Amazon EC2 High Memory U7in-24TB instances (u7in-24tb.224xlarge) are now available in AWS Europe (Paris) region. U7i instances are part of the AWS 7th generation and are powered by custom fourth-generation Intel Xeon Scalable processors (Sapphire Rapids). U7in-24TB instances offer 24 TiB of DDR5 memory, enabling customers to scale transaction processing throughput in a fast-growing data environment. U7i instances offer up to 45% better price performance over existing U-1 instances.

U7in-24TB instances deliver 896 vCPUs and support up to 100 Gbps of Amazon EBS bandwidth for faster data loading and backups, 200 Gbps of network bandwidth, and ENA Express. U7i instances are ideal for customers running mission-critical in-memory databases like SAP HANA, Oracle, and SQL Server.

To learn more about U7i instances, visit the High Memory instances page.

 

​Amazon EC2 High Memory U7in-24TB instances (u7in-24tb.224xlarge) are now available in AWS Europe (Paris) region. U7i instances are part of the AWS 7th generation and are powered by custom fourth-generation Intel Xeon Scalable processors (Sapphire Rapids). U7in-24TB instances offer 24 TiB of DDR5 memory, enabling customers to scale transaction processing throughput in a fast-growing data environment. U7i instances offer up to 45% better price performance over existing U-1 instances.
U7in-24TB instances deliver 896 vCPUs and support up to 100 Gbps of Amazon EBS bandwidth for faster data loading and backups, 200 Gbps of network bandwidth, and ENA Express. U7i instances are ideal for customers running mission-critical in-memory databases like SAP HANA, Oracle, and SQL Server.
To learn more about U7i instances, visit the High Memory instances page.  

Amazon Redshift announces the general availability of two new RG instance sizes – rg.large and rg.12xlarge. These new sizes deliver the same Graviton-powered performance benefits as existing RG instances, including up to 2.4x faster query performance than previous-generation RA3 instances at 30% lower price per vCPU, giving you more flexibility to right-size your provisioned clusters for any workload.

rg.large and rg.12xlarge instance sizes are available on the current track (P202) only. Customers on the trailing track (P201) can continue to use rg.xlarge and rg.4xlarge. Existing RA3 clusters can migrate to RG instances using Snapshot and Restore, Elastic Resize, or Classic Resize. RG instances are available with flexible pricing options, including On-Demand, and 1-year and 3-year Reserved Instances with No Upfront payment.

The new rg.large and rg.12xlarge instance sizes are now available in the following AWS Regions: US East (N. Virginia), US East (Ohio), US West (Oregon), US West (N. California), Canada (Central), Mexico (Central), South America (São Paulo), Europe (Ireland), Europe (Frankfurt), Europe (London), Europe (Paris), Europe (Stockholm), Europe (Spain), Africa (Cape Town), Asia Pacific (Tokyo), Asia Pacific (Seoul), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Mumbai), Asia Pacific (Jakarta), Asia Pacific (Hong Kong), Asia Pacific (Osaka), Asia Pacific (Malaysia), Asia Pacific (Hyderabad), Asia Pacific (Taiwan), Asia Pacific (Thailand), and Asia Pacific (Melbourne).

To get started, refer to the following resources:

• Amazon Redshift node types
• RA3 to RG upgrade guide
• Amazon Redshift cluster versions
• Amazon Redshift pricing

 

​Amazon Redshift announces the general availability of two new RG instance sizes – rg.large and rg.12xlarge. These new sizes deliver the same Graviton-powered performance benefits as existing RG instances, including up to 2.4x faster query performance than previous-generation RA3 instances at 30% lower price per vCPU, giving you more flexibility to right-size your provisioned clusters for any workload. rg.large and rg.12xlarge instance sizes are available on the current track (P202) only. Customers on the trailing track (P201) can continue to use rg.xlarge and rg.4xlarge. Existing RA3 clusters can migrate to RG instances using Snapshot and Restore, Elastic Resize, or Classic Resize. RG instances are available with flexible pricing options, including On-Demand, and 1-year and 3-year Reserved Instances with No Upfront payment.
The new rg.large and rg.12xlarge instance sizes are now available in the following AWS Regions: US East (N. Virginia), US East (Ohio), US West (Oregon), US West (N. California), Canada (Central), Mexico (Central), South America (São Paulo), Europe (Ireland), Europe (Frankfurt), Europe (London), Europe (Paris), Europe (Stockholm), Europe (Spain), Africa (Cape Town), Asia Pacific (Tokyo), Asia Pacific (Seoul), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Mumbai), Asia Pacific (Jakarta), Asia Pacific (Hong Kong), Asia Pacific (Osaka), Asia Pacific (Malaysia), Asia Pacific (Hyderabad), Asia Pacific (Taiwan), Asia Pacific (Thailand), and Asia Pacific (Melbourne). To get started, refer to the following resources:

Amazon Redshift node types
RA3 to RG upgrade guide
Amazon Redshift cluster versions
Amazon Redshift pricing  

AWS Control Tower Account Factory for Terraform (AFT) can now automatically re-apply an account’s customizations when that account moves to a different Organizational Unit (OU). Previously, moving an enrolled account between OUs required manually triggering customization re-application, creating operational overhead and risk of configuration drift. With this capability, you can opt in to automatic re-application in your AFT deployment, so accounts stay consistent with their OU-specific configuration as soon as they’re moved.

To enable this capability, set aft_customization_triggers = [«account_move»] in your AFT configuration. The re-application workflow skips the bootstrap and provisioning phases, running only global and account-level customizations for faster execution. Individual accounts can be excluded from this behavior by setting account_skip_customization_triggers = «true», giving teams precise control over which accounts participate in automated re-application.

This release also includes additional improvements: support for custom Terraform Cloud and Enterprise workspace naming variables, tighter access controls on the AFT logging bucket, and improved scaling for large-scale AWS Enterprise Support enrollment. Organizations enforcing compliance or security baselines tied to OU membership will benefit most from these combined enhancements.

This capability is available today across all AWS regions where AWS Control Tower Account Factory for Terraform is offered. To learn more about enabling automatic customization re-application and upgrading to the latest AFT release, visit the AFT documentation and review the AFT release notes on GitHub.

 

​AWS Control Tower Account Factory for Terraform (AFT) can now automatically re-apply an account’s customizations when that account moves to a different Organizational Unit (OU). Previously, moving an enrolled account between OUs required manually triggering customization re-application, creating operational overhead and risk of configuration drift. With this capability, you can opt in to automatic re-application in your AFT deployment, so accounts stay consistent with their OU-specific configuration as soon as they’re moved.
To enable this capability, set aft_customization_triggers = [«account_move»] in your AFT configuration. The re-application workflow skips the bootstrap and provisioning phases, running only global and account-level customizations for faster execution. Individual accounts can be excluded from this behavior by setting account_skip_customization_triggers = «true», giving teams precise control over which accounts participate in automated re-application.
This release also includes additional improvements: support for custom Terraform Cloud and Enterprise workspace naming variables, tighter access controls on the AFT logging bucket, and improved scaling for large-scale AWS Enterprise Support enrollment. Organizations enforcing compliance or security baselines tied to OU membership will benefit most from these combined enhancements.
This capability is available today across all AWS regions where AWS Control Tower Account Factory for Terraform is offered. To learn more about enabling automatic customization re-application and upgrading to the latest AFT release, visit the AFT documentation and review the AFT release notes on GitHub.  

Amazon CloudWatch Logs Insights query language now supports 25 new commands and functions that expand your ability to query, transform, correlate, and analyze logs. Customers analyzing logs in CloudWatch Logs Insights often need to perform statistical aggregation, handle null values in time-series data, compare logs across time windows, detect outliers, and enrich events with lookup data.

With this launch, CloudWatch Logs Insights adds type conversion and encoding functions (hexToAscii, hexToDec, decToHex), date and time functions (parseDate, formatDate, queryStartTime, queryEndTime, queryTimeRange), string functions (messageSize), JSON inspection functions (jsonArraySize, jsonArrayContains), and a conditional validation function (isNumeric). It also introduces statistical commands (variance, topk, countFrequent), row-sequencing and null-handling commands (autoregress, accum, filldown, fillmissing), sessionization and time-comparison commands (sessionize, logcompare), a data analysis command (outlier), query-composition and join commands (where, appendcols), and a lookup enrichment command (cidrlookup).

These commands and functions are available today in all commercial AWS Regions. To learn more, see the Amazon CloudWatch Logs documentation.

 

​Amazon CloudWatch Logs Insights query language now supports 25 new commands and functions that expand your ability to query, transform, correlate, and analyze logs. Customers analyzing logs in CloudWatch Logs Insights often need to perform statistical aggregation, handle null values in time-series data, compare logs across time windows, detect outliers, and enrich events with lookup data. With this launch, CloudWatch Logs Insights adds type conversion and encoding functions (hexToAscii, hexToDec, decToHex), date and time functions (parseDate, formatDate, queryStartTime, queryEndTime, queryTimeRange), string functions (messageSize), JSON inspection functions (jsonArraySize, jsonArrayContains), and a conditional validation function (isNumeric). It also introduces statistical commands (variance, topk, countFrequent), row-sequencing and null-handling commands (autoregress, accum, filldown, fillmissing), sessionization and time-comparison commands (sessionize, logcompare), a data analysis command (outlier), query-composition and join commands (where, appendcols), and a lookup enrichment command (cidrlookup). These commands and functions are available today in all commercial AWS Regions. To learn more, see the Amazon CloudWatch Logs documentation.  

Starting today, Amazon Elastic Compute Cloud (Amazon EC2) G7e instances accelerated by NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs are now available in the AWS Europe (Frankfurt, Stockholm) and Asia Pacific (Mumbai) Regions. G7e instances offer up to 2.3x inference performance compared to G6e.

Customers can use G7e instances to deploy large language models (LLMs), agentic AI models, multimodal generative AI models, and physical AI models. G7e instances offer the highest performance for spatial computing workloads as well as workloads that require both graphics and AI processing capabilities. G7e instances feature up to 8 NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs, with 96 GB of memory per GPU, and 5th Generation Intel Xeon processors. They support up to 192 virtual CPUs (vCPUs) and up to 1600 Gbps of networking bandwidth. G7e instances support NVIDIA GPUDirect Peer to Peer (P2P) that boosts performance for multi-GPU workloads. Multi-GPU G7e instances also support NVIDIA GPUDirect Remote Direct Memory Access (RDMA) with EFA in EC2 UltraClusters, reducing latency for small-scale multi-node workloads.

You can use G7e instances for Amazon EC2 in the following AWS Regions: US West (Oregon), US East (N. Virginia, Ohio), Europe (Spain, London, Frankfurt, Stockholm) and Asia Pacific (Tokyo, Seoul, Mumbai). You can purchase G7e instances as On-Demand Instances, Spot Instances, or as part of Savings Plans.

To get started, visit the AWS Management Console, AWS Command Line Interface (CLI), and AWS SDKs. To learn more, visit G7e instances.

 

​Starting today, Amazon Elastic Compute Cloud (Amazon EC2) G7e instances accelerated by NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs are now available in the AWS Europe (Frankfurt, Stockholm) and Asia Pacific (Mumbai) Regions. G7e instances offer up to 2.3x inference performance compared to G6e.
Customers can use G7e instances to deploy large language models (LLMs), agentic AI models, multimodal generative AI models, and physical AI models. G7e instances offer the highest performance for spatial computing workloads as well as workloads that require both graphics and AI processing capabilities. G7e instances feature up to 8 NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs, with 96 GB of memory per GPU, and 5th Generation Intel Xeon processors. They support up to 192 virtual CPUs (vCPUs) and up to 1600 Gbps of networking bandwidth. G7e instances support NVIDIA GPUDirect Peer to Peer (P2P) that boosts performance for multi-GPU workloads. Multi-GPU G7e instances also support NVIDIA GPUDirect Remote Direct Memory Access (RDMA) with EFA in EC2 UltraClusters, reducing latency for small-scale multi-node workloads.
You can use G7e instances for Amazon EC2 in the following AWS Regions: US West (Oregon), US East (N. Virginia, Ohio), Europe (Spain, London, Frankfurt, Stockholm) and Asia Pacific (Tokyo, Seoul, Mumbai). You can purchase G7e instances as On-Demand Instances, Spot Instances, or as part of Savings Plans.
To get started, visit the AWS Management Console, AWS Command Line Interface (CLI), and AWS SDKs. To learn more, visit G7e instances.  

Amazon CloudWatch Logs now supports intelligent storage tiering, which automatically classifies your log data across three storage tiers – Standard (existing), Infrequent Access, and Archive Instant Access based on access patterns. This allows you to store logs in Amazon CloudWatch for extended periods at lower-cost tiers without any operational overhead.

With today’s launch, customers can now retain high-volume verbose logs needed to be stored for longer periods at a lower cost in Amazon CloudWatch. Instead of filtering these logs or exporting them, you can now keep them natively in Amazon CloudWatch and benefit from the same query experience regardless of which tier your data resides in. Amazon CloudWatch monitors access patterns and automatically reclassifies data not accessed for 30 days to the Infrequent Access tier, and data not accessed for 90 days to the Archive Instant Access tier. When you access older data, it is automatically promoted back to the Standard tier for 30 days. By consolidating all your logs in CloudWatch, you get full visibility in one tool, thereby eliminating the operational overhead of managing multiple storage solutions and reducing your Mean Time to Resolution (MTTR) by analyzing, and alerting on all your logs in a single place.

Amazon CloudWatch Logs Intelligent-Tiering is available in all AWS commercial regions except Middle East (Bahrain) and Middle East (UAE). You can enable intelligent tiering at the account level in the AWS Management Console, AWS SDKs or through AWS CLI. Learn more about CloudWatch Logs intelligent tiering pricing and documentation.

 

​Amazon CloudWatch Logs now supports intelligent storage tiering, which automatically classifies your log data across three storage tiers – Standard (existing), Infrequent Access, and Archive Instant Access based on access patterns. This allows you to store logs in Amazon CloudWatch for extended periods at lower-cost tiers without any operational overhead.
With today’s launch, customers can now retain high-volume verbose logs needed to be stored for longer periods at a lower cost in Amazon CloudWatch. Instead of filtering these logs or exporting them, you can now keep them natively in Amazon CloudWatch and benefit from the same query experience regardless of which tier your data resides in. Amazon CloudWatch monitors access patterns and automatically reclassifies data not accessed for 30 days to the Infrequent Access tier, and data not accessed for 90 days to the Archive Instant Access tier. When you access older data, it is automatically promoted back to the Standard tier for 30 days. By consolidating all your logs in CloudWatch, you get full visibility in one tool, thereby eliminating the operational overhead of managing multiple storage solutions and reducing your Mean Time to Resolution (MTTR) by analyzing, and alerting on all your logs in a single place.
Amazon CloudWatch Logs Intelligent-Tiering is available in all AWS commercial regions except Middle East (Bahrain) and Middle East (UAE). You can enable intelligent tiering at the account level in the AWS Management Console, AWS SDKs or through AWS CLI. Learn more about CloudWatch Logs intelligent tiering pricing and documentation.  

Amazon Cognito now supports importing users with password hashes in CSV user imports. Previously, users imported from a CSV file had to reset their passwords on first sign-in. Now, you can include password hashes in your CSV file so that imported users can sign in immediately with their existing credentials.

When creating a CSV import, you specify the password hashing algorithm used by your source system. Amazon Cognito imports these users and verifies their password against the imported hash on first sign-in. Supported algorithms include bcrypt, scrypt, Argon2id, and PBKDF2 with SHA-256. All imported hashes receive an additional layer of cryptographic protection before storage.

Password hash import is available in all AWS Regions where Amazon Cognito is available. To get started, create a user import using the AWS Management Console, AWS Command Line Interface (CLI), or AWS Software Development Kits (SDKs). See the developer guide for instructions.

 

​Amazon Cognito now supports importing users with password hashes in CSV user imports. Previously, users imported from a CSV file had to reset their passwords on first sign-in. Now, you can include password hashes in your CSV file so that imported users can sign in immediately with their existing credentials. When creating a CSV import, you specify the password hashing algorithm used by your source system. Amazon Cognito imports these users and verifies their password against the imported hash on first sign-in. Supported algorithms include bcrypt, scrypt, Argon2id, and PBKDF2 with SHA-256. All imported hashes receive an additional layer of cryptographic protection before storage. Password hash import is available in all AWS Regions where Amazon Cognito is available. To get started, create a user import using the AWS Management Console, AWS Command Line Interface (CLI), or AWS Software Development Kits (SDKs). See the developer guide for instructions.  

Amazon MQ now allows you to configure the EBS Disk storage size for RabbitMQ brokers independently of instance type. When creating or updating a broker, you can define a custom storage size, allowing you to right-size storage independently of your instance size to match your specific messaging workload requirements. Configurable storage is available for RabbitMQ M7g brokers on version 4.2 or later using cluster deployments only.

With configurable storage, you can choose a storage size from the default value on M7g to the maximum allowed value depending on your instance size in increments of 5 GB. You can specify the Storage Size using the using the AWS Console, AWS CloudFormation, AWS Command Line Interface (CLI), or the AWS Cloud Development Kit (CDK). Storage changes are applied during the next broker reboot. 

Standard Amazon MQ storage pricing applies based on the disk size as per Amazon MQ pricing. Configurable storage is available in all commercial AWS Regions where Amazon MQ for RabbitMQ is offered. To learn more, see the Amazon MQ Developer Guide.

 

​Amazon MQ now allows you to configure the EBS Disk storage size for RabbitMQ brokers independently of instance type. When creating or updating a broker, you can define a custom storage size, allowing you to right-size storage independently of your instance size to match your specific messaging workload requirements. Configurable storage is available for RabbitMQ M7g brokers on version 4.2 or later using cluster deployments only.
With configurable storage, you can choose a storage size from the default value on M7g to the maximum allowed value depending on your instance size in increments of 5 GB. You can specify the Storage Size using the using the AWS Console, AWS CloudFormation, AWS Command Line Interface (CLI), or the AWS Cloud Development Kit (CDK). Storage changes are applied during the next broker reboot. 
Standard Amazon MQ storage pricing applies based on the disk size as per Amazon MQ pricing. Configurable storage is available in all commercial AWS Regions where Amazon MQ for RabbitMQ is offered. To learn more, see the Amazon MQ Developer Guide.  

Amazon Managed Streaming for Apache Kafka (Amazon MSK) Express Brokers now supports Apache Kafka version 4.2. This release includes Eligible Leader Replicas (ELR) enhancements that strengthen availability with improved leader election correctness. It also introduces a new consumer rebalance protocol that helps ensure smoother and faster group rebalances, and a new Streams Rebalance Protocol that extends broker coordination capabilities to Kafka Streams for optimized task assignments. For a complete list of improvements and bug fixes, please refer to the Apache Kafka release notes for version 4.2.

MSK Express Brokers are designed to deliver up to three times more throughput per broker, scale up to 20 times faster, and reduce recovery time by 90 percent. This launch brings the latest open-source reliability and performance improvements to MSK Express.

To get started, simply select version 4.2.x when creating a new cluster with Express Brokers via the AWS Management Console, AWS CLI, or AWS SDKs. You can also upgrade existing MSK Express Brokers with an in-place rolling update. Amazon MSK orchestrates broker restarts to maintain availability and protect your data during the upgrade. Kafka version 4.2 support is available today across all AWS regions where Amazon MSK Express Brokers is offered. To learn how to get started, see the Amazon MSK Developer Guide.

 

​Amazon Managed Streaming for Apache Kafka (Amazon MSK) Express Brokers now supports Apache Kafka version 4.2. This release includes Eligible Leader Replicas (ELR) enhancements that strengthen availability with improved leader election correctness. It also introduces a new consumer rebalance protocol that helps ensure smoother and faster group rebalances, and a new Streams Rebalance Protocol that extends broker coordination capabilities to Kafka Streams for optimized task assignments. For a complete list of improvements and bug fixes, please refer to the Apache Kafka release notes for version 4.2. MSK Express Brokers are designed to deliver up to three times more throughput per broker, scale up to 20 times faster, and reduce recovery time by 90 percent. This launch brings the latest open-source reliability and performance improvements to MSK Express. To get started, simply select version 4.2.x when creating a new cluster with Express Brokers via the AWS Management Console, AWS CLI, or AWS SDKs. You can also upgrade existing MSK Express Brokers with an in-place rolling update. Amazon MSK orchestrates broker restarts to maintain availability and protect your data during the upgrade. Kafka version 4.2 support is available today across all AWS regions where Amazon MSK Express Brokers is offered. To learn how to get started, see the Amazon MSK Developer Guide.  

Amazon Relational Database Service (RDS) now supports R8gd database instances in 12 additional regions and and M8gd database instances in 6 additional Regions with Optimized Reads for Amazon Aurora PostgreSQL, RDS for PostgreSQL, RDS for MySQL, and RDS for MariaDB.

R8gd and M8gd 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 to store ephemeral data such as temporary tables, reducing network storage access and improving query latency. 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 also 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. R8gd instances are available in the following additional regions: Europe (Ireland), Asia Pacific (Seoul), Asia Pacific (Malaysia), Europe (London), US West (N. California), Asia Pacific (Sydney), Canada (Central), Asia Pacific (Jakarta), Africa (Cape Town), Canada West (Calgary), South America (Sao Paulo) and Asia Pacific (Hong Kong). M8gd instances are available in the following additional regions: Europe (Ireland), Asia Pacific (Malaysia), Europe (London), Asia Pacific (Sydney), South America (Sao Paulo) and Canada (Central). 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 database instances in 12 additional regions and and M8gd database instances in 6 additional Regions with Optimized Reads for Amazon Aurora PostgreSQL, RDS for PostgreSQL, RDS for MySQL, and RDS for MariaDB. R8gd and M8gd 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 to store ephemeral data such as temporary tables, reducing network storage access and improving query latency. 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 also 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. R8gd instances are available in the following additional regions: Europe (Ireland), Asia Pacific (Seoul), Asia Pacific (Malaysia), Europe (London), US West (N. California), Asia Pacific (Sydney), Canada (Central), Asia Pacific (Jakarta), Africa (Cape Town), Canada West (Calgary), South America (Sao Paulo) and Asia Pacific (Hong Kong). M8gd instances are available in the following additional regions: Europe (Ireland), Asia Pacific (Malaysia), Europe (London), Asia Pacific (Sydney), South America (Sao Paulo) and Canada (Central). 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.