Starting today, Amazon Elastic Compute Cloud (Amazon EC2) M8g instances are available in AWS Asia Pacific (Mumbai) and AWS Asia Pacific (Hyderabad) regions. These instances are powered by AWS Graviton4 processors and deliver up to 30% better performance compared to AWS Graviton3-based instances. Amazon EC2 M8g instances are built for general-purpose workloads, such as application servers, microservices, gaming servers, midsize data stores, and caching fleets. 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 M7g 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. M8g 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 M8g 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) M8g instances are available in AWS Asia Pacific (Mumbai) and AWS Asia Pacific (Hyderabad) regions. These instances are powered by AWS Graviton4 processors and deliver up to 30% better performance compared to AWS Graviton3-based instances. Amazon EC2 M8g instances are built for general-purpose workloads, such as application servers, microservices, gaming servers, midsize data stores, and caching fleets. 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 M7g 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. M8g 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 M8g 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.  

Today, Amazon introduces Amazon Nova Sonic, a new foundation model that unifies speech understanding and generation into a single model, to enable human-like voice conversations in artificial intelligence (AI) applications. Amazon Nova Sonic enables developers to build real-time conversational AI applications in Amazon Bedrock, with industry-leading price performance and low latency. It can understand speech in different speaking styles and generate speech in expressive voices, including both masculine-sounding and feminine-sounding voices, in English accents including American and British. Amazon Nova Sonic’s novel architecture can adapt the intonation, prosody, and style of the generated speech response to align with the context and content of the speech input. Additionally, Amazon Nova Sonic allows for function calling and knowledge grounding with enterprise data using Retrieval-Augmented Generation (RAG). Amazon Nova Sonic is developed with responsible AI in mind and features built-in protections including content moderation and watermarking.

To help developers build real-time application with Amazon Nova Sonic, AWS is also announcing the launch of a new bidirectional streaming API in Amazon Bedrock. This API enables two-way streaming of content, which is critical for low latency interactive communication between a human user and the AI model.     

Amazon Nova Sonic can be used to voice-enable virtually any application. It has been extensively tested for a wide range of applications, including enabling customer service call automation at contact centers, outbound marketing, voice-enabled personal assistants and agents, and interactive education and language learning.

The Amazon Nova Sonic model is now available in Amazon Bedrock in the US East (N. Virginia) AWS Region. To learn more, read the AWS News Blog, Amazon Nova Sonic product page, and Amazon Nova Sonic User Guide. To get started with the Amazon Nova Sonic in Amazon Bedrock, visit the Amazon Bedrock console.

 

​Today, Amazon introduces Amazon Nova Sonic, a new foundation model that unifies speech understanding and generation into a single model, to enable human-like voice conversations in artificial intelligence (AI) applications. Amazon Nova Sonic enables developers to build real-time conversational AI applications in Amazon Bedrock, with industry-leading price performance and low latency. It can understand speech in different speaking styles and generate speech in expressive voices, including both masculine-sounding and feminine-sounding voices, in English accents including American and British. Amazon Nova Sonic’s novel architecture can adapt the intonation, prosody, and style of the generated speech response to align with the context and content of the speech input. Additionally, Amazon Nova Sonic allows for function calling and knowledge grounding with enterprise data using Retrieval-Augmented Generation (RAG). Amazon Nova Sonic is developed with responsible AI in mind and features built-in protections including content moderation and watermarking.
To help developers build real-time application with Amazon Nova Sonic, AWS is also announcing the launch of a new bidirectional streaming API in Amazon Bedrock. This API enables two-way streaming of content, which is critical for low latency interactive communication between a human user and the AI model.     
Amazon Nova Sonic can be used to voice-enable virtually any application. It has been extensively tested for a wide range of applications, including enabling customer service call automation at contact centers, outbound marketing, voice-enabled personal assistants and agents, and interactive education and language learning.
The Amazon Nova Sonic model is now available in Amazon Bedrock in the US East (N. Virginia) AWS Region. To learn more, read the AWS News Blog, Amazon Nova Sonic product page, and Amazon Nova Sonic User Guide. To get started with the Amazon Nova Sonic in Amazon Bedrock, visit the Amazon Bedrock console.  

Starting today, Amazon Elastic Compute Cloud (Amazon EC2) C6in instances are available in AWS Region Asia Pacific (Osaka). These sixth-generation network optimized instances, powered by 3rd Generation Intel Xeon Scalable processors and built on the AWS Nitro System, deliver up to 200Gbps network bandwidth, for 2x more network bandwidth over comparable fifth-generation instances.

Customers can use C6in instances to scale the performance of applications such as network virtual appliances (firewalls, virtual routers, load balancers), Telco 5G User Plane Function (UPF), data analytics, high performance computing (HPC), and CPU based AI/ML workloads. C6in instances are available in 10 different sizes with up to 128 vCPUs, including bare metal size. Amazon EC2 sixth-generation x86-based network optimized EC2 instances deliver up to 100Gbps of Amazon Elastic Block Store (Amazon EBS) bandwidth, and up to 400K IOPS. C6in instances offer Elastic Fabric Adapter (EFA) networking support on 32xlarge and metal sizes.

C6in instances are available in these AWS Regions: US East (Ohio, N. Virginia), US West (N. California, Oregon), Europe (Frankfurt, Ireland, London, Milan, Paris, Spain, Stockholm, Zurich), Middle East (Bahrain, UAE), Israel (Tel Aviv), Asia Pacific (Hong Kong, Hyderabad, Jakarta, Malaysia, Melbourne, Mumbai, Osaka, Seoul, Singapore, Sydney, Tokyo), Africa (Cape Town), South America (Sao Paulo), Canada (Central), and AWS GovCloud (US-West, US-East). To learn more, see the Amazon EC2 C6in instances. To get started, see the AWS Management Console, AWS Command Line Interface (AWS CLI), and AWS SDKs.

 

​Starting today, Amazon Elastic Compute Cloud (Amazon EC2) C6in instances are available in AWS Region Asia Pacific (Osaka). These sixth-generation network optimized instances, powered by 3rd Generation Intel Xeon Scalable processors and built on the AWS Nitro System, deliver up to 200Gbps network bandwidth, for 2x more network bandwidth over comparable fifth-generation instances. Customers can use C6in instances to scale the performance of applications such as network virtual appliances (firewalls, virtual routers, load balancers), Telco 5G User Plane Function (UPF), data analytics, high performance computing (HPC), and CPU based AI/ML workloads. C6in instances are available in 10 different sizes with up to 128 vCPUs, including bare metal size. Amazon EC2 sixth-generation x86-based network optimized EC2 instances deliver up to 100Gbps of Amazon Elastic Block Store (Amazon EBS) bandwidth, and up to 400K IOPS. C6in instances offer Elastic Fabric Adapter (EFA) networking support on 32xlarge and metal sizes. C6in instances are available in these AWS Regions: US East (Ohio, N. Virginia), US West (N. California, Oregon), Europe (Frankfurt, Ireland, London, Milan, Paris, Spain, Stockholm, Zurich), Middle East (Bahrain, UAE), Israel (Tel Aviv), Asia Pacific (Hong Kong, Hyderabad, Jakarta, Malaysia, Melbourne, Mumbai, Osaka, Seoul, Singapore, Sydney, Tokyo), Africa (Cape Town), South America (Sao Paulo), Canada (Central), and AWS GovCloud (US-West, US-East). To learn more, see the Amazon EC2 C6in instances. To get started, see the AWS Management Console, AWS Command Line Interface (AWS CLI), and AWS SDKs.  

The first models in the new Llama 4 herd of models—Llama 4 Scout 17B and Llama 4 Maverick 17B—are now available on AWS. You can access Llama 4 models in Amazon SageMaker JumpStart. These advanced multimodal models empower you to build more tailored applications that respond to multiple types of media. Llama 4 offers improved performance at lower cost compared to Llama 3, with expanded language support for global applications. Featuring mixture-of-experts (MoE) architecture, these models deliver efficient multimodal processing for text and image inputs, improved compute efficiency, and enhanced AI safety measures.

According to Meta, the smaller Llama 4 Scout 17B model, is the best multimodal model in the world in its class, and is more powerful than Meta’s Llama 3 models. Scout is a general-purpose model with 17 billion active parameters, 16 experts, and 109 billion total parameters that delivers state-of-the-art performance for its class. Scout significantly increases the context length from 128K in Llama 3, to an industry leading 10 million tokens. This opens up a world of possibilities, including multi-document summarization, parsing extensive user activity for personalized tasks, and reasoning over vast code bases. Llama 4 Maverick 17B is a general-purpose model that comes in both quantized (FP8) and non-quantized (BF16) versions, featuring 128 experts, 400 billion total parameters, and a 1 million context length. It excels in image and text understanding across 12 languages, making it suitable for versatile assistant and chat applications.

Meta’s Llama 4 models are available in Amazon SageMaker JumpStart in the US East (N. Virginia) AWS Region. To learn more, read the launch blog and technical blog. These models can be accessed in the Amazon SageMaker Studio.

 

​The first models in the new Llama 4 herd of models—Llama 4 Scout 17B and Llama 4 Maverick 17B—are now available on AWS. You can access Llama 4 models in Amazon SageMaker JumpStart. These advanced multimodal models empower you to build more tailored applications that respond to multiple types of media. Llama 4 offers improved performance at lower cost compared to Llama 3, with expanded language support for global applications. Featuring mixture-of-experts (MoE) architecture, these models deliver efficient multimodal processing for text and image inputs, improved compute efficiency, and enhanced AI safety measures. According to Meta, the smaller Llama 4 Scout 17B model, is the best multimodal model in the world in its class, and is more powerful than Meta’s Llama 3 models. Scout is a general-purpose model with 17 billion active parameters, 16 experts, and 109 billion total parameters that delivers state-of-the-art performance for its class. Scout significantly increases the context length from 128K in Llama 3, to an industry leading 10 million tokens. This opens up a world of possibilities, including multi-document summarization, parsing extensive user activity for personalized tasks, and reasoning over vast code bases. Llama 4 Maverick 17B is a general-purpose model that comes in both quantized (FP8) and non-quantized (BF16) versions, featuring 128 experts, 400 billion total parameters, and a 1 million context length. It excels in image and text understanding across 12 languages, making it suitable for versatile assistant and chat applications. Meta’s Llama 4 models are available in Amazon SageMaker JumpStart in the US East (N. Virginia) AWS Region. To learn more, read the launch blog and technical blog. These models can be accessed in the Amazon SageMaker Studio.  

AWS Serverless Application Model (AWS SAM) now supports custom domain names for private REST APIs feature of Amazon API Gateway. Developers building serverless applications using SAM can now seamlessly incorporate custom domain names for private APIs directly in their SAM templates, eliminating the need to configure custom domain names separately using other tools.

API Gateway allows you to create a custom domain name, like private.example.com, for your private REST APIs, enabling you to provide API callers with a simpler and intuitive URL. With a private custom domain name, you can reduce complexity, configure security measures with TLS encryption, and manage the lifecycle of the TLS certificate associated with your domain name. AWS SAM is a collection of open-source tools (e.g. SAM, SAM CLI) that make it easy for you to build and manage serverless applications through the authoring, building, deploying, testing, and monitoring phases of your development lifecycle. This launch enables you to easily configure custom domain names for your private REST APIs using SAM and SAM CLI.

To get started, update SAM CLI to the latest version and modify your SAM template to set the EndpointConfiguration to PRIVATE and specify a policy document in the Policy field in the Domain property of the AWS::Serverless::Api resource. SAM will then automatically generate DomainNameV2 and BasePathMappingV2 resources under AWS::Serverless::Api. To learn more, visit the AWS SAM documentation. You can learn more about custom domain name for private REST APIs in API Gateway blog post.
 

 

​AWS Serverless Application Model (AWS SAM) now supports custom domain names for private REST APIs feature of Amazon API Gateway. Developers building serverless applications using SAM can now seamlessly incorporate custom domain names for private APIs directly in their SAM templates, eliminating the need to configure custom domain names separately using other tools. API Gateway allows you to create a custom domain name, like private.example.com, for your private REST APIs, enabling you to provide API callers with a simpler and intuitive URL. With a private custom domain name, you can reduce complexity, configure security measures with TLS encryption, and manage the lifecycle of the TLS certificate associated with your domain name. AWS SAM is a collection of open-source tools (e.g. SAM, SAM CLI) that make it easy for you to build and manage serverless applications through the authoring, building, deploying, testing, and monitoring phases of your development lifecycle. This launch enables you to easily configure custom domain names for your private REST APIs using SAM and SAM CLI. To get started, update SAM CLI to the latest version and modify your SAM template to set the EndpointConfiguration to PRIVATE and specify a policy document in the Policy field in the Domain property of the AWS::Serverless::Api resource. SAM will then automatically generate DomainNameV2 and BasePathMappingV2 resources under AWS::Serverless::Api. To learn more, visit the AWS SAM documentation. You can learn more about custom domain name for private REST APIs in API Gateway blog post.    

Amazon Bedrock Guardrails announces new capabilities to safely build generative AI applications at scale. These new capabilities offer grater flexibility, finer-grained control, and ease of use while using the configurable safeguards provided by Bedrock Guardrails aligning with use cases and responsible AI policies.

Bedrock Guardrails now offers a detect mode that provides a preview of the expected results from your configured policies allowing you to evaluate the effectiveness of the safeguards before deploying them. This enables faster iteration and accelerating time-to-product with different combinations and strengths of policies, allowing you to fine-tune your guardrails before deployment.

Guardrails now offers more configurability with options to enable policies on input prompts, model responses, or both prompts and responses – a significant improvement over the previous default setting where policies were automatically applied to both inputs and outputs. Providing finer-grained control enables you to selectively apply the safeguards to make them work for you.

Bedrock Guardrails offers sensitive information filters that detect personally identifiable information (PIIs) with two modes: Block where requests containing sensitive information are completely blocked, and Mask where sensitive information is redacted and replaced with identifier tags. You can now use either of these two modes for both input prompts and model responses giving you flexibility and ease of use to safely build generative AI applications at scale.

These new capabilities are available in all AWS regions where Amazon Bedrock Guardrails is supported.

To learn more, see the blog post, technical documentation and the Bedrock Guardrails product page.

 

​Amazon Bedrock Guardrails announces new capabilities to safely build generative AI applications at scale. These new capabilities offer grater flexibility, finer-grained control, and ease of use while using the configurable safeguards provided by Bedrock Guardrails aligning with use cases and responsible AI policies. Bedrock Guardrails now offers a detect mode that provides a preview of the expected results from your configured policies allowing you to evaluate the effectiveness of the safeguards before deploying them. This enables faster iteration and accelerating time-to-product with different combinations and strengths of policies, allowing you to fine-tune your guardrails before deployment. Guardrails now offers more configurability with options to enable policies on input prompts, model responses, or both prompts and responses – a significant improvement over the previous default setting where policies were automatically applied to both inputs and outputs. Providing finer-grained control enables you to selectively apply the safeguards to make them work for you. Bedrock Guardrails offers sensitive information filters that detect personally identifiable information (PIIs) with two modes: Block where requests containing sensitive information are completely blocked, and Mask where sensitive information is redacted and replaced with identifier tags. You can now use either of these two modes for both input prompts and model responses giving you flexibility and ease of use to safely build generative AI applications at scale. These new capabilities are available in all AWS regions where Amazon Bedrock Guardrails is supported. To learn more, see the blog post, technical documentation and the Bedrock Guardrails product page.  

At re:Invent 2024, AWS announced the preview of prompt caching, a new capability that can reduce costs by up to 90% and latency by up to 85% by caching frequently used prompts across multiple API calls. Today, AWS is launching prompt caching in generally availability on Amazon Bedrock.

Prompt caching allows you to cache repetitive inputs and avoid reprocessing context such as long system prompts and common examples that help guide the model’s response. When you use prompt caching, fewer computing resources are needed to process your inputs. As a result, not only can we process your request faster, but we can also pass along the cost savings.

Amazon Bedrock is a fully managed service that offers a choice of high-performing FMs from leading AI companies via a single API. Amazon Bedrock also provides a broad set of capabilities customers need to build generative AI applications with security, privacy, and responsible AI capabilities built in. These capabilities help you build tailored applications for multiple use cases across different industries, helping organizations unlock sustained growth from generative AI while providing tools to build customer trust and data governance.

Prompt caching is now generally available for Anthropic’s Claude 3.5 Haiku and Claude 3.7 Sonnet, Nova Micro, Nova Lite, and Nova Pro models. Customers who were given access to Claude 3.5 Sonnet v2 during the prompt caching preview will retain their access, however no additional customers will be granted access to prompt caching on the Claude 3.5 Sonnet v2 model. For regional availability or to learn more about prompt caching, please see our documentation and blog.

 

​At re:Invent 2024, AWS announced the preview of prompt caching, a new capability that can reduce costs by up to 90% and latency by up to 85% by caching frequently used prompts across multiple API calls. Today, AWS is launching prompt caching in generally availability on Amazon Bedrock. Prompt caching allows you to cache repetitive inputs and avoid reprocessing context such as long system prompts and common examples that help guide the model’s response. When you use prompt caching, fewer computing resources are needed to process your inputs. As a result, not only can we process your request faster, but we can also pass along the cost savings. Amazon Bedrock is a fully managed service that offers a choice of high-performing FMs from leading AI companies via a single API. Amazon Bedrock also provides a broad set of capabilities customers need to build generative AI applications with security, privacy, and responsible AI capabilities built in. These capabilities help you build tailored applications for multiple use cases across different industries, helping organizations unlock sustained growth from generative AI while providing tools to build customer trust and data governance. Prompt caching is now generally available for Anthropic’s Claude 3.5 Haiku and Claude 3.7 Sonnet, Nova Micro, Nova Lite, and Nova Pro models. Customers who were given access to Claude 3.5 Sonnet v2 during the prompt caching preview will retain their access, however no additional customers will be granted access to prompt caching on the Claude 3.5 Sonnet v2 model. For regional availability or to learn more about prompt caching, please see our documentation and blog.  

Starting today, Amazon Elastic Compute Cloud (Amazon EC2) M7i and R7i instances powered by custom 4th Gen Intel Xeon Scalable processors (code-named Sapphire Rapids) are available in Asia Pacific (Melbourne) region. These custom processors, available only on AWS, offer up to 15% better performance over comparable x86-based Intel processors utilized by other cloud providers.

M7i and R7i deliver up to 15% better price-performance compared to M6i and R6i instances respectively. They offer larger instance sizes up to 48xlarge, can attach up to 128 EBS volumes and two bare metal sizes (metal-24xl, metal-48xl). These bare-metal sizes support built-in Intel accelerators: Data Streaming Accelerator, In-Memory Analytics Accelerator, and QuickAssist Technology that are used to facilitate efficient offload and acceleration of data operations and optimize performance for workloads.

In addition, all two instance types support the new Intel Advanced Matrix Extensions (AMX) that accelerate matrix multiplication operations for applications such as CPU-based ML.

To learn more, visit Amazon EC2 M7i, R7i instance pages.

 

​Starting today, Amazon Elastic Compute Cloud (Amazon EC2) M7i and R7i instances powered by custom 4th Gen Intel Xeon Scalable processors (code-named Sapphire Rapids) are available in Asia Pacific (Melbourne) region. These custom processors, available only on AWS, offer up to 15% better performance over comparable x86-based Intel processors utilized by other cloud providers. M7i and R7i deliver up to 15% better price-performance compared to M6i and R6i instances respectively. They offer larger instance sizes up to 48xlarge, can attach up to 128 EBS volumes and two bare metal sizes (metal-24xl, metal-48xl). These bare-metal sizes support built-in Intel accelerators: Data Streaming Accelerator, In-Memory Analytics Accelerator, and QuickAssist Technology that are used to facilitate efficient offload and acceleration of data operations and optimize performance for workloads. In addition, all two instance types support the new Intel Advanced Matrix Extensions (AMX) that accelerate matrix multiplication operations for applications such as CPU-based ML. To learn more, visit Amazon EC2 M7i, R7i instance pages.  

AWS Transfer Family now enables you to delete, rename, or move files on remote SFTP servers using SFTP connectors. This enhancement allows you to easily manage files and directories stored on remote SFTP file systems and keep them up to date.

SFTP connectors provide fully managed and low-code capability to copy files between remote SFTP servers and Amazon S3. You can now organize your remote directories by deleting, renaming or moving source files to archive locations after they have been copied from the remote server. This helps you ensure that your remote directories remain current and prevent duplicate file transfers during subsequent job runs. These new features add to the existing SFTP connector functionalities, including capabilities to list remote directory contents and execute bidirectional file transfers.

SFTP connectors support for deleting, renaming and moving files on remote SFTP servers is available in all AWS Regions where AWS Transfer Family is available. To learn more about SFTP connectors, take the self paced workshop or visit the documentation. For information on pricing, see AWS Transfer Family pricing.
 

 

​AWS Transfer Family now enables you to delete, rename, or move files on remote SFTP servers using SFTP connectors. This enhancement allows you to easily manage files and directories stored on remote SFTP file systems and keep them up to date. SFTP connectors provide fully managed and low-code capability to copy files between remote SFTP servers and Amazon S3. You can now organize your remote directories by deleting, renaming or moving source files to archive locations after they have been copied from the remote server. This helps you ensure that your remote directories remain current and prevent duplicate file transfers during subsequent job runs. These new features add to the existing SFTP connector functionalities, including capabilities to list remote directory contents and execute bidirectional file transfers. SFTP connectors support for deleting, renaming and moving files on remote SFTP servers is available in all AWS Regions where AWS Transfer Family is available. To learn more about SFTP connectors, take the self paced workshop or visit the documentation. For information on pricing, see AWS Transfer Family pricing.    

AWS CodeBuild now supports enhanced debugging experience through secure and isolated sandbox environments. You can connect to the sandbox environment via SSH clients or from an IDE to interactively troubleshoot your build or test runs. AWS CodeBuild is a fully managed continuous integration service that compiles source code, runs tests, and produces software packages ready for deployment.

With the enhanced debugging capability, you can investigate issues and validate the fixes in real-time, before committing changes to your buildspec configurations. The sandbox environment maintains a persistent file system during the debugging session, and offers the same native integration with source providers and AWS services as your build environment.

The enhanced debugging experience through sandbox environments is available in all AWS Regions where CodeBuild is offered. To learn more, please visit our documentation and pricing information. To get started with CodeBuild, visit the AWS CodeBuild product page.
 

 

​AWS CodeBuild now supports enhanced debugging experience through secure and isolated sandbox environments. You can connect to the sandbox environment via SSH clients or from an IDE to interactively troubleshoot your build or test runs. AWS CodeBuild is a fully managed continuous integration service that compiles source code, runs tests, and produces software packages ready for deployment. With the enhanced debugging capability, you can investigate issues and validate the fixes in real-time, before committing changes to your buildspec configurations. The sandbox environment maintains a persistent file system during the debugging session, and offers the same native integration with source providers and AWS services as your build environment. The enhanced debugging experience through sandbox environments is available in all AWS Regions where CodeBuild is offered. To learn more, please visit our documentation and pricing information. To get started with CodeBuild, visit the AWS CodeBuild product page.