AWS Step Functions expands its AWS SDK integrations with 28 additional services and over 1,100 new API actions across new and existing AWS services, including Amazon Bedrock AgentCore and Amazon S3 Vectors. This expansion enables you to orchestrate a broader set of AWS services directly from your workflows without writing integration code.

AWS Step Functions is a visual workflow service capable of orchestrating over 220 AWS services to help customers build distributed applications at scale. With the Amazon Bedrock AgentCore service integration, you can invoke AI agent runtimes with built-in retries, run multiple agents in parallel using Map states, and automate agent provisioning workflows that create, update, and tear down agent infrastructure as workflow steps. This expansion also includes Amazon S3 Vectors for automating document ingestion pipelines that populate knowledge bases for AI applications. It also adds support for AWS Lambda durable execution APIs, allowing you to pass an execution name for idempotent invocations of Lambda durable functions and manage durable executions directly from your workflows.

These enhancements are now generally available in all AWS Regions where AWS Step Functions is available. Specific services and API actions are subject to the availability of the target service in the AWS Region. To learn more about AWS Step Functions SDK integrations, visit the Developer Guide, or see the full list of supported services at AWS SDK service integrations.

 

​AWS Step Functions expands its AWS SDK integrations with 28 additional services and over 1,100 new API actions across new and existing AWS services, including Amazon Bedrock AgentCore and Amazon S3 Vectors. This expansion enables you to orchestrate a broader set of AWS services directly from your workflows without writing integration code. AWS Step Functions is a visual workflow service capable of orchestrating over 220 AWS services to help customers build distributed applications at scale. With the Amazon Bedrock AgentCore service integration, you can invoke AI agent runtimes with built-in retries, run multiple agents in parallel using Map states, and automate agent provisioning workflows that create, update, and tear down agent infrastructure as workflow steps. This expansion also includes Amazon S3 Vectors for automating document ingestion pipelines that populate knowledge bases for AI applications. It also adds support for AWS Lambda durable execution APIs, allowing you to pass an execution name for idempotent invocations of Lambda durable functions and manage durable executions directly from your workflows. These enhancements are now generally available in all AWS Regions where AWS Step Functions is available. Specific services and API actions are subject to the availability of the target service in the AWS Region. To learn more about AWS Step Functions SDK integrations, visit the Developer Guide, or see the full list of supported services at AWS SDK service integrations.  

Amazon Web Services (AWS) announces the availability of Amazon EC2 I8ge instances in Europe (Stockholm), Asia Pacific (Mumbai), Asia Pacific (Malaysia), Asia Pacific (Singapore), and Asia Pacific (Sydney) AWS regions. I8ge instances are powered by AWS Graviton4 processors to deliver up to 60% better compute performance compared to previous generation Graviton2-based storage optimized Amazon EC2 instances. I8ge instances use the third generation AWS Nitro SSDs, local NVMe storage that delivers up to 55% better real-time storage performance per TB. They offer up to 60% lower storage I/O latency and up to 75% lower storage I/O latency variability compared to previous generation Im4gn instances.

I8ge instances are storage-optimized instances offering up to 120TB of locally attached NVMe storage. They are ideal for workloads that demand rapid local storage with high random read/write performance and consistently low latency for accessing large datasets. These versatile instances are offered in eleven different sizes including two metal sizes, providing flexibility to match customers’ computational needs. They deliver up to 180 Gbps of network performance bandwidth and 60 Gbps of dedicated bandwidth for Amazon Elastic Block Store (EBS), ensuring fast and efficient data transfer for the most demanding applications.

To begin your Graviton journey, visit the Level up your compute with AWS Graviton page. To get started, see AWS Management Console, AWS Command Line Interface (AWS CLI), and AWS SDKs. To learn more, visit the I8ge instances page.

 

​Amazon Web Services (AWS) announces the availability of Amazon EC2 I8ge instances in Europe (Stockholm), Asia Pacific (Mumbai), Asia Pacific (Malaysia), Asia Pacific (Singapore), and Asia Pacific (Sydney) AWS regions. I8ge instances are powered by AWS Graviton4 processors to deliver up to 60% better compute performance compared to previous generation Graviton2-based storage optimized Amazon EC2 instances. I8ge instances use the third generation AWS Nitro SSDs, local NVMe storage that delivers up to 55% better real-time storage performance per TB. They offer up to 60% lower storage I/O latency and up to 75% lower storage I/O latency variability compared to previous generation Im4gn instances. I8ge instances are storage-optimized instances offering up to 120TB of locally attached NVMe storage. They are ideal for workloads that demand rapid local storage with high random read/write performance and consistently low latency for accessing large datasets. These versatile instances are offered in eleven different sizes including two metal sizes, providing flexibility to match customers’ computational needs. They deliver up to 180 Gbps of network performance bandwidth and 60 Gbps of dedicated bandwidth for Amazon Elastic Block Store (EBS), ensuring fast and efficient data transfer for the most demanding applications. To begin your Graviton journey, visit the Level up your compute with AWS Graviton page. To get started, see AWS Management Console, AWS Command Line Interface (AWS CLI), and AWS SDKs. To learn more, visit the I8ge instances page.  

AWS HealthImaging now supports fine-grained access control, enabling organizations to securely manage access to medical imaging data at the DICOM study and series levels. Medical imaging workflows are typically organized around DICOM studies, which are stored in AWS HealthImaging as one or more image set resources. Now customers can easily grant users access to all image sets for a set of DICOM Studies or Series with easy-to-maintain IAM policies.

Customers can now grant permissions for DICOMweb APIs using DICOM Study Instance UIDs and Series Instance UIDs directly in their IAM policies, eliminating the need to list individual image set ARNs. Customers can now create dynamic, temporary access grants using AWS Security Token Service (STS) session policies with low-latency authentication. This capability provides enhanced protection for Protected Health Information (PHI) by scoping access grants to specific Studies or Series rather than entire data stores. This launch better supports use cases such as pathologist case-level access, radiology study sharing with external partners, and controlled research data distribution. To learn more, see the AWS HealthImaging Developer Guide.

AWS HealthImaging is a HIPAA-eligible service that empowers healthcare providers, life sciences organizations, and their software partners to store, analyze, and share medical images. AWS HealthImaging is generally available in the following AWS Regions: US East (N. Virginia), US West (Oregon), Asia Pacific (Sydney), Europe (Ireland), and Europe (London). 

 

​AWS HealthImaging now supports fine-grained access control, enabling organizations to securely manage access to medical imaging data at the DICOM study and series levels. Medical imaging workflows are typically organized around DICOM studies, which are stored in AWS HealthImaging as one or more image set resources. Now customers can easily grant users access to all image sets for a set of DICOM Studies or Series with easy-to-maintain IAM policies.
Customers can now grant permissions for DICOMweb APIs using DICOM Study Instance UIDs and Series Instance UIDs directly in their IAM policies, eliminating the need to list individual image set ARNs. Customers can now create dynamic, temporary access grants using AWS Security Token Service (STS) session policies with low-latency authentication. This capability provides enhanced protection for Protected Health Information (PHI) by scoping access grants to specific Studies or Series rather than entire data stores. This launch better supports use cases such as pathologist case-level access, radiology study sharing with external partners, and controlled research data distribution. To learn more, see the AWS HealthImaging Developer Guide.
AWS HealthImaging is a HIPAA-eligible service that empowers healthcare providers, life sciences organizations, and their software partners to store, analyze, and share medical images. AWS HealthImaging is generally available in the following AWS Regions: US East (N. Virginia), US West (Oregon), Asia Pacific (Sydney), Europe (Ireland), and Europe (London).   

Amazon EC2 High Memory U7i-8TB instances (u7i-8tb.112xlarge) and U7i-12TB instances (u7i-12tb.224xlarge) are now available in AWS Europe (Milan). U7i instances are part of AWS 7th generation and are powered by custom fourth generation Intel Xeon Scalable Processors (Sapphire Rapids). U7i-8tb instances offer 8TiB of DDR5 memory, and U7i-12tb instances offer 12TiB of DDR5 memory, enabling customers to scale transaction processing throughput in a fast-growing data environment.

U7i-8tb instances deliver 448 vCPUs; U7i-12tb instances deliver 896 vCPUs. Both instances support up to 100 Gbps of Amazon EBS bandwidth for faster data loading and backups, 100 Gbps of network bandwidth, and ENA Express. U7i instances are ideal for customers using 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 U7i-8TB instances (u7i-8tb.112xlarge) and U7i-12TB instances (u7i-12tb.224xlarge) are now available in AWS Europe (Milan). U7i instances are part of AWS 7th generation and are powered by custom fourth generation Intel Xeon Scalable Processors (Sapphire Rapids). U7i-8tb instances offer 8TiB of DDR5 memory, and U7i-12tb instances offer 12TiB of DDR5 memory, enabling customers to scale transaction processing throughput in a fast-growing data environment.
U7i-8tb instances deliver 448 vCPUs; U7i-12tb instances deliver 896 vCPUs. Both instances support up to 100 Gbps of Amazon EBS bandwidth for faster data loading and backups, 100 Gbps of network bandwidth, and ENA Express. U7i instances are ideal for customers using mission-critical in-memory databases like SAP HANA, Oracle, and SQL Server.
To learn more about U7i instances, visit the High Memory instances page.  

Starting today, customers can deploy their Graviton-based and GPU-accelerated workloads on Amazon Elastic Container Service (Amazon ECS) Managed Instances in a Federal Information Processing Standard (FIPS) compliant mode in the AWS GovCloud (US) Regions. FIPS is a U.S. and Canadian government standard that specifies the security requirements for cryptographic modules that protect sensitive information.

In the AWS GovCloud (US) Regions, Amazon ECS Managed Instances automatically enable FIPS compliance by default. ECS Managed Instances communicate through FIPS-compliant endpoints, use appropriately configured cryptographic modules, and boot the underlying kernel in FIPS mode. Customers with federal compliance requirements can run workloads with FIPS-validated cryptographic modules across a broad range of instance types, including Graviton-based, GPU-accelerated, network-optimized, and burstable performance instances.

To learn more about FIPS, refer to FIPS on AWS and AWS Fargate Federal Information Processing Standard (FIPS-140). To get started with ECS Managed Instances, use the AWS Console, Amazon ECS MCP Server, ECS Express Mode, or your favorite infrastructure-as-code tooling to enable it in a new or existing Amazon ECS cluster. You will be charged for the management of compute provisioned, in addition to your regular Amazon EC2 costs. To learn more about ECS Managed Instances, visit the feature page, documentation, and AWS News launch blog.

 

​Starting today, customers can deploy their Graviton-based and GPU-accelerated workloads on Amazon Elastic Container Service (Amazon ECS) Managed Instances in a Federal Information Processing Standard (FIPS) compliant mode in the AWS GovCloud (US) Regions. FIPS is a U.S. and Canadian government standard that specifies the security requirements for cryptographic modules that protect sensitive information.
In the AWS GovCloud (US) Regions, Amazon ECS Managed Instances automatically enable FIPS compliance by default. ECS Managed Instances communicate through FIPS-compliant endpoints, use appropriately configured cryptographic modules, and boot the underlying kernel in FIPS mode. Customers with federal compliance requirements can run workloads with FIPS-validated cryptographic modules across a broad range of instance types, including Graviton-based, GPU-accelerated, network-optimized, and burstable performance instances.
To learn more about FIPS, refer to FIPS on AWS and AWS Fargate Federal Information Processing Standard (FIPS-140). To get started with ECS Managed Instances, use the AWS Console, Amazon ECS MCP Server, ECS Express Mode, or your favorite infrastructure-as-code tooling to enable it in a new or existing Amazon ECS cluster. You will be charged for the management of compute provisioned, in addition to your regular Amazon EC2 costs. To learn more about ECS Managed Instances, visit the feature page, documentation, and AWS News launch blog.  

Today, AWS announces the ability to remotely connect from Kiro and Cursor IDEs to Amazon SageMaker Studio. This new capability allows data scientists, ML engineers, and developers to leverage their Kiro and Cursor setup – including its spec-driven development, conversational coding, and automated feature generation capabilities – while accessing the scalable compute resources of Amazon SageMaker Studio. By connecting Kiro and Cursor to SageMaker Studio using the AWS Toolkit extension, you can eliminate context switching between your local IDE and cloud infrastructure, maintaining your existing agentic development workflows within a single environment for all your AWS analytics and AI/ML services.

SageMaker Studio, offers a broad set of fully managed cloud interactive development environments (IDE), including JupyterLab and Code Editor based on Code-OSS (Open-Source Software), and VS Code IDE as remote IDE. Starting today, you can also use your customized local Kiro and Cursor setup – complete with specs, steering files, and hooks – while accessing your compute resources and data on Amazon SageMaker. You can authenticate using the AWS Toolkit extension in Kiro or Cursor or through SageMaker Studio’s web interface. Once authenticated, connect to any of your SageMaker Studio development environments in a few simple clicks. You maintain the same security boundaries as SageMaker Studio’s web-based environments while developing AI models and analyzing data in local IDE of your choice – Kiro or Cursor.

To learn more, refer to the SageMaker user guide.

 

​Today, AWS announces the ability to remotely connect from Kiro and Cursor IDEs to Amazon SageMaker Studio. This new capability allows data scientists, ML engineers, and developers to leverage their Kiro and Cursor setup – including its spec-driven development, conversational coding, and automated feature generation capabilities – while accessing the scalable compute resources of Amazon SageMaker Studio. By connecting Kiro and Cursor to SageMaker Studio using the AWS Toolkit extension, you can eliminate context switching between your local IDE and cloud infrastructure, maintaining your existing agentic development workflows within a single environment for all your AWS analytics and AI/ML services. SageMaker Studio, offers a broad set of fully managed cloud interactive development environments (IDE), including JupyterLab and Code Editor based on Code-OSS (Open-Source Software), and VS Code IDE as remote IDE. Starting today, you can also use your customized local Kiro and Cursor setup – complete with specs, steering files, and hooks – while accessing your compute resources and data on Amazon SageMaker. You can authenticate using the AWS Toolkit extension in Kiro or Cursor or through SageMaker Studio’s web interface. Once authenticated, connect to any of your SageMaker Studio development environments in a few simple clicks. You maintain the same security boundaries as SageMaker Studio’s web-based environments while developing AI models and analyzing data in local IDE of your choice – Kiro or Cursor. To learn more, refer to the SageMaker user guide.  

AWS Lambda increases the file descriptor limit from 1,024 to 4,096, a 4x increase, for functions running on Lambda Managed Instances (LMI). This capability enables customers to run I/O intensive workloads such as high-concurrency web services, and file-heavy data processing pipelines, without running into file descriptor limits. LMI enables you to run Lambda functions on managed Amazon EC2 instances with built-in routing, load-balancing, and auto-scaling, giving you access to specialized compute configurations including the latest-generation processors and high-bandwidth networking, with no operational overhead.

Customers use Lambda functions to build a wide range of serverless applications such as event-driven workloads, web applications, and AI-driven workflows. These applications rely on file descriptors for operations such as opening files, establishing network socket connections to external services and databases, and managing concurrent I/O streams for data processing. Each open file, network socket, or internal resource consumes one file descriptor. Today, Lambda supports a maximum of 1,024 file descriptors. However, LMI allows multiple requests to be processed simultaneously, which often requires higher number of file descriptors. With this launch, AWS Lambda is increasing the file descriptor limit to 4,096, allowing customers to run I/O intensive workloads, maintain larger connection pools, and effectively utilize multi-concurrency for functions running on LMI.

This feature is available in all AWS Regions where AWS Lambda Managed Instances is generally available. To get started, visit the AWS Lambda Managed Instances documentation.

 

​AWS Lambda increases the file descriptor limit from 1,024 to 4,096, a 4x increase, for functions running on Lambda Managed Instances (LMI). This capability enables customers to run I/O intensive workloads such as high-concurrency web services, and file-heavy data processing pipelines, without running into file descriptor limits. LMI enables you to run Lambda functions on managed Amazon EC2 instances with built-in routing, load-balancing, and auto-scaling, giving you access to specialized compute configurations including the latest-generation processors and high-bandwidth networking, with no operational overhead. Customers use Lambda functions to build a wide range of serverless applications such as event-driven workloads, web applications, and AI-driven workflows. These applications rely on file descriptors for operations such as opening files, establishing network socket connections to external services and databases, and managing concurrent I/O streams for data processing. Each open file, network socket, or internal resource consumes one file descriptor. Today, Lambda supports a maximum of 1,024 file descriptors. However, LMI allows multiple requests to be processed simultaneously, which often requires higher number of file descriptors. With this launch, AWS Lambda is increasing the file descriptor limit to 4,096, allowing customers to run I/O intensive workloads, maintain larger connection pools, and effectively utilize multi-concurrency for functions running on LMI. This feature is available in all AWS Regions where AWS Lambda Managed Instances is generally available. To get started, visit the AWS Lambda Managed Instances documentation.  

The AWS Advanced JDBC Wrapper now supports automatically caching JDBC queries with Valkey, including Amazon ElastiCache for Valkey caches. Previously, developers who needed to cache JDBC query result sets had to manually write code to store and retrieve data from the cache for each query. Now you can automatically cache result sets from your Aurora and RDS PostgreSQL, MySQL, and MariaDB databases in just a few short steps. Simply add the wrapper dependency, enable the query cache plugin, configure database and cache endpoints, and indicate which queries to cache in your application code.

With this capability, you can store and retrieve query results directly from ElastiCache for Valkey, reducing the number of database reads and lowering read latency for frequently accessed data. Automated query caching can improve performance, lower costs, and increase application resilience by reducing database resource requirements. The AWS Advanced JDBC Wrapper supports annotating queries for caching using popular persistence APIs and frameworks including Hibernate and Spring Data, as well as manual query hinting.

JDBC query caching with the AWS Advanced JDBC Wrapper works seamlessly with Amazon ElastiCache for Valkey. You can create a new Amazon ElastiCache for Valkey serverless cache with the AWS Management Console, Software Development Kit (SDK), Command Line Interface (CLI), or Model Context Protocol (MCP) server. For more information, see the Advanced JDBC Wrapper and Amazon ElastiCache for Valkey documentation.

 

​The AWS Advanced JDBC Wrapper now supports automatically caching JDBC queries with Valkey, including Amazon ElastiCache for Valkey caches. Previously, developers who needed to cache JDBC query result sets had to manually write code to store and retrieve data from the cache for each query. Now you can automatically cache result sets from your Aurora and RDS PostgreSQL, MySQL, and MariaDB databases in just a few short steps. Simply add the wrapper dependency, enable the query cache plugin, configure database and cache endpoints, and indicate which queries to cache in your application code. With this capability, you can store and retrieve query results directly from ElastiCache for Valkey, reducing the number of database reads and lowering read latency for frequently accessed data. Automated query caching can improve performance, lower costs, and increase application resilience by reducing database resource requirements. The AWS Advanced JDBC Wrapper supports annotating queries for caching using popular persistence APIs and frameworks including Hibernate and Spring Data, as well as manual query hinting. JDBC query caching with the AWS Advanced JDBC Wrapper works seamlessly with Amazon ElastiCache for Valkey. You can create a new Amazon ElastiCache for Valkey serverless cache with the AWS Management Console, Software Development Kit (SDK), Command Line Interface (CLI), or Model Context Protocol (MCP) server. For more information, see the Advanced JDBC Wrapper and Amazon ElastiCache for Valkey documentation.  

Starting today, the general-purpose Amazon EC2 M8a instances are available in AWS Europe (Ireland) region. M8a instances are powered by 5th Gen AMD EPYC processors (formerly code named Turin) with a maximum frequency of 4.5 GHz, deliver up to 30% higher performance, and up to 19% better price-performance compared to M7a instances.

M8a instances deliver 45% more memory bandwidth compared to M7a instances, making these instances ideal for even latency sensitive workloads. M8a instances deliver even higher performance gains for specific workloads. M8a instances are up to 60% faster for GroovyJVM benchmark, and up to 39% faster for Cassandra benchmark compared to Amazon EC2 M7a instances. M8a instances are SAP-certified and offer 12 sizes including 2 bare metal sizes. This range of instance sizes allows customers to precisely match their workload requirements.

M8a instances are built using the latest sixth generation AWS Nitro Cards and ideal for applications that benefit from high performance and high throughput such as financial applications, gaming, rendering, application servers, simulation modeling, mid-size data stores, application development environments, and caching fleets.

To get started, sign in to the AWS Management Console. Customers can purchase these instances via Savings Plans, On-Demand instances, and Spot instances. For more information visit the Amazon EC2 M8a instance page.

 

​Starting today, the general-purpose Amazon EC2 M8a instances are available in AWS Europe (Ireland) region. M8a instances are powered by 5th Gen AMD EPYC processors (formerly code named Turin) with a maximum frequency of 4.5 GHz, deliver up to 30% higher performance, and up to 19% better price-performance compared to M7a instances. M8a instances deliver 45% more memory bandwidth compared to M7a instances, making these instances ideal for even latency sensitive workloads. M8a instances deliver even higher performance gains for specific workloads. M8a instances are up to 60% faster for GroovyJVM benchmark, and up to 39% faster for Cassandra benchmark compared to Amazon EC2 M7a instances. M8a instances are SAP-certified and offer 12 sizes including 2 bare metal sizes. This range of instance sizes allows customers to precisely match their workload requirements. M8a instances are built using the latest sixth generation AWS Nitro Cards and ideal for applications that benefit from high performance and high throughput such as financial applications, gaming, rendering, application servers, simulation modeling, mid-size data stores, application development environments, and caching fleets. To get started, sign in to the AWS Management Console. Customers can purchase these instances via Savings Plans, On-Demand instances, and Spot instances. For more information visit the Amazon EC2 M8a instance page.  

Starting today, the general-purpose Amazon EC2 M8a instances are available in AWS GovCloud (US-West) region. M8a instances are powered by 5th Gen AMD EPYC processors (formerly code named Turin) with a maximum frequency of 4.5 GHz, deliver up to 30% higher performance, and up to 19% better price-performance compared to M7a instances.

M8a instances deliver 45% more memory bandwidth compared to M7a instances, making these instances ideal for even latency sensitive workloads. M8a instances deliver even higher performance gains for specific workloads. M8a instances are up to 60% faster for GroovyJVM benchmark, and up to 39% faster for Cassandra benchmark compared to Amazon EC2 M7a instances. M8a instances are SAP-certified and offer 12 sizes including 2 bare metal sizes. This range of instance sizes allows customers to precisely match their workload requirements.

M8a instances are built using the latest sixth generation AWS Nitro Cards and ideal for applications that benefit from high performance and high throughput such as financial applications, gaming, rendering, application servers, simulation modeling, mid-size data stores, application development environments, and caching fleets.

To get started, sign in to the AWS Management Console. Customers can purchase these instances via Savings Plans, On-Demand instances, and Spot instances. For more information visit the Amazon EC2 M8a instance page.

 

​Starting today, the general-purpose Amazon EC2 M8a instances are available in AWS GovCloud (US-West) region. M8a instances are powered by 5th Gen AMD EPYC processors (formerly code named Turin) with a maximum frequency of 4.5 GHz, deliver up to 30% higher performance, and up to 19% better price-performance compared to M7a instances. M8a instances deliver 45% more memory bandwidth compared to M7a instances, making these instances ideal for even latency sensitive workloads. M8a instances deliver even higher performance gains for specific workloads. M8a instances are up to 60% faster for GroovyJVM benchmark, and up to 39% faster for Cassandra benchmark compared to Amazon EC2 M7a instances. M8a instances are SAP-certified and offer 12 sizes including 2 bare metal sizes. This range of instance sizes allows customers to precisely match their workload requirements. M8a instances are built using the latest sixth generation AWS Nitro Cards and ideal for applications that benefit from high performance and high throughput such as financial applications, gaming, rendering, application servers, simulation modeling, mid-size data stores, application development environments, and caching fleets. To get started, sign in to the AWS Management Console. Customers can purchase these instances via Savings Plans, On-Demand instances, and Spot instances. For more information visit the Amazon EC2 M8a instance page.