AWS Deadline Cloud now supports persistent storage for Service-Managed Fleets (SMF), allowing you to maintain data across worker lifecycle events. AWS Deadline Cloud is a fully managed service that makes it easy for teams to run compute-intensive workloads in the cloud for visual effects, animation, product design, simulation, and gaming.

Previously, Deadline Cloud SMF workers relied only on ephemeral storage, requiring software and assets to be reinstalled each time a worker was recycled or replaced. Now, Deadline Cloud attaches persistent Amazon Elastic Block Store (Amazon EBS) volumes to SMF workers, preserving Conda environments, Perforce workspaces, shader caches, and asset collections across worker lifecycle events. This reduces worker startup time and helps you complete jobs faster. You can configure the number of persistent volumes per worker and set a time-to-live (TTL) to control how long volumes are retained, giving you flexibility to balance storage costs with startup performance. 

Persistent storage for SMF is available in all AWS Regions where Deadline Cloud is offered. Persistent volumes are priced the same as existing Service-Managed Fleets EBS pricing. See the Deadline Cloud pricing page for details. To learn more, visit the AWS Deadline Cloud product page or our user guide.

 

​AWS Deadline Cloud now supports persistent storage for Service-Managed Fleets (SMF), allowing you to maintain data across worker lifecycle events. AWS Deadline Cloud is a fully managed service that makes it easy for teams to run compute-intensive workloads in the cloud for visual effects, animation, product design, simulation, and gaming. Previously, Deadline Cloud SMF workers relied only on ephemeral storage, requiring software and assets to be reinstalled each time a worker was recycled or replaced. Now, Deadline Cloud attaches persistent Amazon Elastic Block Store (Amazon EBS) volumes to SMF workers, preserving Conda environments, Perforce workspaces, shader caches, and asset collections across worker lifecycle events. This reduces worker startup time and helps you complete jobs faster. You can configure the number of persistent volumes per worker and set a time-to-live (TTL) to control how long volumes are retained, giving you flexibility to balance storage costs with startup performance.  Persistent storage for SMF is available in all AWS Regions where Deadline Cloud is offered. Persistent volumes are priced the same as existing Service-Managed Fleets EBS pricing. See the Deadline Cloud pricing page for details. To learn more, visit the AWS Deadline Cloud product page or our user guide.  

Amazon SageMaker Studio quick setup now completes in under twenty seconds, reduced from over two minutes. Whether you are building ML pipelines, exploring data, developing with notebooks, or fine-tuning foundation models, you can go from sign-in to a fully configured Studio environment almost instantly.

As part of this streamlined setup, newly created Studio environments now come with serverless model customization permissions automatically configured. A new managed policy, AmazonSageMakerModelCustomizationCoreAccess, is created and attached for you, providing permissions for serverless model customization jobs including fine-tuning with custom reward functions for reinforcement learning, model evaluation, and deployment to SageMaker or Bedrock endpoints. This eliminates the need to manually create and configure IAM roles and policies before you can start experimenting. For existing Studio environments, actionable messages with direct links to documentation guide you through adding these permissions.

This feature is available in all AWS Commercial Regions where Amazon SageMaker Studio is supported. To get started, create a new Studio environment using quick setup in the SageMaker AI Console. To learn more, see Quick setup and Model Customization permissions setup in the Amazon SageMaker documentation.

 

​Amazon SageMaker Studio quick setup now completes in under twenty seconds, reduced from over two minutes. Whether you are building ML pipelines, exploring data, developing with notebooks, or fine-tuning foundation models, you can go from sign-in to a fully configured Studio environment almost instantly. As part of this streamlined setup, newly created Studio environments now come with serverless model customization permissions automatically configured. A new managed policy, AmazonSageMakerModelCustomizationCoreAccess, is created and attached for you, providing permissions for serverless model customization jobs including fine-tuning with custom reward functions for reinforcement learning, model evaluation, and deployment to SageMaker or Bedrock endpoints. This eliminates the need to manually create and configure IAM roles and policies before you can start experimenting. For existing Studio environments, actionable messages with direct links to documentation guide you through adding these permissions. This feature is available in all AWS Commercial Regions where Amazon SageMaker Studio is supported. To get started, create a new Studio environment using quick setup in the SageMaker AI Console. To learn more, see Quick setup and Model Customization permissions setup in the Amazon SageMaker documentation.  

Today, AWS announces durability support for Amazon ElastiCache. Durability enables you to use ElastiCache for workloads that require microsecond read latency but cannot tolerate data loss. With durability support, ElastiCache now stores data durably across multiple Availability Zones (AZs) using a Multi-AZ transactional log to enable fast failover, database recovery, and node restarts to prevent data loss in the unlikely event of a failure.

You can choose between two durability options: synchronous and asynchronous writes. Synchronous writes persist data across at least two AZs before responding to the client, designed for zero data loss at single-digit millisecond write latency. Asynchronous writes persist data after responding to the client, maintaining microsecond write latency at no additional cost. However, up to 10 seconds of uncommitted data could be lost in the rare event of a failure. Both options maintain microsecond read latency. You can now use ElastiCache for a broader set of use cases beyond caching where data loss is unacceptable such as AI agent long-term memory, AI agent workflow state, knowledge bases for RAG applications, payment tokenization, and real-time inventory management.

Durability for ElastiCache is available in all AWS commercial Regions, AWS China Regions, and AWS GovCloud (US) Regions starting with Valkey 9.0. To get started, create a new ElastiCache cluster and select your preferred durability option using the AWS Management Console, AWS Software Development Kit (SDK), or AWS Command Line Interface (CLI). For pricing details, visit the Amazon ElastiCache pricing page. To learn more, visit the ElastiCache documentation and blog.  

 

​Today, AWS announces durability support for Amazon ElastiCache. Durability enables you to use ElastiCache for workloads that require microsecond read latency but cannot tolerate data loss. With durability support, ElastiCache now stores data durably across multiple Availability Zones (AZs) using a Multi-AZ transactional log to enable fast failover, database recovery, and node restarts to prevent data loss in the unlikely event of a failure.
You can choose between two durability options: synchronous and asynchronous writes. Synchronous writes persist data across at least two AZs before responding to the client, designed for zero data loss at single-digit millisecond write latency. Asynchronous writes persist data after responding to the client, maintaining microsecond write latency at no additional cost. However, up to 10 seconds of uncommitted data could be lost in the rare event of a failure. Both options maintain microsecond read latency. You can now use ElastiCache for a broader set of use cases beyond caching where data loss is unacceptable such as AI agent long-term memory, AI agent workflow state, knowledge bases for RAG applications, payment tokenization, and real-time inventory management.
Durability for ElastiCache is available in all AWS commercial Regions, AWS China Regions, and AWS GovCloud (US) Regions starting with Valkey 9.0. To get started, create a new ElastiCache cluster and select your preferred durability option using the AWS Management Console, AWS Software Development Kit (SDK), or AWS Command Line Interface (CLI). For pricing details, visit the Amazon ElastiCache pricing page. To learn more, visit the ElastiCache documentation and blog.    

AWS today announced that AWS Cost and Usage Report 2.0 (CUR 2.0) provides new integration options with AWS Athena and AWS Redshift. This capability allows customers to analyze the data from their AWS CUR 2.0 in Amazon Simple Storage Service (Amazon S3) using standard SQL without building custom data warehouse solutions, bringing feature parity with CUR 1.0 integration options.

With this launch, when customers select Athena or Redshift integration, CUR 2.0 exports are automatically delivered in the optimal format (Parquet, GZIP) for the chosen query engine. Each export includes the supporting metadata and automation resources needed to get started quickly, such as infrastructure templates, table definitions, and data loading instructions, so customers can begin querying their cost data without manual configuration. As CUR 2.0 data refreshes periodically, updates are automatically reflected in the Athena or Redshift tables with no additional ETL required.

This feature is available in all commercial AWS Regions, except the AWS GovCloud (US) Regions and the China Regions.

To learn more about this feature, see AWS Data Exports and AWS Billing and Cost Management in the AWS Cost Management User Guide.

 

​AWS today announced that AWS Cost and Usage Report 2.0 (CUR 2.0) provides new integration options with AWS Athena and AWS Redshift. This capability allows customers to analyze the data from their AWS CUR 2.0 in Amazon Simple Storage Service (Amazon S3) using standard SQL without building custom data warehouse solutions, bringing feature parity with CUR 1.0 integration options. With this launch, when customers select Athena or Redshift integration, CUR 2.0 exports are automatically delivered in the optimal format (Parquet, GZIP) for the chosen query engine. Each export includes the supporting metadata and automation resources needed to get started quickly, such as infrastructure templates, table definitions, and data loading instructions, so customers can begin querying their cost data without manual configuration. As CUR 2.0 data refreshes periodically, updates are automatically reflected in the Athena or Redshift tables with no additional ETL required. This feature is available in all commercial AWS Regions, except the AWS GovCloud (US) Regions and the China Regions. To learn more about this feature, see AWS Data Exports and AWS Billing and Cost Management in the AWS Cost Management User Guide.  

Amazon Relational Database Service (Amazon RDS) for SQL Server launches Bring Your Own Media (BYOM) for Microsoft SQL Server. With BYOM, customers who migrate SQL Server applications from on-premises environments can adopt a managed database service on AWS and reuse their existing Microsoft SQL Server licenses, including Software Assurance, through Microsoft’s License Mobility program.

Amazon RDS provides a managed SQL Server database service that lowers operating costs with features such as high availability, automated backups and monitoring. BYOM helps customers who currently run Microsoft SQL Server on-premises, on other clouds, or as self-managed SQL Server on Amazon EC2, and want to adopt Amazon RDS and reuse their existing Microsoft SQL Server licenses. They no longer have to incur the cost of additional Microsoft SQL Server licenses, or wait for existing license agreements to expire to adopt RDS. Amazon RDS for SQL Server BYOM is integrated with AWS License Manager so customers can track their Microsoft SQL Server license usage across their AWS environment for licensing compliance.

To learn more about how to set up RDS SQL Server database instances with BYOM, visit the Amazon RDS SQL Server User Guide. For BYOM pricing and regional availability, visit the Amazon RDS for SQL Server pricing page. 

 

​Amazon Relational Database Service (Amazon RDS) for SQL Server launches Bring Your Own Media (BYOM) for Microsoft SQL Server. With BYOM, customers who migrate SQL Server applications from on-premises environments can adopt a managed database service on AWS and reuse their existing Microsoft SQL Server licenses, including Software Assurance, through Microsoft’s License Mobility program. Amazon RDS provides a managed SQL Server database service that lowers operating costs with features such as high availability, automated backups and monitoring. BYOM helps customers who currently run Microsoft SQL Server on-premises, on other clouds, or as self-managed SQL Server on Amazon EC2, and want to adopt Amazon RDS and reuse their existing Microsoft SQL Server licenses. They no longer have to incur the cost of additional Microsoft SQL Server licenses, or wait for existing license agreements to expire to adopt RDS. Amazon RDS for SQL Server BYOM is integrated with AWS License Manager so customers can track their Microsoft SQL Server license usage across their AWS environment for licensing compliance. To learn more about how to set up RDS SQL Server database instances with BYOM, visit the Amazon RDS SQL Server User Guide. For BYOM pricing and regional availability, visit the Amazon RDS for SQL Server pricing page.   

AWS HealthOmics now allows customers to specify the Nextflow engine version at run time via the StartRun API, enabling customers to pin runs to a specific Nextflow version for controlled migration. With this launch, customers can select from supported Nextflow versions (22.04, 23.10, 24.10, 25.10, 26.04) through the new engine-settings parameter, giving explicit control at the point of execution. AWS HealthOmics is a HIPAA-eligible service that helps healthcare and life sciences customers accelerate scientific breakthroughs at scale with fully managed bioinformatics workflows.

Nextflow version pinning gives customers full control over when and how they adopt new engine versions. The run-time version override ensures that even when a workflow definition specifies a version via manifest.nextflowVersion in its config or profile, the StartRun API parameter takes precedence, enabling customers to test the same workflow across multiple engine versions without modifying workflow source code. Production workflows can remain on a validated engine version while development teams test newer versions in parallel, reducing the risk of unexpected behavior changes. This is valuable for regulated environments where pipeline validation is required before upgrading to a new engine version.

Nextflow version pinning at run time is now available for Nextflow workflow runs in all AWS HealthOmics regions: US East (N. Virginia), US West (Oregon), Europe (Frankfurt, Ireland, London), Israel (Tel Aviv), and Asia Pacific (Singapore, Seoul). To learn more, visit the Nextflow engine settings documentation.

 

​AWS HealthOmics now allows customers to specify the Nextflow engine version at run time via the StartRun API, enabling customers to pin runs to a specific Nextflow version for controlled migration. With this launch, customers can select from supported Nextflow versions (22.04, 23.10, 24.10, 25.10, 26.04) through the new engine-settings parameter, giving explicit control at the point of execution. AWS HealthOmics is a HIPAA-eligible service that helps healthcare and life sciences customers accelerate scientific breakthroughs at scale with fully managed bioinformatics workflows.
Nextflow version pinning gives customers full control over when and how they adopt new engine versions. The run-time version override ensures that even when a workflow definition specifies a version via manifest.nextflowVersion in its config or profile, the StartRun API parameter takes precedence, enabling customers to test the same workflow across multiple engine versions without modifying workflow source code. Production workflows can remain on a validated engine version while development teams test newer versions in parallel, reducing the risk of unexpected behavior changes. This is valuable for regulated environments where pipeline validation is required before upgrading to a new engine version.
Nextflow version pinning at run time is now available for Nextflow workflow runs in all AWS HealthOmics regions: US East (N. Virginia), US West (Oregon), Europe (Frankfurt, Ireland, London), Israel (Tel Aviv), and Asia Pacific (Singapore, Seoul). To learn more, visit the Nextflow engine settings documentation.  

AWS HealthOmics now supports Nextflow version 26.04, enabling customers to take advantage of new Nextflow features and enhancements: record types, the strict syntax parser, workflow output summaries, and agent logging mode. AWS HealthOmics is a HIPAA-eligible service that helps healthcare and life sciences customers accelerate scientific breakthroughs at scale with fully managed bioinformatics workflows.

The strict syntax parser, now enabled by default in Nextflow v26.04, helps customers save compute time and costs by enforcing strict linting, consistent block structures, and unambiguous scoping, catching issues during pipeline initialization rather than hours into workflows. Record types allow workflow developers to write workflows with meaningful data names rather than keeping track of order of tuple elements, making workflows more readable, and less error-prone. Workflow output summary in JSON format simplifies integration with downstream tooling. Agent logging mode provides structured, minimal output optimized for AI-assisted workflow debugging and development.

Nextflow v26.04 is now available in all AWS HealthOmics regions: US East (N. Virginia), US West (Oregon), Europe (Frankfurt, Ireland, London), Israel (Tel Aviv), and Asia Pacific (Singapore, Seoul). To learn more, visit the AWS HealthOmics Nextflow workflow definition specifics documentation.

 

​AWS HealthOmics now supports Nextflow version 26.04, enabling customers to take advantage of new Nextflow features and enhancements: record types, the strict syntax parser, workflow output summaries, and agent logging mode. AWS HealthOmics is a HIPAA-eligible service that helps healthcare and life sciences customers accelerate scientific breakthroughs at scale with fully managed bioinformatics workflows.
The strict syntax parser, now enabled by default in Nextflow v26.04, helps customers save compute time and costs by enforcing strict linting, consistent block structures, and unambiguous scoping, catching issues during pipeline initialization rather than hours into workflows. Record types allow workflow developers to write workflows with meaningful data names rather than keeping track of order of tuple elements, making workflows more readable, and less error-prone. Workflow output summary in JSON format simplifies integration with downstream tooling. Agent logging mode provides structured, minimal output optimized for AI-assisted workflow debugging and development.
Nextflow v26.04 is now available in all AWS HealthOmics regions: US East (N. Virginia), US West (Oregon), Europe (Frankfurt, Ireland, London), Israel (Tel Aviv), and Asia Pacific (Singapore, Seoul). To learn more, visit the AWS HealthOmics Nextflow workflow definition specifics documentation.  

Amazon Quick now enables enterprise customers to connect their privately hosted Model Context Protocol (MCP) servers to Quick through Amazon Virtual Private Cloud (VPC). Amazon Quick is an AI assistant that turns questions into answers, answers into actions, and actions into outcomes for you and your entire team. Previously, Quick’s MCP support was limited to third-party hosted servers accessible over the public internet. With VPC support, organizations that host MCP servers on private networks for proprietary applications, custom data sources, and internal tools can now securely extend those capabilities to AI workflows in Quick.

With VPC connectivity for MCP, you can connect Quick to MCP servers running on Amazon EC2, AWS Fargate, AWS Agentcore, or other compute within your private network without exposing them to the internet. During MCP connector creation, select your VPC connection and provide your MCP server URL. Once connected, your team interacts with private MCP servers through natural language in Quick, with all traffic routed securely through your VPC.

VPC support for MCP servers is available in all AWS Regions where Amazon Quick is available.

Learn more about Amazon Quick and try for free. To learn more about connecting private MCP servers, visit the MCP documentation and the VPC connectivity guide.

 

​Amazon Quick now enables enterprise customers to connect their privately hosted Model Context Protocol (MCP) servers to Quick through Amazon Virtual Private Cloud (VPC). Amazon Quick is an AI assistant that turns questions into answers, answers into actions, and actions into outcomes for you and your entire team. Previously, Quick’s MCP support was limited to third-party hosted servers accessible over the public internet. With VPC support, organizations that host MCP servers on private networks for proprietary applications, custom data sources, and internal tools can now securely extend those capabilities to AI workflows in Quick. With VPC connectivity for MCP, you can connect Quick to MCP servers running on Amazon EC2, AWS Fargate, AWS Agentcore, or other compute within your private network without exposing them to the internet. During MCP connector creation, select your VPC connection and provide your MCP server URL. Once connected, your team interacts with private MCP servers through natural language in Quick, with all traffic routed securely through your VPC. VPC support for MCP servers is available in all AWS Regions where Amazon Quick is available. Learn more about Amazon Quick and try for free. To learn more about connecting private MCP servers, visit the MCP documentation and the VPC connectivity guide.  

Amazon Quick Research now enables customers to encrypt their data using customer-managed keys (CMK) through AWS Key Management Service (KMS).

This enhancement allows organizations with strict security and compliance requirements to manage their own encryption keys. With customer-managed keys, you gain enhanced security control and comprehensive audit capabilities through AWS CloudTrail integration. You can encrypt your data with your own KMS keys, trace all data access for security auditing, and revoke access to compromised keys within 15 minutes during security incidents. This feature supports multiple CMKs with one default key per AWS account per region, providing the flexibility to manage encryption across different datasets while maintaining granular control over your sensitive business intelligence data.

Customer-managed keys must be created in the same AWS account and region as your Quick resources, and only symmetric AWS KMS keys are supported.

This feature is generally available in all AWS Regions where Amazon Quick is available. To learn more, visit the Amazon Quick Research detail page.

 

​Amazon Quick Research now enables customers to encrypt their data using customer-managed keys (CMK) through AWS Key Management Service (KMS).
This enhancement allows organizations with strict security and compliance requirements to manage their own encryption keys. With customer-managed keys, you gain enhanced security control and comprehensive audit capabilities through AWS CloudTrail integration. You can encrypt your data with your own KMS keys, trace all data access for security auditing, and revoke access to compromised keys within 15 minutes during security incidents. This feature supports multiple CMKs with one default key per AWS account per region, providing the flexibility to manage encryption across different datasets while maintaining granular control over your sensitive business intelligence data.
Customer-managed keys must be created in the same AWS account and region as your Quick resources, and only symmetric AWS KMS keys are supported.
This feature is generally available in all AWS Regions where Amazon Quick is available. To learn more, visit the Amazon Quick Research detail page.  

Amazon SageMaker Unified Studio now supports custom IAM permissions boundaries, so organizations that enforce Service Control Policies (SCPs) requiring permissions boundaries on all IAM roles can adopt SageMaker Unified Studio without modifying their security posture.

When a user creates a project, SageMaker Unified Studio provisions three IAM roles: a project user role, an Amazon Bedrock service role, and a Bedrock Lambda execution role. With this launch, administrators can specify a permissions boundary in the Tooling blueprint configuration, and all three roles are created with that permissions boundary attached. This satisfies SCP requirements at creation time, and project provisioning succeeds without administrator intervention. The permissions boundary also limits what the provisioned roles can do, so administrators retain control over project-level permissions even as new projects are created. Because the permissions boundary is set at the blueprint level, it applies to every new project automatically.

This feature is available in all AWS Regions where Amazon SageMaker Unified Studio is available. To learn more, visit the Manage Tooling blueprint parameters documentation.

 

​Amazon SageMaker Unified Studio now supports custom IAM permissions boundaries, so organizations that enforce Service Control Policies (SCPs) requiring permissions boundaries on all IAM roles can adopt SageMaker Unified Studio without modifying their security posture. When a user creates a project, SageMaker Unified Studio provisions three IAM roles: a project user role, an Amazon Bedrock service role, and a Bedrock Lambda execution role. With this launch, administrators can specify a permissions boundary in the Tooling blueprint configuration, and all three roles are created with that permissions boundary attached. This satisfies SCP requirements at creation time, and project provisioning succeeds without administrator intervention. The permissions boundary also limits what the provisioned roles can do, so administrators retain control over project-level permissions even as new projects are created. Because the permissions boundary is set at the blueprint level, it applies to every new project automatically. This feature is available in all AWS Regions where Amazon SageMaker Unified Studio is available. To learn more, visit the Manage Tooling blueprint parameters documentation.