AWS announces the general availability of Automated Reasoning checks, a safeguard within Amazon Bedrock Guardrails that uses formal verification techniques to validate the accuracy and policy compliance of outputs from generative AI models. Automated Reasoning checks deliver up to 99% accuracy at detecting correct responses from LLMs – giving you provable assurance in detecting AI hallucinations while also assisting with ambiguity detection in model responses.

Automated Reasoning checks provides a fundamentally different approach from traditional testing methods. Unlike sampling outputs for quality, Automated Reasoning checks offers mathematically rigorous guarantees that AI responses adhere to defined business rules and domain knowledge. This is especially valuable for enterprises in regulated industries that require unambiguous validation of AI outputs before deployment.

Automated Reasoning checks in Amazon Bedrock Guardrails is now generally available in the US (N. Virginia), US (Ohio), US (Oregon), Europe (Frankfurt), Europe (Ireland), and Europe (Paris) Regions. Customers can access the service through the Amazon Bedrock console, as well as the Amazon Bedrock Python SDK.

To learn more about Automated Reasoning checks and how you can integrate it into your generative AI workflows, please visit the Amazon Bedrock documentation. You can also reach out to your AWS account team or an AWS Solutions Architect to discuss your specific use case and requirements. CloudFormation support will be coming soon. Read the news blog, review the documentation, or visit the Guardrails webpage to learn more.

 

​AWS announces the general availability of Automated Reasoning checks, a safeguard within Amazon Bedrock Guardrails that uses formal verification techniques to validate the accuracy and policy compliance of outputs from generative AI models. Automated Reasoning checks deliver up to 99% accuracy at detecting correct responses from LLMs – giving you provable assurance in detecting AI hallucinations while also assisting with ambiguity detection in model responses. Automated Reasoning checks provides a fundamentally different approach from traditional testing methods. Unlike sampling outputs for quality, Automated Reasoning checks offers mathematically rigorous guarantees that AI responses adhere to defined business rules and domain knowledge. This is especially valuable for enterprises in regulated industries that require unambiguous validation of AI outputs before deployment. Automated Reasoning checks in Amazon Bedrock Guardrails is now generally available in the US (N. Virginia), US (Ohio), US (Oregon), Europe (Frankfurt), Europe (Ireland), and Europe (Paris) Regions. Customers can access the service through the Amazon Bedrock console, as well as the Amazon Bedrock Python SDK. To learn more about Automated Reasoning checks and how you can integrate it into your generative AI workflows, please visit the Amazon Bedrock documentation. You can also reach out to your AWS account team or an AWS Solutions Architect to discuss your specific use case and requirements. CloudFormation support will be coming soon. Read the news blog, review the documentation, or visit the Guardrails webpage to learn more.  

AWS continues to expand access to the most advanced foundation models with OpenAI open weight models now available in Amazon Bedrock and Amazon SageMaker JumpStart. Accessing these new open weight models from OpenAI on AWS, gpt-oss-120b and gpt-oss-20b, gives you more freedom to innovate and choose the optimal models for your specific use cases while maintaining complete control over your data.

These OpenAI open weight models excel at coding, scientific analysis, and mathematical reasoning tasks, with performance comparable to leading alternatives. Both feature a 128K context window and adjustable reasoning levels (low/medium/high) to match specific requirements. They support external tool integration and can be used in agentic workflows through frameworks like Strands Agents. With full chain-of-thought output capabilities, you get detailed visibility into the model’s reasoning process. Access these models through Amazon Bedrock’s unified API, allowing you to seamlessly experiment and switch between providers without rewriting code. You can use the OpenAI SDK to call Amazon Bedrock directly by simply updating the endpoint. The models give you the flexibility to modify and customize them for your specific business needs while benefiting from enterprise-grade security and seamless scaling.

The new OpenAI open weight models are now available in Amazon Bedrock in the US West (Oregon) AWS Region, while Amazon SageMaker JumpStart supports these models in the US East (Ohio, N. Virginia) and Asia Pacific (Mumbai, Tokyo) AWS Regions. Deploy them through either Amazon Bedrock’s serverless experience or Amazon SageMaker JumpStart’s comprehensive machine learning development capabilities. To learn more, read the blog, product page, and Amazon Bedrock pricing. Get started today in the Amazon Bedrock console or Amazon SageMaker AI console.

 

​AWS continues to expand access to the most advanced foundation models with OpenAI open weight models now available in Amazon Bedrock and Amazon SageMaker JumpStart. Accessing these new open weight models from OpenAI on AWS, gpt-oss-120b and gpt-oss-20b, gives you more freedom to innovate and choose the optimal models for your specific use cases while maintaining complete control over your data. These OpenAI open weight models excel at coding, scientific analysis, and mathematical reasoning tasks, with performance comparable to leading alternatives. Both feature a 128K context window and adjustable reasoning levels (low/medium/high) to match specific requirements. They support external tool integration and can be used in agentic workflows through frameworks like Strands Agents. With full chain-of-thought output capabilities, you get detailed visibility into the model’s reasoning process. Access these models through Amazon Bedrock’s unified API, allowing you to seamlessly experiment and switch between providers without rewriting code. You can use the OpenAI SDK to call Amazon Bedrock directly by simply updating the endpoint. The models give you the flexibility to modify and customize them for your specific business needs while benefiting from enterprise-grade security and seamless scaling. The new OpenAI open weight models are now available in Amazon Bedrock in the US West (Oregon) AWS Region, while Amazon SageMaker JumpStart supports these models in the US East (Ohio, N. Virginia) and Asia Pacific (Mumbai, Tokyo) AWS Regions. Deploy them through either Amazon Bedrock’s serverless experience or Amazon SageMaker JumpStart’s comprehensive machine learning development capabilities. To learn more, read the blog, product page, and Amazon Bedrock pricing. Get started today in the Amazon Bedrock console or Amazon SageMaker AI console.  

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

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

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

 

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

Amazon RDS io2 Block Express volumes are now available in all commercial regions, except and AWS GovCloud (US) and AWS China regions. Amazon RDS io2 Block Express volumes provide consistent sub-millisecond latency for mission critical workloads.

Amazon RDS io2 Block Express volumes are designed for all your critical database workloads that demand high performance, high throughput, and consistently low latency. io2 Block Express storage has the lowest p99.9 I/O latency and the best outlier latency control among major cloud providers, making it ideal for the most I/O-intensive, mission-critical database workloads. io2 Block Express supports 99.999% durability, up to 64 TiB volumes, 4,000 MB/s throughput, and up to 256,000 Provisioned IOPS for your most demanding database needs for the same price as Amazon RDS io1 volumes. You can upgrade from an Amazon RDS io1 volume to an Amazon RDS io2 Block Express without any downtime using the ModifyDBInstance API.

To learn more about Amazon RDS storage, visit the Amazon RDS User’s Guide. Create or update a fully managed Amazon RDS database with an io2 Block Express volume or modify an existing io1, gp2, or gp3 volume type without disruptions in the Amazon RDS Management Console.

 

​Amazon RDS io2 Block Express volumes are now available in all commercial regions, except and AWS GovCloud (US) and AWS China regions. Amazon RDS io2 Block Express volumes provide consistent sub-millisecond latency for mission critical workloads.
Amazon RDS io2 Block Express volumes are designed for all your critical database workloads that demand high performance, high throughput, and consistently low latency. io2 Block Express storage has the lowest p99.9 I/O latency and the best outlier latency control among major cloud providers, making it ideal for the most I/O-intensive, mission-critical database workloads. io2 Block Express supports 99.999% durability, up to 64 TiB volumes, 4,000 MB/s throughput, and up to 256,000 Provisioned IOPS for your most demanding database needs for the same price as Amazon RDS io1 volumes. You can upgrade from an Amazon RDS io1 volume to an Amazon RDS io2 Block Express without any downtime using the ModifyDBInstance API.
To learn more about Amazon RDS storage, visit the Amazon RDS User’s Guide. Create or update a fully managed Amazon RDS database with an io2 Block Express volume or modify an existing io1, gp2, or gp3 volume type without disruptions in the Amazon RDS Management Console.  

AWS Parallel Computing Service (PCS) now supports Internet Protocol Version 6 (IPv6) for Slurm endpoints, enabling customers to run workloads in IPv6-only or dual-stack Amazon Virtual Private Clouds (VPCs). This helps customers meet IPv6 compliance requirements.

PCS is a managed service that makes it easier for you to run and scale your high performance computing (HPC) workloads and build scientific and engineering models on AWS using Slurm. You can use PCS to build complete, elastic environments that integrate compute, storage, networking, and visualization tools. PCS simplifies cluster operations with managed updates and built-in observability features, helping to remove the burden of maintenance. You can work in a familiar environment, focusing on your research and innovation instead of worrying about infrastructure.

PCS support for IPv6 is available in all AWS Regions where PCS is available. Visit our getting started documentation to start using the feature.

 

​AWS Parallel Computing Service (PCS) now supports Internet Protocol Version 6 (IPv6) for Slurm endpoints, enabling customers to run workloads in IPv6-only or dual-stack Amazon Virtual Private Clouds (VPCs). This helps customers meet IPv6 compliance requirements. PCS is a managed service that makes it easier for you to run and scale your high performance computing (HPC) workloads and build scientific and engineering models on AWS using Slurm. You can use PCS to build complete, elastic environments that integrate compute, storage, networking, and visualization tools. PCS simplifies cluster operations with managed updates and built-in observability features, helping to remove the burden of maintenance. You can work in a familiar environment, focusing on your research and innovation instead of worrying about infrastructure. PCS support for IPv6 is available in all AWS Regions where PCS is available. Visit our getting started documentation to start using the feature.  

AWS Elastic Beanstalk now supports VPCE endpoints that have been validated under the Federal Information Processing Standard (FIPS) 140-3 program. You can now easily use AWS PrivateLink to access Elastic Beanstalk endpoints when you require a secure connection using a FIPS 140-3 validated cryptographic module.

FIPS compliant endpoints help companies contracting with the US federal government meet the FIPS security requirement to encrypt sensitive data in supported regions. To create a VPC endpoint that connects to an Elastic Beanstalk endpoint, see Setting up a VPC endpoint for Elastic Beanstalk.

This new capability is available in all AWS Commercial Regions in the United States. For a complete list of regions and service offerings, see AWS Regions.

For more information about FIPS 140-3 at AWS, visit FIPS 140-3 Compliance. To learn more about Elastic Beanstalk, visit the Elastic Beanstalk product page.

 

​AWS Elastic Beanstalk now supports VPCE endpoints that have been validated under the Federal Information Processing Standard (FIPS) 140-3 program. You can now easily use AWS PrivateLink to access Elastic Beanstalk endpoints when you require a secure connection using a FIPS 140-3 validated cryptographic module. FIPS compliant endpoints help companies contracting with the US federal government meet the FIPS security requirement to encrypt sensitive data in supported regions. To create a VPC endpoint that connects to an Elastic Beanstalk endpoint, see Setting up a VPC endpoint for Elastic Beanstalk. This new capability is available in all AWS Commercial Regions in the United States. For a complete list of regions and service offerings, see AWS Regions. For more information about FIPS 140-3 at AWS, visit FIPS 140-3 Compliance. To learn more about Elastic Beanstalk, visit the Elastic Beanstalk product page.  

Customers can now use Anthropic’s Claude Opus 4.1 in Amazon Bedrock. Claude Opus 4.1 is Anthropic’s most intelligent model to date and an industry leader for coding and agents.

Claude Opus 4.1 is a drop-in replacement for Opus 4 and delivers superior performance and precision for real-world coding and agentic tasks. Its advanced coding capabilities include independently planning and executing complex end-to-end development tasks while adapting to your style and maintaining high quality. The model also offers improved frontend code generation, delivering strong visual output quality with a focus on effectively handling complex logic. Opus 4.1’s long-horizon task handling and complex problem-solving abilities make it an ideal virtual collaborator for sustained reasoning and long chains of actions. It also enhances AI agent performance, enabling agents to tackle complex, multi-step tasks with peak accuracy. Anthropic believes Opus 4.1 shines in agentic search and research, content creation, memory and context management—allowing for comprehensive insight synthesis, high-quality content production, and effective summarization.

Claude Opus 4.1 is now available in Amazon Bedrock in US West (Oregon), US East (N. Virginia), and US East (Ohio). To get started with Claude Opus 4.1 in Amazon Bedrock, visit the Amazon Bedrock console, Anthropic’s Claude in Amazon Bedrock product page, and the Amazon Bedrock pricing page.

 

​Customers can now use Anthropic’s Claude Opus 4.1 in Amazon Bedrock. Claude Opus 4.1 is Anthropic’s most intelligent model to date and an industry leader for coding and agents.
Claude Opus 4.1 is a drop-in replacement for Opus 4 and delivers superior performance and precision for real-world coding and agentic tasks. Its advanced coding capabilities include independently planning and executing complex end-to-end development tasks while adapting to your style and maintaining high quality. The model also offers improved frontend code generation, delivering strong visual output quality with a focus on effectively handling complex logic. Opus 4.1’s long-horizon task handling and complex problem-solving abilities make it an ideal virtual collaborator for sustained reasoning and long chains of actions. It also enhances AI agent performance, enabling agents to tackle complex, multi-step tasks with peak accuracy. Anthropic believes Opus 4.1 shines in agentic search and research, content creation, memory and context management—allowing for comprehensive insight synthesis, high-quality content production, and effective summarization.
Claude Opus 4.1 is now available in Amazon Bedrock in US West (Oregon), US East (N. Virginia), and US East (Ohio). To get started with Claude Opus 4.1 in Amazon Bedrock, visit the Amazon Bedrock console, Anthropic’s Claude in Amazon Bedrock product page, and the Amazon Bedrock pricing page.  

Today, AWS announces the general availability of Amazon Elastic VMware Service (Amazon EVS), allowing you to run VMware Cloud Foundation (VCF) directly within your Amazon Virtual Private Cloud (Amazon VPC). With Amazon EVS, you can leverage the scale, elasticity, and performance of AWS while maintaining your familiar VCF software and existing skills, eliminating the need to re-platform or re-factor applications during your migration.

Amazon EVS offers you choice, control, and flexibility in managing your VMware environments. You can self-manage your infrastructure or leverage the expertise of AWS Partners to manage and operate your VCF environment on AWS. The service allows you to retain control over your VMware architecture in the cloud with full administrative access to your environment. Meet your unique needs as you optimize your virtualization stack on AWS and integrate preferred external storage, backup, and disaster recovery solutions. You can also take advantage of flexible consumption models, including on-demand, one-year, and three-year options, enabling cost optimization. Amazon EVS combines the benefits of AWS with a ready-to-use VCF environment so you can migrate, scale, and optimize with over 200 fully featured services, from advanced generative AI capabilities to foundational compute, storage, and database services. With license portability entitlement, you can bring your VCF licenses to Amazon EVS. The service currently supports VCF version 5.2.1 and runs workloads on i4i.metal instances.

Amazon EVS is now generally available in six AWS Regions: US East (N. Virginia), US East (Ohio), US West (Oregon), Asia Pacific (Tokyo), Europe (Frankfurt), and Europe (Ireland).

To get started with Amazon EVS, access the service through the AWS Console. For more information about features and pricing, visit the Amazon EVS product page and refer to the documentation to help you get started.

 

​Today, AWS announces the general availability of Amazon Elastic VMware Service (Amazon EVS), allowing you to run VMware Cloud Foundation (VCF) directly within your Amazon Virtual Private Cloud (Amazon VPC). With Amazon EVS, you can leverage the scale, elasticity, and performance of AWS while maintaining your familiar VCF software and existing skills, eliminating the need to re-platform or re-factor applications during your migration. Amazon EVS offers you choice, control, and flexibility in managing your VMware environments. You can self-manage your infrastructure or leverage the expertise of AWS Partners to manage and operate your VCF environment on AWS. The service allows you to retain control over your VMware architecture in the cloud with full administrative access to your environment. Meet your unique needs as you optimize your virtualization stack on AWS and integrate preferred external storage, backup, and disaster recovery solutions. You can also take advantage of flexible consumption models, including on-demand, one-year, and three-year options, enabling cost optimization. Amazon EVS combines the benefits of AWS with a ready-to-use VCF environment so you can migrate, scale, and optimize with over 200 fully featured services, from advanced generative AI capabilities to foundational compute, storage, and database services. With license portability entitlement, you can bring your VCF licenses to Amazon EVS. The service currently supports VCF version 5.2.1 and runs workloads on i4i.metal instances.
Amazon EVS is now generally available in six AWS Regions: US East (N. Virginia), US East (Ohio), US West (Oregon), Asia Pacific (Tokyo), Europe (Frankfurt), and Europe (Ireland). To get started with Amazon EVS, access the service through the AWS Console. For more information about features and pricing, visit the Amazon EVS product page and refer to the documentation to help you get started.  

AWS Resource Explorer now supports 120 more resource types across all AWS commercial Regions from services including Amazon API Gateway, Amazon Bedrock, Amazon Kendra, Amazon Sagemaker, and more.

With this release, customers can now search for the following resource types in AWS Resource Explorer:

1. apigateway:restapis/deployments	61. iotwireless:WirelessDevice
2. apigateway:restapis/resources/methods	62. iotwireless:WirelessGateway
3. apigateway:restapis/resources	63. iotwireless:WirelessGatewayTaskDefinition
4. apigateway:restapis/stages	64. ivs:playback-key
5. apigateway:apis	65. kendra:index/access-control-configuration
6. apigateway:apis/routes	66. kendra:index/data-source
7. apigateway:apis/stages	67. kendra:index/faq
8. appmesh:mesh/virtualGateway/gatewayRoute	68. kendra:index/featured-results-set
9. appmesh:mesh/virtualRouter/route	69. kendra:index/query-suggestions-block-list
10. appmesh:mesh/virtualGateway	70. kendra:index/thesaurus
11. appmesh:mesh/virtualRouter	71. kendra:index/experience
12. apprunner:autoscalingconfiguration	72. kinesisvideo:channel
13. apprunner:connection	73. license-manager:grant
14. autoscaling:autoScalingGroup	74. mediapackage-vod:assets
15. backup-gateway:hypervisor	75. mediastore:container
16. batch:job-definition	76. mediatailor:channel
17. bedrock:agent	77. mediatailor:liveSource
18. bedrock:application-inference-profile	78. memorydb:snapshot
19. bedrock:data-automation-project	79. mobiletargeting:templates/SMS
20. bedrock:flow	80. mobiletargeting:templates/PUSH
21. bedrock:guardrail	81. mobiletargeting:templates/EMAIL
22. bedrock:knowledge-base	82. mq:configuration
23. bedrock:prompt	83. profile:domains
24. bedrock:prompt-router	84. proton:environment-template
25. chime:app-instance	85. proton:service-template
26. chime:app-instance/bot	86. redshift:hsmclientcertificate
27. chime:app-instance/user	87. s3:multiregionaccesspoint
28. chime:media-insights-pipeline-configuration	88. sagemaker:action
29. chime:media-pipeline	89. sagemaker:algorithm
30. chime:media-pipeline-kinesis-video-stream-pool	90. sagemaker:app
31. chime:sma	91. sagemaker:artifact
32. chime:vc	92. sagemaker:code-repository
33. config:config-rule	93. sagemaker:context
34. connect:instance/operating-hours	94. sagemaker:endpoint-config
35. dms:cert	95. sagemaker:experiment
36. eks:eks-anywhere-subscription	96. sagemaker:experiment-trial
37. eks:podidentityassociation	97. sagemaker:experiment-trial-component
38. emr-containers:jobtemplates	98. sagemaker:human-task-ui
39. emr-containers:virtualclusters/endpoints	99. sagemaker:image-version
40. emr-containers:securityconfigurations	100. sagemaker:inference-component
41. events:api-destination	101. sagemaker:inference-experiment
42. gamelift:script	102. sagemaker:model-package-group
43. guardduty:detector	103. sagemaker:model-package
44. guardduty:malware-protection-plan	104. sagemaker:model-card
45. guardduty:detector/publishingDestination	105. sagemaker:notebook-instance-lifecycle-config
46. inspector2:filter	106. sagemaker:project
47. iot:billinggroup	107. sagemaker:space
48. iot:fleetmetric	108. sagemaker:user-profile
49. iot:scheduledaudit	109. sagemaker:workforce
50. iot:thinggroup	110. sagemaker:cluster
51. iot:thingtype	111. sagemaker:flow-definition
52. iotfleethub:application	112. sagemaker:hub
53. iotsitewise:access-policy	113. sagemaker:mlflow-tracking-server
54. iotsitewise:portal	114. sagemaker:studio-lifecycle-config
55. iotsitewise:project	115. sagemaker:workteam
56. iotwireless:Destination	116. ses:dedicated-ip-pool
57. iotwireless:DeviceProfile	117. ssm:session
58. iotwireless:FuotaTask	118. synthetics:canary
59. iotwireless:MulticastGroup	119. transfer:server
60. iotwireless:SidewalkAccount	120. transfer:user

To view a complete list of all supported types, see the supported resource types page.

 

​AWS Resource Explorer now supports 120 more resource types across all AWS commercial Regions from services including Amazon API Gateway, Amazon Bedrock, Amazon Kendra, Amazon Sagemaker, and more. With this release, customers can now search for the following resource types in AWS Resource Explorer:

1. apigateway:restapis/deployments
61. iotwireless:WirelessDevice

2. apigateway:restapis/resources/methods
62. iotwireless:WirelessGateway

3. apigateway:restapis/resources
63. iotwireless:WirelessGatewayTaskDefinition

4. apigateway:restapis/stages
64. ivs:playback-key

5. apigateway:apis
65. kendra:index/access-control-configuration

6. apigateway:apis/routes
66. kendra:index/data-source

7. apigateway:apis/stages
67. kendra:index/faq

8. appmesh:mesh/virtualGateway/gatewayRoute
68. kendra:index/featured-results-set

9. appmesh:mesh/virtualRouter/route
69. kendra:index/query-suggestions-block-list

10. appmesh:mesh/virtualGateway
70. kendra:index/thesaurus

11. appmesh:mesh/virtualRouter
71. kendra:index/experience

12. apprunner:autoscalingconfiguration
72. kinesisvideo:channel

13. apprunner:connection
73. license-manager:grant

14. autoscaling:autoScalingGroup
74. mediapackage-vod:assets

15. backup-gateway:hypervisor
75. mediastore:container

16. batch:job-definition
76. mediatailor:channel

17. bedrock:agent
77. mediatailor:liveSource

18. bedrock:application-inference-profile
78. memorydb:snapshot

19. bedrock:data-automation-project
79. mobiletargeting:templates/SMS

20. bedrock:flow
80. mobiletargeting:templates/PUSH

21. bedrock:guardrail
81. mobiletargeting:templates/EMAIL

22. bedrock:knowledge-base
82. mq:configuration

23. bedrock:prompt
83. profile:domains

24. bedrock:prompt-router
84. proton:environment-template

25. chime:app-instance
85. proton:service-template

26. chime:app-instance/bot
86. redshift:hsmclientcertificate

27. chime:app-instance/user
87. s3:multiregionaccesspoint

28. chime:media-insights-pipeline-configuration
88. sagemaker:action

29. chime:media-pipeline
89. sagemaker:algorithm

30. chime:media-pipeline-kinesis-video-stream-pool
90. sagemaker:app

31. chime:sma
91. sagemaker:artifact

32. chime:vc
92. sagemaker:code-repository

33. config:config-rule
93. sagemaker:context

34. connect:instance/operating-hours
94. sagemaker:endpoint-config

35. dms:cert
95. sagemaker:experiment

36. eks:eks-anywhere-subscription
96. sagemaker:experiment-trial

37. eks:podidentityassociation
97. sagemaker:experiment-trial-component

38. emr-containers:jobtemplates
98. sagemaker:human-task-ui

39. emr-containers:virtualclusters/endpoints
99. sagemaker:image-version

40. emr-containers:securityconfigurations
100. sagemaker:inference-component

41. events:api-destination
101. sagemaker:inference-experiment

42. gamelift:script
102. sagemaker:model-package-group

43. guardduty:detector
103. sagemaker:model-package

44. guardduty:malware-protection-plan
104. sagemaker:model-card

45. guardduty:detector/publishingDestination
105. sagemaker:notebook-instance-lifecycle-config

46. inspector2:filter
106. sagemaker:project

47. iot:billinggroup
107. sagemaker:space

48. iot:fleetmetric
108. sagemaker:user-profile

49. iot:scheduledaudit
109. sagemaker:workforce

50. iot:thinggroup
110. sagemaker:cluster

51. iot:thingtype
111. sagemaker:flow-definition

52. iotfleethub:application
112. sagemaker:hub

53. iotsitewise:access-policy
113. sagemaker:mlflow-tracking-server

54. iotsitewise:portal
114. sagemaker:studio-lifecycle-config

55. iotsitewise:project
115. sagemaker:workteam

56. iotwireless:Destination
116. ses:dedicated-ip-pool

57. iotwireless:DeviceProfile
117. ssm:session

58. iotwireless:FuotaTask
118. synthetics:canary

59. iotwireless:MulticastGroup
119. transfer:server

60. iotwireless:SidewalkAccount
120. transfer:user

To view a complete list of all supported types, see the supported resource types page.  

AWS IoT SiteWise is a managed service that simplifies the collection, organization, and monitoring of industrial equipment data at scale. Today, we are excited to announce asset model interfaces in AWS IoT SiteWise, a new feature that enables industrial customers to define and maintain standardized properties and metrics across similar equipment and process types, while maintaining the flexibility to accommodate equipment variations. 

Prior to asset model interfaces, users needed to define properties, metrics, and hierarchies at the individual asset model level. This process becomes extensive when scaling to hundreds or thousands of assets. Now, users can create a standardized interfaces that define required properties and metrics across multiple asset types. These interfaces act as templates that ensure consistency in data modeling while still allowing individual assets to maintain their unique characteristics. The feature includes automatic property mapping, standardized metric calculations, and simplified rollup metrics management, facilitating data aggregation and analysis across entire industrial operations.

To support larger industrial operations, we have also increased service quotas for AWS IoT SiteWise Asset Model properties and hierarchies. This enhancement allows customers to scale their asset model interfaces more effectively. 

Asset model interfaces is available in all regions where AWS IoT SiteWise is available. Visit the AWS IoT SiteWise documentation and explore Asset Model Interface examples to see how this feature can be implemented in various industrial scenarios. For details on the new limits, please refer to the AWS Service Quotas console.

 

​AWS IoT SiteWise is a managed service that simplifies the collection, organization, and monitoring of industrial equipment data at scale. Today, we are excited to announce asset model interfaces in AWS IoT SiteWise, a new feature that enables industrial customers to define and maintain standardized properties and metrics across similar equipment and process types, while maintaining the flexibility to accommodate equipment variations.  Prior to asset model interfaces, users needed to define properties, metrics, and hierarchies at the individual asset model level. This process becomes extensive when scaling to hundreds or thousands of assets. Now, users can create a standardized interfaces that define required properties and metrics across multiple asset types. These interfaces act as templates that ensure consistency in data modeling while still allowing individual assets to maintain their unique characteristics. The feature includes automatic property mapping, standardized metric calculations, and simplified rollup metrics management, facilitating data aggregation and analysis across entire industrial operations. To support larger industrial operations, we have also increased service quotas for AWS IoT SiteWise Asset Model properties and hierarchies. This enhancement allows customers to scale their asset model interfaces more effectively.  Asset model interfaces is available in all regions where AWS IoT SiteWise is available. Visit the AWS IoT SiteWise documentation and explore Asset Model Interface examples to see how this feature can be implemented in various industrial scenarios. For details on the new limits, please refer to the AWS Service Quotas console.