers can now access these services over IPv6 using dual-stack endpoints. With AWS Cloud WAN, you can use a central dashboard and network policies to create a global network that spans multiple locations and networks, allowing you to configure and manage different networks using the same technology. The Cloud WAN central dashboard, powered by AWS Network Manager, generates a complete view of the network to help you monitor network health, security, and performance. AWS Network Manager reduces the operational complexity of managing global networks across AWS and on-premises locations. Previously, you could access AWS Cloud WAN and AWS Network Manager using public IPv4 endpoints only. With this launch, you can now access these services’ APIs/CLI privately, without going through the public Internet. Additionally, these services now support IPv6 endpoints. To learn more about AWS Network Manager, refer documentation, and for AWS Cloud WAN, refer documentation.  

Today, we are excited to announce a set of improvements to the seller management experience for Machine Learning (ML) products in AWS Marketplace. Sellers can now quickly publish and update ML listings with a new self-service experience in the AWS Marketplace Management Portal. Additionally, sellers can now create, view, and manage private offers for ML products through a guided step-by-step process in the AWS Marketplace Management Portal, making it easier for them to extend custom pricing terms to their customers. To help improve operational efficiency, sellers can now utilize AWS Marketplace Catalog APIs to automate creating and updating ML product listings and private offers.

These three features help streamline the process of listing and managing ML products in AWS Marketplace, offering a more seamless experience for ML sellers and enabling them to bring their innovative ML solutions to customers faster and more efficiently. With the new self-service experience, sellers can perform all listing creation and management actions and publish private offers in minutes, all without requiring help from AWS Marketplace support. With access to APIs, ML sellers can automate their listing creation and update processes by integrating directly with AWS Marketplace from within their model publishing pipelines.

To get started, visit the Machine Learning product page in the AWS Marketplace Management Portal. To learn more, access the AWS Marketplace Seller Guide.
 

 

​Today, we are excited to announce a set of improvements to the seller management experience for Machine Learning (ML) products in AWS Marketplace. Sellers can now quickly publish and update ML listings with a new self-service experience in the AWS Marketplace Management Portal. Additionally, sellers can now create, view, and manage private offers for ML products through a guided step-by-step process in the AWS Marketplace Management Portal, making it easier for them to extend custom pricing terms to their customers. To help improve operational efficiency, sellers can now utilize AWS Marketplace Catalog APIs to automate creating and updating ML product listings and private offers. These three features help streamline the process of listing and managing ML products in AWS Marketplace, offering a more seamless experience for ML sellers and enabling them to bring their innovative ML solutions to customers faster and more efficiently. With the new self-service experience, sellers can perform all listing creation and management actions and publish private offers in minutes, all without requiring help from AWS Marketplace support. With access to APIs, ML sellers can automate their listing creation and update processes by integrating directly with AWS Marketplace from within their model publishing pipelines. To get started, visit the Machine Learning product page in the AWS Marketplace Management Portal. To learn more, access the AWS Marketplace Seller Guide.