AWS Marketplace now offers AI agents and tools from AWS Partners, allowing customers to find and buy third-party AI agent solutions with streamlined procurement and multiple deployment options. Customers can accelerate their discovery of AI agents and agent tools in a centralized catalog while enjoying the benefits of purchasing through AWS Marketplace and Partners can quickly bring their AI agent solutions to market.

Customers can explore AI agent products on the new “AI Agent & Tools” solution page. Using natural language, customers can search and receive results that match their specific use cases. When evaluating solutions, customers can review listings that support model context protocol (MCP) and agent-to-agent (A2A) standard protocols, along with various deployment options, to determine the best-fit solution for their needs. Customers can then purchase and deploy their chosen solutions through various paths, including Amazon Bedrock AgentCore Runtime, or add tools to AgentCore Gateway to accelerate agent development.

For AWS Partners, AI Agents and Tools in AWS Marketplace accelerates customer reach and adoption for agentic solutions. By listing their AI agents and tools, Partners can leverage established AWS Marketplace channels to streamline sales, offer flexible pricing, and provide secure AWS deployment options. Partners can categorize their offerings and highlight MCP and A2A protocol support, enhancing discoverability through advanced search and filtering in the AWS Marketplace catalog. Integration with Amazon Bedrock AgentCore services further simplifies deployment for customers, reducing time to value and providing a secure, scalable environment for customers building innovative agentic solutions.

Start exploring AI agent solutions in AWS Marketplace. Learn how AWS Partners can start selling by accessing the AWS Marketplace Seller Guide. 

 

​AWS Marketplace now offers AI agents and tools from AWS Partners, allowing customers to find and buy third-party AI agent solutions with streamlined procurement and multiple deployment options. Customers can accelerate their discovery of AI agents and agent tools in a centralized catalog while enjoying the benefits of purchasing through AWS Marketplace and Partners can quickly bring their AI agent solutions to market.
Customers can explore AI agent products on the new “AI Agent & Tools” solution page. Using natural language, customers can search and receive results that match their specific use cases. When evaluating solutions, customers can review listings that support model context protocol (MCP) and agent-to-agent (A2A) standard protocols, along with various deployment options, to determine the best-fit solution for their needs. Customers can then purchase and deploy their chosen solutions through various paths, including Amazon Bedrock AgentCore Runtime, or add tools to AgentCore Gateway to accelerate agent development. For AWS Partners, AI Agents and Tools in AWS Marketplace accelerates customer reach and adoption for agentic solutions. By listing their AI agents and tools, Partners can leverage established AWS Marketplace channels to streamline sales, offer flexible pricing, and provide secure AWS deployment options. Partners can categorize their offerings and highlight MCP and A2A protocol support, enhancing discoverability through advanced search and filtering in the AWS Marketplace catalog. Integration with Amazon Bedrock AgentCore services further simplifies deployment for customers, reducing time to value and providing a secure, scalable environment for customers building innovative agentic solutions. Start exploring AI agent solutions in AWS Marketplace. Learn how AWS Partners can start selling by accessing the AWS Marketplace Seller Guide.   

Today, Amazon Nova is introducing the most comprehensive suite of model customization capabilities made available for any proprietary model family. Available as ready-to-use recipes on SageMaker AI, these capabilities allow customers to adapt Nova Micro, Nova Lite, and Nova Pro across the model training lifecycle, including pre-training, supervised fine-tuning, and alignment.

Using these customization techniques, you can adapt Nova models to accurately reflect your proprietary knowledge, workflows, and brand in your generative AI applications while maintaining Nova’s industry-leading price performance and low latency. The techniques include Continued Pre-Training, Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), Proximal Policy Optimization, and Knowledge Distillation — with support for both parameter-efficient and full-model training options across SFT, DPO and Distillation.

Nova customization recipes are available in SageMaker training jobs and SageMaker HyperPod, giving you flexibility to select the environment that best fits your infrastructure and scale requirements. You can deploy your customized models on Amazon Bedrock and invoke them via on-demand inference or Provisioned Throughput. On-demand inference is available only with parameter efficient training techniques.

Recipes for Amazon Nova on Amazon SageMaker AI are available in US East (N. Virginia).

To get started read Amazon Nova user guide and visit the GitHub repository to browse Nova specific SageMaker training recipes. 

 

​Today, Amazon Nova is introducing the most comprehensive suite of model customization capabilities made available for any proprietary model family. Available as ready-to-use recipes on SageMaker AI, these capabilities allow customers to adapt Nova Micro, Nova Lite, and Nova Pro across the model training lifecycle, including pre-training, supervised fine-tuning, and alignment. Using these customization techniques, you can adapt Nova models to accurately reflect your proprietary knowledge, workflows, and brand in your generative AI applications while maintaining Nova’s industry-leading price performance and low latency. The techniques include Continued Pre-Training, Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), Proximal Policy Optimization, and Knowledge Distillation — with support for both parameter-efficient and full-model training options across SFT, DPO and Distillation. Nova customization recipes are available in SageMaker training jobs and SageMaker HyperPod, giving you flexibility to select the environment that best fits your infrastructure and scale requirements. You can deploy your customized models on Amazon Bedrock and invoke them via on-demand inference or Provisioned Throughput. On-demand inference is available only with parameter efficient training techniques. Recipes for Amazon Nova on Amazon SageMaker AI are available in US East (N. Virginia). To get started read Amazon Nova user guide and visit the GitHub repository to browse Nova specific SageMaker training recipes.   

Today, Amazon EKS announces support for up to 100,000 worker nodes in a cluster, enabling you to run ultra scale AI/ML training and inference workloads in a single cluster. With Amazon EC2’s new generation accelerated computing instance types, 100,000 worker nodes support up to 1.6 million Trainium chips with Trn2 instances and 800,000 NVIDIA GPUs with P5 and P6 instances in a single cluster. This enables you to run ultra scale AI/ML workloads that require all compute accelerators to be available within a single cluster, as these workloads cannot be easily distributed across multiple clusters.

The most advanced AI models with trillions of parameters demonstrate significantly enhanced capabilities in understanding context, reasoning, and solving complex tasks. To build and operate these increasingly powerful models, organizations require access to massive numbers of compute accelerators in a single cluster. Consolidated access to such a large pool of compute accelerators delivers crucial benefits: allows organizations to build and deploy more powerful AI models than ever before, reduces costs by efficiently sharing compute accelerators between training and inference workloads, and enables seamless use of existing AI/ML tools and frameworks that are not designed to work across clusters.

To learn more, see the launch blog.

 

​Today, Amazon EKS announces support for up to 100,000 worker nodes in a cluster, enabling you to run ultra scale AI/ML training and inference workloads in a single cluster. With Amazon EC2’s new generation accelerated computing instance types, 100,000 worker nodes support up to 1.6 million Trainium chips with Trn2 instances and 800,000 NVIDIA GPUs with P5 and P6 instances in a single cluster. This enables you to run ultra scale AI/ML workloads that require all compute accelerators to be available within a single cluster, as these workloads cannot be easily distributed across multiple clusters. The most advanced AI models with trillions of parameters demonstrate significantly enhanced capabilities in understanding context, reasoning, and solving complex tasks. To build and operate these increasingly powerful models, organizations require access to massive numbers of compute accelerators in a single cluster. Consolidated access to such a large pool of compute accelerators delivers crucial benefits: allows organizations to build and deploy more powerful AI models than ever before, reduces costs by efficiently sharing compute accelerators between training and inference workloads, and enables seamless use of existing AI/ML tools and frameworks that are not designed to work across clusters. To learn more, see the launch blog.  

Amazon S3 Vectors delivers purpose-built, cost-optimized vector storage for AI agents, AI inference, and semantic search of your content stored in Amazon S3. By reducing the cost of uploading, storing, and querying vectors by up to 90%, S3 Vectors makes it cost-effective to create and use large vector datasets to improve the memory and context of AI agents as well as semantic search results of your S3 data. Designed to provide the same elasticity, scale, and durability as Amazon S3, S3 Vectors lets you store and search data with sub-second query performance. It’s ideal for applications that need to build and maintain vector indexes so you can organize and search through massive amounts of information. S3 Vectors provides a simple and flexible API for operations such as finding similar scenes in petabyte-scale video archives, identifying collections of related business documents, or detecting rare patterns in diagnostic collections including millions of medical images.

S3 Vectors is natively integrated with Amazon Bedrock Knowledge Bases so that you can reduce the cost of using large vector datasets for retrieval-augmented generation (RAG). You can also use S3 Vectors with Amazon OpenSearch Service to lower storage costs for infrequent queried vectors, and then quickly move them to OpenSearch as demands increase or to enhance search capabilities.

S3 Vectors introduces a new bucket type optimized for durable, low-cost vector storage. It includes a dedicated set of APIs for you to store, access, and query vectors without provisioning any infrastructure. Within a vector bucket, you can organize vector data within vector indexes and elastically scale up to 10,000 indexes per bucket. When creating a Knowledge Base in Amazon Bedrock or Amazon SageMaker Unified Studio, you can select an S3 vector index as your vector store or use the Quick Create workflow to set one up. Within OpenSearch, you can adopt a tiered strategy to store large vector datasets in S3 for near real-time access while effortlessly activating the vector data with the highest performance requirements in OpenSearch.

Amazon S3 Vectors preview is now available in the US East (N. Virginia), US East (Ohio), US West (Oregon), Asia Pacific (Sydney), and Europe (Frankfurt) Regions. To learn more, visit the product page, S3 pricing page, documentation, and AWS News Blog. 

 

​Amazon S3 Vectors delivers purpose-built, cost-optimized vector storage for AI agents, AI inference, and semantic search of your content stored in Amazon S3. By reducing the cost of uploading, storing, and querying vectors by up to 90%, S3 Vectors makes it cost-effective to create and use large vector datasets to improve the memory and context of AI agents as well as semantic search results of your S3 data. Designed to provide the same elasticity, scale, and durability as Amazon S3, S3 Vectors lets you store and search data with sub-second query performance. It’s ideal for applications that need to build and maintain vector indexes so you can organize and search through massive amounts of information. S3 Vectors provides a simple and flexible API for operations such as finding similar scenes in petabyte-scale video archives, identifying collections of related business documents, or detecting rare patterns in diagnostic collections including millions of medical images.
S3 Vectors is natively integrated with Amazon Bedrock Knowledge Bases so that you can reduce the cost of using large vector datasets for retrieval-augmented generation (RAG). You can also use S3 Vectors with Amazon OpenSearch Service to lower storage costs for infrequent queried vectors, and then quickly move them to OpenSearch as demands increase or to enhance search capabilities.
S3 Vectors introduces a new bucket type optimized for durable, low-cost vector storage. It includes a dedicated set of APIs for you to store, access, and query vectors without provisioning any infrastructure. Within a vector bucket, you can organize vector data within vector indexes and elastically scale up to 10,000 indexes per bucket. When creating a Knowledge Base in Amazon Bedrock or Amazon SageMaker Unified Studio, you can select an S3 vector index as your vector store or use the Quick Create workflow to set one up. Within OpenSearch, you can adopt a tiered strategy to store large vector datasets in S3 for near real-time access while effortlessly activating the vector data with the highest performance requirements in OpenSearch. Amazon S3 Vectors preview is now available in the US East (N. Virginia), US East (Ohio), US West (Oregon), Asia Pacific (Sydney), and Europe (Frankfurt) Regions. To learn more, visit the product page, S3 pricing page, documentation, and AWS News Blog.   

TwelveLabs’ Marengo 2.7 and Pegasus 1.2 multimodal foundation models are now available in Amazon Bedrock. Marengo 2.7 is a video embedding model proficient at performing tasks such as search and classification, enabling enhanced video understanding, while Pegasus 1.2 is a video language model that can generate text based on your video data. By integrating these AI capabilities, organizations can now easily search, categorize, and extract insights from video libraries without deep technical expertise.

These models open up new possibilities for content discovery, analysis, and enhanced user experiences, making video intelligence more accessible across industries. Media and entertainment customers can transform production workflows by instantly finding, summarizing, and connecting video moments, helping storytellers focus on creativity instead of searching through footage. Advertising customers can accelerate content analysis and workflow efficiency through AI-powered video understanding, helping brands more effectively connect with their audience while reducing production time and costs.

Marengo 2.7 is available in Amazon Bedrock in the US East (N. Virginia), Europe (Ireland), and Asia Pacific (Seoul) AWS Regions. Pegasus 1.2 is available in Amazon Bedrock in the US West (Oregon) and Europe (Ireland) AWS Regions via cross-region inference. To learn more, read the blog, product page, Amazon Bedrock pricing, and documentation. To get started with TwelveLabs in Amazon Bedrock, visit the Amazon Bedrock console.

 

​TwelveLabs’ Marengo 2.7 and Pegasus 1.2 multimodal foundation models are now available in Amazon Bedrock. Marengo 2.7 is a video embedding model proficient at performing tasks such as search and classification, enabling enhanced video understanding, while Pegasus 1.2 is a video language model that can generate text based on your video data. By integrating these AI capabilities, organizations can now easily search, categorize, and extract insights from video libraries without deep technical expertise. These models open up new possibilities for content discovery, analysis, and enhanced user experiences, making video intelligence more accessible across industries. Media and entertainment customers can transform production workflows by instantly finding, summarizing, and connecting video moments, helping storytellers focus on creativity instead of searching through footage. Advertising customers can accelerate content analysis and workflow efficiency through AI-powered video understanding, helping brands more effectively connect with their audience while reducing production time and costs. Marengo 2.7 is available in Amazon Bedrock in the US East (N. Virginia), Europe (Ireland), and Asia Pacific (Seoul) AWS Regions. Pegasus 1.2 is available in Amazon Bedrock in the US West (Oregon) and Europe (Ireland) AWS Regions via cross-region inference. To learn more, read the blog, product page, Amazon Bedrock pricing, and documentation. To get started with TwelveLabs in Amazon Bedrock, visit the Amazon Bedrock console.  

Today, AWS announces enhancements to its Free Tier program, offering new customers up to $200 in AWS credits to evaluate over 200 services. This program benefits a wide range of users, including cloud professionals, software developers, students, and early entrepreneurs to gain hands-on experience with AWS services, develop new skills, and build proof-of-concepts. With the new AWS Free Tier, new customers can explore AWS’s extensive portfolio of services without incurring costs, making it easier to get started with AWS.

As part of the enhanced Free Tier program, new customers receive $100 in AWS credits upon sign-up and can earn an additional $100 in credits by using services such as Amazon EC2 and Amazon Bedrock. It provides customers access to greater number of AWS services, while providing them control over the transition to paid usage. In addition to the ability to apply credits to paid services, customers continue to have access to over 30 always-free services. Additionally, the new Free Tier is integrated with AWS’s suite of Cloud Financial Management tools, making it easy to monitor and forecast future usage.

Customers can get started with the new AWS Free Tier program features by selecting the free account plan during sign-up. The free account plan expires either 6 months after sign-up or when Free Tier credits are depleted, whichever comes first. When ready, customers can easily upgrade to the paid plan with a single click to get access to more services and continue building on AWS.

The new AWS Free Tier features are generally available in all AWS Regions, except the AWS GovCloud (US) Regions and the China Regions. To learn more, visit AWS Free Tier website and AWS Free Tier documentation.

 

​Today, AWS announces enhancements to its Free Tier program, offering new customers up to $200 in AWS credits to evaluate over 200 services. This program benefits a wide range of users, including cloud professionals, software developers, students, and early entrepreneurs to gain hands-on experience with AWS services, develop new skills, and build proof-of-concepts. With the new AWS Free Tier, new customers can explore AWS’s extensive portfolio of services without incurring costs, making it easier to get started with AWS. As part of the enhanced Free Tier program, new customers receive $100 in AWS credits upon sign-up and can earn an additional $100 in credits by using services such as Amazon EC2 and Amazon Bedrock. It provides customers access to greater number of AWS services, while providing them control over the transition to paid usage. In addition to the ability to apply credits to paid services, customers continue to have access to over 30 always-free services. Additionally, the new Free Tier is integrated with AWS’s suite of Cloud Financial Management tools, making it easy to monitor and forecast future usage. Customers can get started with the new AWS Free Tier program features by selecting the free account plan during sign-up. The free account plan expires either 6 months after sign-up or when Free Tier credits are depleted, whichever comes first. When ready, customers can easily upgrade to the paid plan with a single click to get access to more services and continue building on AWS. The new AWS Free Tier features are generally available in all AWS Regions, except the AWS GovCloud (US) Regions and the China Regions. To learn more, visit AWS Free Tier website and AWS Free Tier documentation.  

Today, Amazon SageMaker launches two new capabilities designed to simplify data management and governance. The first capability, automated lakehouse onboarding, allows customers to automatically ingest metadata for datasets, such as Glue Data Catalog tables, into SageMaker Catalog. This occurs when creating a new SageMaker Unified Studio domain or by updating existing domains. It removes the need for manual IAM permissions configuration, metadata ingestion jobs, or scripts, making datasets immediately discoverable and ready for governance, analysis, and collaboration in Amazon SageMaker Unified Studio. The second capability, direct sharing enables data owners to grant access to their assets in SageMaker Catalog to other SageMaker Unified Studio projects without the need for subscription requests. This streamlines cross-team collaboration, accelerates projects, and reduces handoffs, all while maintaining robust governance.

These enhancements are generally available in all AWS Regions where Amazon SageMaker is supported, including: Asia Pacific (Tokyo), Europe (Ireland), US East (N. Virginia), US East (Ohio), US West (Oregon), Europe (Frankfurt), South America (São Paulo), Asia Pacific (Seoul), Europe (London), Asia Pacific (Singapore), Asia Pacific (Sydney), Canada (Central), Asia Pacific (Mumbai), Europe (Paris), Europe (Stockholm).

For more details, visit the Amazon SageMaker documentation.

 

​Today, Amazon SageMaker launches two new capabilities designed to simplify data management and governance. The first capability, automated lakehouse onboarding, allows customers to automatically ingest metadata for datasets, such as Glue Data Catalog tables, into SageMaker Catalog. This occurs when creating a new SageMaker Unified Studio domain or by updating existing domains. It removes the need for manual IAM permissions configuration, metadata ingestion jobs, or scripts, making datasets immediately discoverable and ready for governance, analysis, and collaboration in Amazon SageMaker Unified Studio. The second capability, direct sharing enables data owners to grant access to their assets in SageMaker Catalog to other SageMaker Unified Studio projects without the need for subscription requests. This streamlines cross-team collaboration, accelerates projects, and reduces handoffs, all while maintaining robust governance. These enhancements are generally available in all AWS Regions where Amazon SageMaker is supported, including: Asia Pacific (Tokyo), Europe (Ireland), US East (N. Virginia), US East (Ohio), US West (Oregon), Europe (Frankfurt), South America (São Paulo), Asia Pacific (Seoul), Europe (London), Asia Pacific (Singapore), Asia Pacific (Sydney), Canada (Central), Asia Pacific (Mumbai), Europe (Paris), Europe (Stockholm). For more details, visit the Amazon SageMaker documentation.  

You can now add Amazon S3 general purpose buckets to the Amazon SageMaker Catalog. This helps data scientists, engineers, and business analysts to easily discover and access datasets in Amazon S3, while giving data producers the ability to maintain granular security controls. As a result, teams get efficient access to the right datasets in S3, without any compromise on data governance.

SageMaker Catalog now allows data producers to share unstructured data in Amazon S3 general purpose buckets as assets called «S3 Object Collections». You can curate these assets using simple predefined forms, enriching them with business metadata such as the data owner, classification terms, and more. Once you publish an S3 Object Collection, consumers can discover them using SageMaker’s search and browse capabilities, and subscribe to them to receive access to data and updates over time. This feature helps you to manage permissions and governance at scale, with efficient cross-team data sharing.

This integration is available in all AWS Regions where Amazon SageMaker is available. To learn more, visit Amazon SageMaker. Get started with Amazon SageMaker and Amazon S3 by reading the documentation.

 

​You can now add Amazon S3 general purpose buckets to the Amazon SageMaker Catalog. This helps data scientists, engineers, and business analysts to easily discover and access datasets in Amazon S3, while giving data producers the ability to maintain granular security controls. As a result, teams get efficient access to the right datasets in S3, without any compromise on data governance. SageMaker Catalog now allows data producers to share unstructured data in Amazon S3 general purpose buckets as assets called «S3 Object Collections». You can curate these assets using simple predefined forms, enriching them with business metadata such as the data owner, classification terms, and more. Once you publish an S3 Object Collection, consumers can discover them using SageMaker’s search and browse capabilities, and subscribe to them to receive access to data and updates over time. This feature helps you to manage permissions and governance at scale, with efficient cross-team data sharing. This integration is available in all AWS Regions where Amazon SageMaker is available. To learn more, visit Amazon SageMaker. Get started with Amazon SageMaker and Amazon S3 by reading the documentation.  

Amazon SageMaker now offers a visual builder experience for creating and managing workflows. This feature is part of the next generation of Amazon SageMaker – the center for all your data, analytics, and AI, and is available within SageMaker Unified Studio, a single data and AI development environment. Visual workflows in Amazon SageMaker provides a drag-and-drop interface for building workflows and simplifies authoring and scheduling processes for data engineers and data scientists.

With visual workflows, you now have a visual, low-code way to represent a series of tasks, such as a data processing job that loads information into a table followed by a notebook that analyzes the loaded data. You can quickly get started by authoring your workflow visually and then continue to customize with code, if desired. Amazon SageMaker uses Amazon Managed Workflows for Apache Airflow (MWAA) to run workflows. With this new feature, you can create and modify workflows, view and adjust workflow schedules, pause or resume schedules as needed, and monitor the status and logs of workflow runs. This visual approach simplifies workflow management and provides greater ease-of-use for your data processes.

This new feature is now available in all AWS regions where Amazon SageMaker is available. Access the supported region list for the most up-to-date availability information.

To learn more, visit our Amazon SageMaker Unified Studio documentation, blog post and MWAA pricing page. 

 

​Amazon SageMaker now offers a visual builder experience for creating and managing workflows. This feature is part of the next generation of Amazon SageMaker – the center for all your data, analytics, and AI, and is available within SageMaker Unified Studio, a single data and AI development environment. Visual workflows in Amazon SageMaker provides a drag-and-drop interface for building workflows and simplifies authoring and scheduling processes for data engineers and data scientists. With visual workflows, you now have a visual, low-code way to represent a series of tasks, such as a data processing job that loads information into a table followed by a notebook that analyzes the loaded data. You can quickly get started by authoring your workflow visually and then continue to customize with code, if desired. Amazon SageMaker uses Amazon Managed Workflows for Apache Airflow (MWAA) to run workflows. With this new feature, you can create and modify workflows, view and adjust workflow schedules, pause or resume schedules as needed, and monitor the status and logs of workflow runs. This visual approach simplifies workflow management and provides greater ease-of-use for your data processes. This new feature is now available in all AWS regions where Amazon SageMaker is available. Access the supported region list for the most up-to-date availability information. To learn more, visit our Amazon SageMaker Unified Studio documentation, blog post and MWAA pricing page.   

Amazon Redshift now supports automatic refresh of materialized views that are defined on external Apache Iceberg tables in the Amazon S3 data lake.

With this update, Amazon Redshift will automatically refresh a materialized view defined on Apache Iceberg tables, that reside in the Amazon S3 bucket of the Amazon data lake, when there is new data is added or deleted to the Apache Iceberg tables.

You can start using this new capability immediately to build more complex and flexible analytics pipelines in the Amazon S3 data lake. To learn more about automatic refresh of materialized views and to get started with using materialized views in Amazon Redshift, refer to the Autorefreshing a materialized view sub-section of the Refreshing a materialized view section of the Amazon Redshift Materialized Views documentation.

 

​Amazon Redshift now supports automatic refresh of materialized views that are defined on external Apache Iceberg tables in the Amazon S3 data lake.
With this update, Amazon Redshift will automatically refresh a materialized view defined on Apache Iceberg tables, that reside in the Amazon S3 bucket of the Amazon data lake, when there is new data is added or deleted to the Apache Iceberg tables.
You can start using this new capability immediately to build more complex and flexible analytics pipelines in the Amazon S3 data lake. To learn more about automatic refresh of materialized views and to get started with using materialized views in Amazon Redshift, refer to the Autorefreshing a materialized view sub-section of the Refreshing a materialized view section of the Amazon Redshift Materialized Views documentation.