GPT-5.6 Terra and Luna are now generally available on Amazon Bedrock in AWS GovCloud (US-West) and AWS GovCloud (US-East), bringing the smartest family of models from OpenAI yet to Bedrock’s next-generation inference engine built for high-performance, security and reliability. GPT-5.6 sets a new standard for intelligence and efficiency, allowing you to solve harder problems in less time and with more intelligence per token. The two models span capability tiers from balanced performance (Terra) to fast, cost-efficient inference (Luna).

With GPT-5.6, you can build autonomous coding agents, run long-horizon genomics and biology analyses, and perform advanced cybersecurity research. Terra provides GPT-5.5-level performance at half the cost and Luna brings fast, affordable inference at the lowest price point. GPT-5.6 also supports prompt caching with explicit cache breakpoints, so repeated context across agentic workflows is billed at a 90% discount and doesn’t compound cost as you scale. 

GPT-5.6 Terra and Luna support 1 million token context windows on Amazon Bedrock, enabling you to process full codebases, lengthy documents, and multi-turn agent histories in a single request. Models reason over broader context and return more accurate, coherent responses without chunking or information loss.

For regional availability, please see the Amazon Bedrock regional availability page.  Get started with Terra and Luna using the Amazon Bedrock Console or the Responses API on the bedrock-mantle endpoint. To learn more, see the Amazon Bedrock documentation and read the launch blog post. 

 

​GPT-5.6 Terra and Luna are now generally available on Amazon Bedrock in AWS GovCloud (US-West) and AWS GovCloud (US-East), bringing the smartest family of models from OpenAI yet to Bedrock’s next-generation inference engine built for high-performance, security and reliability. GPT-5.6 sets a new standard for intelligence and efficiency, allowing you to solve harder problems in less time and with more intelligence per token. The two models span capability tiers from balanced performance (Terra) to fast, cost-efficient inference (Luna).
With GPT-5.6, you can build autonomous coding agents, run long-horizon genomics and biology analyses, and perform advanced cybersecurity research. Terra provides GPT-5.5-level performance at half the cost and Luna brings fast, affordable inference at the lowest price point. GPT-5.6 also supports prompt caching with explicit cache breakpoints, so repeated context across agentic workflows is billed at a 90% discount and doesn’t compound cost as you scale. 
GPT-5.6 Terra and Luna support 1 million token context windows on Amazon Bedrock, enabling you to process full codebases, lengthy documents, and multi-turn agent histories in a single request. Models reason over broader context and return more accurate, coherent responses without chunking or information loss.
For regional availability, please see the Amazon Bedrock regional availability page.  Get started with Terra and Luna using the Amazon Bedrock Console or the Responses API on the bedrock-mantle endpoint. To learn more, see the Amazon Bedrock documentation and read the launch blog post.   

Today, OpenAI announced that they are lowering API prices for GPT-5.6 Sol. Following the recent Terra and Luna price reductions, Sol now costs $4 per million input tokens and $20 per million output tokens—20% lower input pricing and 33.3% lower output pricing. This promotional pricing is available at least through November 21, 2026.

Whether you’re building autonomous coding agents, running complex multi-step analyses, or performing advanced research workflows, the reduced pricing gives you more room to experiment and scale what’s already working. GPT-5.6 Sol delivers state-of-the-art results on agentic coding benchmarks, and the lower price point makes it more accessible for sustained, high-volume workloads.

For latest Regional availability of GPT-5.6 Sol, check the AWS Regions page. To learn more and view the pricing visit the Amazon Bedrock documentation on GPT-5.6 Sol.

 

​Today, OpenAI announced that they are lowering API prices for GPT-5.6 Sol. Following the recent Terra and Luna price reductions, Sol now costs $4 per million input tokens and $20 per million output tokens—20% lower input pricing and 33.3% lower output pricing. This promotional pricing is available at least through November 21, 2026.
Whether you’re building autonomous coding agents, running complex multi-step analyses, or performing advanced research workflows, the reduced pricing gives you more room to experiment and scale what’s already working. GPT-5.6 Sol delivers state-of-the-art results on agentic coding benchmarks, and the lower price point makes it more accessible for sustained, high-volume workloads.
For latest Regional availability of GPT-5.6 Sol, check the AWS Regions page. To learn more and view the pricing visit the Amazon Bedrock documentation on GPT-5.6 Sol.  

The Amazon Elastic Kubernetes Service (Amazon EKS) Capability for Argo CD now supports custom configuration through a standard argocd-cm ConfigMap in your cluster. This capability gives you a fully managed GitOps continuous delivery experience, and you can now tune it to fit how your teams work. You can define custom health checks for your Custom Resources, customize the Argo CD UI banner content, adjust how the capability watches and compares the resources it manages, and more. You configure these settings the same way you do in upstream Argo CD, and AWS applies them to your managed capability.

With this launch, cluster administrators now have more control over how Argo CD reports application health. By default, Argo CD has no built-in health logic for Custom Resources, so an Application can report as healthy while its resources are still provisioning, and sync waves can advance before those resources are ready. With a custom health check, you define this logic yourself. For example, a health check for a database resource can hold an Application at progressing until the database is ready. The capability also includes built-in health checks for AWS Controllers for Kubernetes (ACK) and kro (Kube Resource Orchestrator) resources, so these report accurate health with no additional configuration.

You can configure the EKS Capability for Argo CD in all AWS Regions where the capability is available. To learn more, see Amazon EKS and Configure Argo CD settings in the Amazon EKS User Guide.

 

 

​The Amazon Elastic Kubernetes Service (Amazon EKS) Capability for Argo CD now supports custom configuration through a standard argocd-cm ConfigMap in your cluster. This capability gives you a fully managed GitOps continuous delivery experience, and you can now tune it to fit how your teams work. You can define custom health checks for your Custom Resources, customize the Argo CD UI banner content, adjust how the capability watches and compares the resources it manages, and more. You configure these settings the same way you do in upstream Argo CD, and AWS applies them to your managed capability.
With this launch, cluster administrators now have more control over how Argo CD reports application health. By default, Argo CD has no built-in health logic for Custom Resources, so an Application can report as healthy while its resources are still provisioning, and sync waves can advance before those resources are ready. With a custom health check, you define this logic yourself. For example, a health check for a database resource can hold an Application at progressing until the database is ready. The capability also includes built-in health checks for AWS Controllers for Kubernetes (ACK) and kro (Kube Resource Orchestrator) resources, so these report accurate health with no additional configuration.
You can configure the EKS Capability for Argo CD in all AWS Regions where the capability is available. To learn more, see Amazon EKS and Configure Argo CD settings in the Amazon EKS User Guide.
   

Amazon Connect Customer now lets managers chat with their data in plain language and get back the answer, the evidence behind it, and the fix, in seconds. Managers have always had the data. What they haven’t had is the time to dig through dashboards, find what’s driving performance, and decide what to do next. Now Amazon Connect Customer does that work for them. It searches across more than 150 metrics spanning self-service, agent performance, and queue performance to find what matters, explain why, and recommend the best next step. 
 
Managers can start broad and go deep in the same conversation. For example, a manager can ask which queues are the best candidates for automation, and Amazon Connect Customer reviews where handle time and after-contact work run highest, then returns a prioritized list with confidence scores and projected impact. What once required analysts, dashboards, and weeks of investigation now becomes a prioritized action plan in seconds. 
 
This feature is available in all AWS Regions where Amazon Connect Customer AI Agents are supported. To learn more, visit our product documentation. 

 

​Amazon Connect Customer now lets managers chat with their data in plain language and get back the answer, the evidence behind it, and the fix, in seconds. Managers have always had the data. What they haven’t had is the time to dig through dashboards, find what’s driving performance, and decide what to do next. Now Amazon Connect Customer does that work for them. It searches across more than 150 metrics spanning self-service, agent performance, and queue performance to find what matters, explain why, and recommend the best next step.    Managers can start broad and go deep in the same conversation. For example, a manager can ask which queues are the best candidates for automation, and Amazon Connect Customer reviews where handle time and after-contact work run highest, then returns a prioritized list with confidence scores and projected impact. What once required analysts, dashboards, and weeks of investigation now becomes a prioritized action plan in seconds.    This feature is available in all AWS Regions where Amazon Connect Customer AI Agents are supported. To learn more, visit our product documentation.   

The AWS Deadline Cloud monitor now shows the progress, status, and health of your automatic file downlaods from jobs running in the cloud. Deadline Cloud is a fully managed service that helps teams run compute-intensive workloads in the cloud for visual effects, animation, product design, simulation, and gaming. The Deadline Cloud Monitor (DCM) desktop app provides customers with visibility into their render environments, jobs, resources, and costs. Now, customers can also use the monitor to confirm that automatically configured job outputs successfully downloaded to their destination drive.

With this update, the monitor app introduces a new Download status column at both the job and task level, showing download progress and confirming when all output files are available on your drive. An indicator displays how current that status is. If files are unavailable for any reason, the app surfaces clear guidance on next steps. This eliminates manual drive verification and helps teams confidently confirm output availability before downstream tasks begin, particularly valuable in large-scale render pipelines where manual file checking is impractical.

To learn more about AWS Deadline Cloud and the new automatic download status feature in the Deadline Cloud Monitor desktop app, visit https://aws.amazon.com/deadline-cloud/.

 

​The AWS Deadline Cloud monitor now shows the progress, status, and health of your automatic file downlaods from jobs running in the cloud. Deadline Cloud is a fully managed service that helps teams run compute-intensive workloads in the cloud for visual effects, animation, product design, simulation, and gaming. The Deadline Cloud Monitor (DCM) desktop app provides customers with visibility into their render environments, jobs, resources, and costs. Now, customers can also use the monitor to confirm that automatically configured job outputs successfully downloaded to their destination drive.
With this update, the monitor app introduces a new Download status column at both the job and task level, showing download progress and confirming when all output files are available on your drive. An indicator displays how current that status is. If files are unavailable for any reason, the app surfaces clear guidance on next steps. This eliminates manual drive verification and helps teams confidently confirm output availability before downstream tasks begin, particularly valuable in large-scale render pipelines where manual file checking is impractical.
To learn more about AWS Deadline Cloud and the new automatic download status feature in the Deadline Cloud Monitor desktop app, visit https://aws.amazon.com/deadline-cloud/.  

AWS Glue 6.0 is now generally available, delivering a 30% price reduction and introducing full support for Apache Iceberg v3, newer versions of Apache Hudi and Delta Lake, and new capabilities to improve developer productivity. AWS Glue 6.0 also upgrades runtime to Apache Spark 4.1, Python 3.13, and Scala 2.13.

With Apache Iceberg v3, AWS Glue 6.0 adds the VARIANT data type with automatic shredding for faster reads on semi-structured data, deletion vectors for high-performance row-level updates, geometry and geography data types for spatial processing, and flexible schema evolution through UNKNOWN data type and DEFAULT column values. Glue 6.0 also introduces features that boost developer productivity and performance, such as Spark Declarative Pipelines that eliminate repetitive orchestration code, Real-Time Mode streaming for sub-second latencies, and Arrow-native Python UDFs for improved PySpark performance. These capabilities help you implement large-scale ETL, recurring batch workloads, streaming analytics, and AI application development using AWS Glue.

AWS Glue 6.0 is available in all AWS Commercial, AWS GovCloud (US), and AWS China regions.

To get started, select Glue 6.0 from the version dropdown in the AWS Glue console or SageMaker Unified Studio when creating a new job, or migrate existing jobs using the Spark Upgrade Agent. To learn more, visit the AWS Glue documentation and AWS Glue pricing.

 

​AWS Glue 6.0 is now generally available, delivering a 30% price reduction and introducing full support for Apache Iceberg v3, newer versions of Apache Hudi and Delta Lake, and new capabilities to improve developer productivity. AWS Glue 6.0 also upgrades runtime to Apache Spark 4.1, Python 3.13, and Scala 2.13.
With Apache Iceberg v3, AWS Glue 6.0 adds the VARIANT data type with automatic shredding for faster reads on semi-structured data, deletion vectors for high-performance row-level updates, geometry and geography data types for spatial processing, and flexible schema evolution through UNKNOWN data type and DEFAULT column values. Glue 6.0 also introduces features that boost developer productivity and performance, such as Spark Declarative Pipelines that eliminate repetitive orchestration code, Real-Time Mode streaming for sub-second latencies, and Arrow-native Python UDFs for improved PySpark performance. These capabilities help you implement large-scale ETL, recurring batch workloads, streaming analytics, and AI application development using AWS Glue.
AWS Glue 6.0 is available in all AWS Commercial, AWS GovCloud (US), and AWS China regions.
To get started, select Glue 6.0 from the version dropdown in the AWS Glue console or SageMaker Unified Studio when creating a new job, or migrate existing jobs using the Spark Upgrade Agent. To learn more, visit the AWS Glue documentation and AWS Glue pricing.  

Amazon Simple Email Service (SES) now supports open and click tracking override parameters in the SendEmail and SendBulkEmail APIs. Senders can enable or disable open tracking and click tracking on an individual API call, rather than managing tracking preferences through separate configuration sets.

Previously, controlling tracking behavior required maintaining a distinct configuration set for each combination of open- and click-tracking settings. With this new capability, you specify the tracking preference directly in the send request, reducing configuration overhead and simplifying how you honor recipient-level tracking consent. This is useful for senders that must respect per-recipient consent choices to meet data protection requirements such as GDPR and CNIL guidance.

The tracking overrides apply per request and take precedence over the tracking behavior defined in the associated configuration set, giving you fine-grained control without changing your existing configuration set structure. There is no additional cost to use this feature.

This capability is available in all AWS Regions where Amazon SES is available.

To learn more, see the documentation on open and click tracking in the Amazon SES Developer Guide. 

 

​Amazon Simple Email Service (SES) now supports open and click tracking override parameters in the SendEmail and SendBulkEmail APIs. Senders can enable or disable open tracking and click tracking on an individual API call, rather than managing tracking preferences through separate configuration sets. Previously, controlling tracking behavior required maintaining a distinct configuration set for each combination of open- and click-tracking settings. With this new capability, you specify the tracking preference directly in the send request, reducing configuration overhead and simplifying how you honor recipient-level tracking consent. This is useful for senders that must respect per-recipient consent choices to meet data protection requirements such as GDPR and CNIL guidance. The tracking overrides apply per request and take precedence over the tracking behavior defined in the associated configuration set, giving you fine-grained control without changing your existing configuration set structure. There is no additional cost to use this feature. This capability is available in all AWS Regions where Amazon SES is available.
To learn more, see the documentation on open and click tracking in the Amazon SES Developer Guide.   

Amazon Elastic Compute Cloud (Amazon EC2) C8gd, M8gd, and R8gd instances with up to 11.4 TB of local NVMe-based SSD block-level storage are now available in additional regions. C8gd instances are now available in Asia Pacific (Singapore), M8gd instances are available in Mexico (Central) and Asia Pacific (Melbourne), and R8gd instances are available in Europe (Zurich). These instances are powered by AWS Graviton4 processors, delivering up to 30% better performance over Graviton3-based instances. They have up to 40% higher performance for I/O intensive database workloads, and up to 20% faster query results for I/O intensive real-time data analytics than comparable AWS Graviton3-based instances. These instances are built on the AWS Nitro System and are a great fit for applications that need access to high-speed, low latency local storage.

Each instance is available in 12 different sizes. They provide up to 50 Gbps of network bandwidth and up to 40 Gbps of bandwidth to the Amazon Elastic Block Store (Amazon EBS). Additionally, customers can now adjust the network and Amazon EBS bandwidth on these instances by 25% using EC2 instance bandwidth weighting configuration, providing greater flexibility with the allocation of bandwidth resources to better optimize workloads. These instances offer Elastic Fabric Adapter (EFA) networking on 24xlarge, 48xlarge, metal-24xl, and metal-48xl sizes.

C8gd instances are ideal for compute-intensive workloads such as high-performance web servers, batch processing, distributed analytics, ad serving, video encoding, and gaming servers. M8gd instances are well-suited for balanced workloads including application servers, microservices, enterprise applications, and small to medium databases. R8gd instances are ideal for memory-intensive workloads such as in-memory databases, real-time big data analytics, large in-memory caches, and scientific computing applications.

To learn more, see Amazon C8gd Instances, Amazon M8gd Instances and Amazon R8gd 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.

 

​Amazon Elastic Compute Cloud (Amazon EC2) C8gd, M8gd, and R8gd instances with up to 11.4 TB of local NVMe-based SSD block-level storage are now available in additional regions. C8gd instances are now available in Asia Pacific (Singapore), M8gd instances are available in Mexico (Central) and Asia Pacific (Melbourne), and R8gd instances are available in Europe (Zurich). These instances are powered by AWS Graviton4 processors, delivering up to 30% better performance over Graviton3-based instances. They have up to 40% higher performance for I/O intensive database workloads, and up to 20% faster query results for I/O intensive real-time data analytics than comparable AWS Graviton3-based instances. These instances are built on the AWS Nitro System and are a great fit for applications that need access to high-speed, low latency local storage. Each instance is available in 12 different sizes. They provide up to 50 Gbps of network bandwidth and up to 40 Gbps of bandwidth to the Amazon Elastic Block Store (Amazon EBS). Additionally, customers can now adjust the network and Amazon EBS bandwidth on these instances by 25% using EC2 instance bandwidth weighting configuration, providing greater flexibility with the allocation of bandwidth resources to better optimize workloads. These instances offer Elastic Fabric Adapter (EFA) networking on 24xlarge, 48xlarge, metal-24xl, and metal-48xl sizes.
C8gd instances are ideal for compute-intensive workloads such as high-performance web servers, batch processing, distributed analytics, ad serving, video encoding, and gaming servers. M8gd instances are well-suited for balanced workloads including application servers, microservices, enterprise applications, and small to medium databases. R8gd instances are ideal for memory-intensive workloads such as in-memory databases, real-time big data analytics, large in-memory caches, and scientific computing applications.
To learn more, see Amazon C8gd Instances, Amazon M8gd Instances and Amazon R8gd 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.  

AWS Local Zone in Las Vegas, Nevada is now generally available. The new AWS Local Zone supports Amazon Elastic Compute Cloud (Amazon EC2) C7i, M7i, R7i, and C8gn instances, Amazon Elastic Block Store (Amazon EBS) volume types gp3, gp2, io1, sc1, and st1, Amazon Elastic Container Service (Amazon ECS), Amazon Elastic Kubernetes Service (Amazon EKS), Application Load Balancer, and AWS Direct Connect.

AWS Local Zones are AWS infrastructure deployments that extend core services, such as compute, storage, networking, and other select services, closer to metropolitan areas worldwide. AWS Local Zones help you achieve single-digit millisecond latency for end-user workloads, meet data residency requirements, support AI/ML inference workloads, and accelerate migration and modernization of legacy applications to the cloud, all while maintaining consistent AWS APIs, tools, and services as AWS Regions. AWS Local Zones are available in more than 30 metropolitan areas worldwide.

To get started, enable the Las Vegas Local Zone (us-west-2-las-2a) from the Regions and Zones tab in the AWS Global View or by using the ModifyAvailabilityZoneGroup API. For pricing information, visit the AWS Local Zones pricing page. To learn more, visit the AWS Local Zones overview page.

 

​AWS Local Zone in Las Vegas, Nevada is now generally available. The new AWS Local Zone supports Amazon Elastic Compute Cloud (Amazon EC2) C7i, M7i, R7i, and C8gn instances, Amazon Elastic Block Store (Amazon EBS) volume types gp3, gp2, io1, sc1, and st1, Amazon Elastic Container Service (Amazon ECS), Amazon Elastic Kubernetes Service (Amazon EKS), Application Load Balancer, and AWS Direct Connect.
AWS Local Zones are AWS infrastructure deployments that extend core services, such as compute, storage, networking, and other select services, closer to metropolitan areas worldwide. AWS Local Zones help you achieve single-digit millisecond latency for end-user workloads, meet data residency requirements, support AI/ML inference workloads, and accelerate migration and modernization of legacy applications to the cloud, all while maintaining consistent AWS APIs, tools, and services as AWS Regions. AWS Local Zones are available in more than 30 metropolitan areas worldwide.
To get started, enable the Las Vegas Local Zone (us-west-2-las-2a) from the Regions and Zones tab in the AWS Global View or by using the ModifyAvailabilityZoneGroup API. For pricing information, visit the AWS Local Zones pricing page. To learn more, visit the AWS Local Zones overview page.  

Amazon Timestream for InfluxDB now supports AWS Key Management Service (AWS KMS) customer managed keys for encrypting data at rest in InfluxDB 2 database instances, InfluxDB 2 Read Replicas, and InfluxDB 3 clusters. Customers select a symmetric AWS KMS key when creating a database resource.

Timestream for InfluxDB uses the selected key to encrypt the underlying database storage for InfluxDB 2 and InfluxDB 3 resources. The key must be in the same AWS account and AWS Region as the database resource. Customers specify the key during resource creation. The key cannot be changed after the resource is created.

Customer managed key support is available through the AWS Management Console, AWS Command Line Interface (AWS CLI), and Timestream for InfluxDB application programming interface (API). The feature is available in all AWS Regions where Timestream for InfluxDB is available. There is no additional Timestream for InfluxDB charge for using customer managed keys. Standard AWS KMS charges apply.

Support for Customer managed keys is available in all AWS Regions where Amazon Timestream for InfluxDB is available. To get started, open the Amazon Timestream console. For more information, see the Amazon Timestream for InfluxDB documentation and pricing page.

 

​Amazon Timestream for InfluxDB now supports AWS Key Management Service (AWS KMS) customer managed keys for encrypting data at rest in InfluxDB 2 database instances, InfluxDB 2 Read Replicas, and InfluxDB 3 clusters. Customers select a symmetric AWS KMS key when creating a database resource.
Timestream for InfluxDB uses the selected key to encrypt the underlying database storage for InfluxDB 2 and InfluxDB 3 resources. The key must be in the same AWS account and AWS Region as the database resource. Customers specify the key during resource creation. The key cannot be changed after the resource is created.
Customer managed key support is available through the AWS Management Console, AWS Command Line Interface (AWS CLI), and Timestream for InfluxDB application programming interface (API). The feature is available in all AWS Regions where Timestream for InfluxDB is available. There is no additional Timestream for InfluxDB charge for using customer managed keys. Standard AWS KMS charges apply. Support for Customer managed keys is available in all AWS Regions where Amazon Timestream for InfluxDB is available. To get started, open the Amazon Timestream console. For more information, see the Amazon Timestream for InfluxDB documentation and pricing page.