Amazon Bedrock now supports reinforcement fine-tuning, helping you improve model accuracy without needing deep machine learning expertise or large sums of labeled data. Amazon Bedrock automates the reinforcement fine-tuning workflow, making this advanced model customization technique accessible to everyday developers. Models learn to align with your specific requirements using a small set of prompts rather than the large sums of data needed for traditional fine-tuning methods, enabling teams to get started quickly. This capability teaches models through feedback on multiple possible responses to the same prompt, improving their judgement of what makes a good response. Reinforcement fine-tuning in Amazon Bedrock delivers 66% accuracy gains on average over base models so you can use smaller, faster, and more cost-effective model variants while maintaining high quality.

Organizations struggle to adapt AI models to their unique business needs, forcing them to choose between generic models with average performance or expensive, complex customization that requires specialized talent, infrastructure, and risky data movement. Reinforcement fine-tuning in Amazon Bedrock removes this complexity by making advanced model customization fast, automated, and secure. You can train models by uploading training data directly from your computer or choose from datasets already stored in Amazon S3, eliminating the need for any labeled datasets. You can define reward functions using verifiable rule-based graders or AI-based judges along with built-in templates to optimize your models for both objective tasks such as code generation or math reasoning, and subjective tasks such as instruction following or chatbot interactions. Your proprietary data never leaves AWS’s secure, governed environment during the entire customization process, mitigating security and compliance concerns.

You can get started with reinforcement fine-tuning in Amazon Bedrock through the Amazon Bedrock console and via the Amazon Bedrock APIs. At launch, you can use reinforcement fine-tuning with Amazon Nova 2 Lite with support for additional models coming soon. To learn more about reinforcement fine-tuning in Amazon Bedrock, read the launch blog, pricing page, and documentation.

 

​Amazon Bedrock now supports reinforcement fine-tuning, helping you improve model accuracy without needing deep machine learning expertise or large sums of labeled data. Amazon Bedrock automates the reinforcement fine-tuning workflow, making this advanced model customization technique accessible to everyday developers. Models learn to align with your specific requirements using a small set of prompts rather than the large sums of data needed for traditional fine-tuning methods, enabling teams to get started quickly. This capability teaches models through feedback on multiple possible responses to the same prompt, improving their judgement of what makes a good response. Reinforcement fine-tuning in Amazon Bedrock delivers 66% accuracy gains on average over base models so you can use smaller, faster, and more cost-effective model variants while maintaining high quality.
Organizations struggle to adapt AI models to their unique business needs, forcing them to choose between generic models with average performance or expensive, complex customization that requires specialized talent, infrastructure, and risky data movement. Reinforcement fine-tuning in Amazon Bedrock removes this complexity by making advanced model customization fast, automated, and secure. You can train models by uploading training data directly from your computer or choose from datasets already stored in Amazon S3, eliminating the need for any labeled datasets. You can define reward functions using verifiable rule-based graders or AI-based judges along with built-in templates to optimize your models for both objective tasks such as code generation or math reasoning, and subjective tasks such as instruction following or chatbot interactions. Your proprietary data never leaves AWS’s secure, governed environment during the entire customization process, mitigating security and compliance concerns.
You can get started with reinforcement fine-tuning in Amazon Bedrock through the Amazon Bedrock console and via the Amazon Bedrock APIs. At launch, you can use reinforcement fine-tuning with Amazon Nova 2 Lite with support for additional models coming soon. To learn more about reinforcement fine-tuning in Amazon Bedrock, read the launch blog, pricing page, and documentation.  

Amazon SageMaker HyperPod now supports elastic training, enabling organizations to accelerate foundation model training by automatically scaling training workloads based on resource availability and workload priorities. This represents a fundamental shift from training with a fixed set of resources, as it saves hours of engineering time spent reconfiguring training jobs based on compute availability.

Any change in compute availability previously required manually halting training, reconfiguring training parameters, and restarting jobs—a process that requires distributed training expertise and leaves expensive AI accelerators sitting idle during training job reconfiguration. Elastic training automatically expands training jobs to absorb idle AI accelerators and seamlessly contracting when higher-priority workloads need resources—all without halting training entirely.

By eliminating manual reconfiguration overhead and ensuring continuous utilization of available compute, elastic training can help save time previously spent on infrastructure management, reduce costs by maximizing cluster utilization, and accelerate time-to-market. Training can start immediately with minimal resources and grow opportunistically as capacity becomes available.

SageMaker HyperPod is available in all regions where Amazon SageMaker HyperPod is currently available. Organizations can enable elastic training with zero code changes using HyperPod recipes for publicly available models including Llama and GPT OSS. For custom model architectures, customers can integrate elastic training capabilities through lightweight configuration updates and minimal code modifications, making it accessible to teams without requiring distributed systems expertise.

To get started, visit the Amazon SageMaker HyperPod product page and see the elastic training documentation for implementation guidance.

 

​Amazon SageMaker HyperPod now supports elastic training, enabling organizations to accelerate foundation model training by automatically scaling training workloads based on resource availability and workload priorities. This represents a fundamental shift from training with a fixed set of resources, as it saves hours of engineering time spent reconfiguring training jobs based on compute availability.
Any change in compute availability previously required manually halting training, reconfiguring training parameters, and restarting jobs—a process that requires distributed training expertise and leaves expensive AI accelerators sitting idle during training job reconfiguration. Elastic training automatically expands training jobs to absorb idle AI accelerators and seamlessly contracting when higher-priority workloads need resources—all without halting training entirely.
By eliminating manual reconfiguration overhead and ensuring continuous utilization of available compute, elastic training can help save time previously spent on infrastructure management, reduce costs by maximizing cluster utilization, and accelerate time-to-market. Training can start immediately with minimal resources and grow opportunistically as capacity becomes available.
SageMaker HyperPod is available in all regions where Amazon SageMaker HyperPod is currently available. Organizations can enable elastic training with zero code changes using HyperPod recipes for publicly available models including Llama and GPT OSS. For custom model architectures, customers can integrate elastic training capabilities through lightweight configuration updates and minimal code modifications, making it accessible to teams without requiring distributed systems expertise.
To get started, visit the Amazon SageMaker HyperPod product page and see the elastic training documentation for implementation guidance.  

Starting today, new general purpose high-frequency high-network Amazon Elastic Compute Cloud (Amazon EC2) M8azn instances are available for preview. These instances are powered by fifth generation AMD EPYC (formerly code named Turin) processors, offering the highest maximum CPU frequency, 5GHz in the cloud. The M8azn instances offer up to 2x compute performance versus previous generation M5zn instances. These instances also deliver 24% higher performance than M8a instances.

M8azn instances are built on the AWS Nitro System, a collection of hardware and software innovations designed by AWS. The AWS Nitro System enables the delivery of efficient, flexible, and secure cloud services with isolated multitenancy, private networking, and fast local storage. These instances are ideal for applications such as gaming, high-performance computing, high-frequency trading (HFT), CI/CD, and simulation modeling for the automotive, aerospace, energy, and telecommunication industries.

To learn more or request access to the M8azn instances preview, visit the Amazon EC2 M8a page.

 

​Starting today, new general purpose high-frequency high-network Amazon Elastic Compute Cloud (Amazon EC2) M8azn instances are available for preview. These instances are powered by fifth generation AMD EPYC (formerly code named Turin) processors, offering the highest maximum CPU frequency, 5GHz in the cloud. The M8azn instances offer up to 2x compute performance versus previous generation M5zn instances. These instances also deliver 24% higher performance than M8a instances. M8azn instances are built on the AWS Nitro System, a collection of hardware and software innovations designed by AWS. The AWS Nitro System enables the delivery of efficient, flexible, and secure cloud services with isolated multitenancy, private networking, and fast local storage. These instances are ideal for applications such as gaming, high-performance computing, high-frequency trading (HFT), CI/CD, and simulation modeling for the automotive, aerospace, energy, and telecommunication industries. To learn more or request access to the M8azn instances preview, visit the Amazon EC2 M8a page.  

AWS announces the Apache Spark upgrade agent, a new capability that accelerates Apache Spark version upgrades for Amazon EMR on EC2 and EMR Serverless. The agent converts complex upgrade processes that typically take months into projects spanning weeks through automated code analysis and transformation. Organizations invest substantial engineering resources analyzing API changes, resolving conflicts, and validating applications during Spark upgrades. The agent introduces conversational interfaces where engineers express upgrade requirements in natural language, while maintaining full control over code modifications.

The Apache Spark upgrade agent automatically identifies API changes and behavioral modifications across PySpark and Scala applications. Engineers can initiate upgrades directly from SageMaker Unified Studio, Kiro CLI or IDE of their choice with the help of MCP (Model Context Protocol) compatibility. During the upgrade process, the agent analyzes existing code and suggests specific changes, and engineers can review and approve before implementation. The agent validates functional correctness through data quality validations. The agent currently supports upgrades from Spark 2.4 to 3.5 and maintains data processing accuracy throughout the upgrade process.

The Apache Spark upgrade agent is now available in all AWS Regions where SageMaker Unified Studio is available. To start using the agent, visit SageMaker Unified Studio and select IDE Spaces or install the Kiro CLI. For detailed implementation guidance, reference documentation, and migration examples, visit the documentation.

 

​AWS announces the Apache Spark upgrade agent, a new capability that accelerates Apache Spark version upgrades for Amazon EMR on EC2 and EMR Serverless. The agent converts complex upgrade processes that typically take months into projects spanning weeks through automated code analysis and transformation. Organizations invest substantial engineering resources analyzing API changes, resolving conflicts, and validating applications during Spark upgrades. The agent introduces conversational interfaces where engineers express upgrade requirements in natural language, while maintaining full control over code modifications. The Apache Spark upgrade agent automatically identifies API changes and behavioral modifications across PySpark and Scala applications. Engineers can initiate upgrades directly from SageMaker Unified Studio, Kiro CLI or IDE of their choice with the help of MCP (Model Context Protocol) compatibility. During the upgrade process, the agent analyzes existing code and suggests specific changes, and engineers can review and approve before implementation. The agent validates functional correctness through data quality validations. The agent currently supports upgrades from Spark 2.4 to 3.5 and maintains data processing accuracy throughout the upgrade process. The Apache Spark upgrade agent is now available in all AWS Regions where SageMaker Unified Studio is available. To start using the agent, visit SageMaker Unified Studio and select IDE Spaces or install the Kiro CLI. For detailed implementation guidance, reference documentation, and migration examples, visit the documentation.  

Today, Amazon announces the availability of Amazon Nova 2 Sonic, our speech-to-speech model for natural, real-time conversational AI that delivers industry leading quality and price for voice-based conversational AI. It offers best-in-class streaming speech understanding with robustness to background noise and users’ speaking styles, efficient dialog handling, and speech generation with expressive voices that can speak natively in multiple languages (Polyglot voices). It has superior reasoning, instruction following, and tool invocation accuracy over the previous model.

Nova 2 Sonic builds on the capabilities introduced in the original Nova Sonic model with new features including expanded language support (Portuguese and Hindi), polyglot voices that enable the model to speak different languages with native expressivity using the same voice, and turn-taking controllability to allow developers to set low, medium, or high pause sensitivity. The model also adds cross-modal interaction, allowing users to seamlessly switch between voice and text in the same session, asynchronous tool calling to support multi-step tasks without interrupting conversation flow, and a one-million token context window for sustained interactions.

Developers can integrate Nova Sonic 2 directly into real-time voice systems using Amazon Bedrock’s bidirectional streaming API. Nova Sonic 2 now also seamlessly integrates with Amazon Connect and other leading telephony providers, including Vonage, Twilio, and AudioCodes, as well as open source frameworks such as LiveKit and Pipecat.

Amazon Nova 2 Sonic is available in Amazon Bedrock in the following AWS Regions: US East (N. Virginia), US West (Oregon), Asia Pacific (Tokyo), and Europe (Stockholm). To learn more, read the AWS News Blog and the Amazon Nova Sonic User Guide. To get started with Nova Sonic 2 in Amazon Bedrock, visit the Amazon Bedrock console.

 

​Today, Amazon announces the availability of Amazon Nova 2 Sonic, our speech-to-speech model for natural, real-time conversational AI that delivers industry leading quality and price for voice-based conversational AI. It offers best-in-class streaming speech understanding with robustness to background noise and users’ speaking styles, efficient dialog handling, and speech generation with expressive voices that can speak natively in multiple languages (Polyglot voices). It has superior reasoning, instruction following, and tool invocation accuracy over the previous model.
Nova 2 Sonic builds on the capabilities introduced in the original Nova Sonic model with new features including expanded language support (Portuguese and Hindi), polyglot voices that enable the model to speak different languages with native expressivity using the same voice, and turn-taking controllability to allow developers to set low, medium, or high pause sensitivity. The model also adds cross-modal interaction, allowing users to seamlessly switch between voice and text in the same session, asynchronous tool calling to support multi-step tasks without interrupting conversation flow, and a one-million token context window for sustained interactions.
Developers can integrate Nova Sonic 2 directly into real-time voice systems using Amazon Bedrock’s bidirectional streaming API. Nova Sonic 2 now also seamlessly integrates with Amazon Connect and other leading telephony providers, including Vonage, Twilio, and AudioCodes, as well as open source frameworks such as LiveKit and Pipecat.
Amazon Nova 2 Sonic is available in Amazon Bedrock in the following AWS Regions: US East (N. Virginia), US West (Oregon), Asia Pacific (Tokyo), and Europe (Stockholm). To learn more, read the AWS News Blog and the Amazon Nova Sonic User Guide. To get started with Nova Sonic 2 in Amazon Bedrock, visit the Amazon Bedrock console.  

Amazon S3 Storage Lens provides organization-wide visibility into your storage usage and activity to help optimize costs, improve performance, and strengthen data protection. Today, we are adding three new capabilities to S3 Storage Lens that give you deeper insights into your S3 storage usage and application performance: performance metrics that provide insights into how your applications interact with S3 data, analytics for billions of prefixes in your buckets, and metrics export directly to S3 Tables for easier querying and analysis.

We are adding three specific types of performance metrics. Access pattern metrics identify inefficient requests, including those that are too small and create unnecessary network overhead. Request origin metrics, such as cross-Region request counts, show when applications access data across regions, impacting latency and costs. Object access count metrics reveal when applications frequently read a small subset of objects that could be optimized through caching or moving to high-performance storage.

We are expanding the prefix analytics in S3 Storage Lens to enable analyzing billions of prefixes per bucket, whereas previously metrics were limited to the largest prefixes that met minimum size and depth thresholds. This gives you visibility into storage usage and activity across all your prefixes. Finally, we are making it possible to export metrics directly to managed S3 Tables, making them immediately available for querying with AWS analytics services like Amazon QuickSight and enabling you to join this data with other AWS service data for deeper insights.

To get started, enable performance metrics or expanded prefixes in your S3 Storage Lens advanced metrics dashboard configuration. These capabilities are available in all AWS Regions, except for AWS China Regions and AWS GovCloud (US) Regions. You can enable metrics export to managed S3 Tables in both free and advanced dashboard configurations in AWS Regions where S3 Tables are available. To learn more, visit the S3 Storage Lens overview page, documentation, S3 pricing page, and read the AWS News Blog.

 

​Amazon S3 Storage Lens provides organization-wide visibility into your storage usage and activity to help optimize costs, improve performance, and strengthen data protection. Today, we are adding three new capabilities to S3 Storage Lens that give you deeper insights into your S3 storage usage and application performance: performance metrics that provide insights into how your applications interact with S3 data, analytics for billions of prefixes in your buckets, and metrics export directly to S3 Tables for easier querying and analysis. We are adding three specific types of performance metrics. Access pattern metrics identify inefficient requests, including those that are too small and create unnecessary network overhead. Request origin metrics, such as cross-Region request counts, show when applications access data across regions, impacting latency and costs. Object access count metrics reveal when applications frequently read a small subset of objects that could be optimized through caching or moving to high-performance storage. We are expanding the prefix analytics in S3 Storage Lens to enable analyzing billions of prefixes per bucket, whereas previously metrics were limited to the largest prefixes that met minimum size and depth thresholds. This gives you visibility into storage usage and activity across all your prefixes. Finally, we are making it possible to export metrics directly to managed S3 Tables, making them immediately available for querying with AWS analytics services like Amazon QuickSight and enabling you to join this data with other AWS service data for deeper insights. To get started, enable performance metrics or expanded prefixes in your S3 Storage Lens advanced metrics dashboard configuration. These capabilities are available in all AWS Regions, except for AWS China Regions and AWS GovCloud (US) Regions. You can enable metrics export to managed S3 Tables in both free and advanced dashboard configurations in AWS Regions where S3 Tables are available. To learn more, visit the S3 Storage Lens overview page, documentation, S3 pricing page, and read the AWS News Blog.  

Amazon Web Services is announcing the preview of Amazon EC2 X8i, next-generation Memory optimized instances. X8i instances are powered by custom Intel Xeon 6 processors delivering the highest performance and fastest memory among comparable Intel processors in the cloud. X8i instances offer 1.5x more memory capacity (up to 6TB) , and up to 3.4x more memory bandwidth compared to previous generation X2i instances.

X8i instances will be SAP-certified and deliver 46% higher SAPS compared to X2i instances, for mission-critical SAP workloads. X8i instances are a great choice for memory-intensive workloads, including in-memory databases and analytics, large-scale traditional databases, and Electronic Design Automation (EDA). X8i instances offer 35% higher performance than X2i instances with even higher gains for some workloads.

To learn more or request access to the X8i instances preview, visit the Amazon EC2 X8i page.

 

​Amazon Web Services is announcing the preview of Amazon EC2 X8i, next-generation Memory optimized instances. X8i instances are powered by custom Intel Xeon 6 processors delivering the highest performance and fastest memory among comparable Intel processors in the cloud. X8i instances offer 1.5x more memory capacity (up to 6TB) , and up to 3.4x more memory bandwidth compared to previous generation X2i instances. X8i instances will be SAP-certified and deliver 46% higher SAPS compared to X2i instances, for mission-critical SAP workloads. X8i instances are a great choice for memory-intensive workloads, including in-memory databases and analytics, large-scale traditional databases, and Electronic Design Automation (EDA). X8i instances offer 35% higher performance than X2i instances with even higher gains for some workloads. To learn more or request access to the X8i instances preview, visit the Amazon EC2 X8i page.  

AWS announces further enhancements to Amazon GuardDuty Extended Threat Detection with new capabilities to detect multistage attacks targeting Amazon Elastic Compute Cloud (Amazon EC2) instances and Amazon Elastic Container Service (Amazon ECS) clusters running on AWS Fargate or Amazon EC2. GuardDuty Extended Threat Detection uses artificial intelligence and machine learning algorithms trained at AWS scale to automatically correlate security signals and detect critical threats. It analyzes multiple security signals across network activity, process runtime behavior, malware execution, and AWS API activity over extended periods to detect sophisticated attack patterns that might otherwise go unnoticed.

With this launch, GuardDuty introduces two new critical-severity findings: AttackSequence:EC2/CompromisedInstanceGroup and AttackSequence:ECS/CompromisedCluster. These findings provide attack sequence information, allowing you to spend less time on initial analysis and more time responding to critical threats, minimizing business impact. For example, GuardDuty can identify suspicious processes followed by persistence attempts, crypto-mining activities, and reverse shell creation, representing these related events as a single, critical-severity finding. Each finding includes a detailed summary, events timeline, mapping to MITRE ATT&CK® tactics and techniques, and remediation recommendations.

While GuardDuty Extended Threat Detection is automatically enabled for GuardDuty customers at no additional cost, its detection comprehensiveness depends on your enabled GuardDuty protection plans. To improve attack sequence coverage and threat analysis of Amazon EC2 instances, enable Runtime Monitoring for EC2. To enable detection of compromised ECS clusters, enable Runtime Monitoring for Fargate or EC2 depending on your infrastructure type.

To get started, enable GuardDuty protection plans via the Console or API. New GuardDuty customers can start with a 30-day free trial, and existing customers who haven’t used Runtime Monitoring can also try it free for 30 days. For additional information, visit the blog post and Amazon Guard Duty product page.

 

​AWS announces further enhancements to Amazon GuardDuty Extended Threat Detection with new capabilities to detect multistage attacks targeting Amazon Elastic Compute Cloud (Amazon EC2) instances and Amazon Elastic Container Service (Amazon ECS) clusters running on AWS Fargate or Amazon EC2. GuardDuty Extended Threat Detection uses artificial intelligence and machine learning algorithms trained at AWS scale to automatically correlate security signals and detect critical threats. It analyzes multiple security signals across network activity, process runtime behavior, malware execution, and AWS API activity over extended periods to detect sophisticated attack patterns that might otherwise go unnoticed. With this launch, GuardDuty introduces two new critical-severity findings: AttackSequence:EC2/CompromisedInstanceGroup and AttackSequence:ECS/CompromisedCluster. These findings provide attack sequence information, allowing you to spend less time on initial analysis and more time responding to critical threats, minimizing business impact. For example, GuardDuty can identify suspicious processes followed by persistence attempts, crypto-mining activities, and reverse shell creation, representing these related events as a single, critical-severity finding. Each finding includes a detailed summary, events timeline, mapping to MITRE ATT&CK® tactics and techniques, and remediation recommendations. While GuardDuty Extended Threat Detection is automatically enabled for GuardDuty customers at no additional cost, its detection comprehensiveness depends on your enabled GuardDuty protection plans. To improve attack sequence coverage and threat analysis of Amazon EC2 instances, enable Runtime Monitoring for EC2. To enable detection of compromised ECS clusters, enable Runtime Monitoring for Fargate or EC2 depending on your infrastructure type. To get started, enable GuardDuty protection plans via the Console or API. New GuardDuty customers can start with a 30-day free trial, and existing customers who haven’t used Runtime Monitoring can also try it free for 30 days. For additional information, visit the blog post and Amazon Guard Duty product page.  

Today, AWS announces Database Savings Plans, a new flexible pricing model that helps you save up to 35% in exchange for a commitment to a consistent amount of usage (measured in $/hour) over a one-year term with no upfront payment.

Database Savings Plans automatically apply to eligible serverless and provisioned instance usage regardless of supported engine, instance family, size, deployment option, or AWS Region. For example, with Database Savings Plans, you can change between Aurora db.r7g and db.r8g instances, shift a workload from EU (Ireland) to US (Ohio), modernize from Amazon RDS for Oracle to Amazon Aurora PostgreSQL or from RDS to Amazon DynamoDB and still benefit from discounted pricing offered by Database Savings Plans.

Database Savings Plans will be available starting today in all AWS Regions, except China Regions, with support for Amazon Aurora, Amazon RDS, Amazon DynamoDB, Amazon ElastiCache, Amazon DocumentDB (with MongoDB compatibility), Amazon Neptune, Amazon Keyspaces (for Apache Cassandra), Amazon Timestream, and AWS Database Migration Service (DMS).

You can get started with Database Savings Plans from the AWS Billing and Cost Management Console or by using the AWS CLI. To realize the largest savings, you can make a commitment to Savings Plans by using purchase recommendations provided in the console. For a more customized analysis, you can use the Savings Plans Purchase Analyzer to estimate potential cost savings for custom purchase scenarios. For more information, visit the Database Savings Plans pricing page and the AWS Savings Plans FAQs.

 

​Today, AWS announces Database Savings Plans, a new flexible pricing model that helps you save up to 35% in exchange for a commitment to a consistent amount of usage (measured in $/hour) over a one-year term with no upfront payment. Database Savings Plans automatically apply to eligible serverless and provisioned instance usage regardless of supported engine, instance family, size, deployment option, or AWS Region. For example, with Database Savings Plans, you can change between Aurora db.r7g and db.r8g instances, shift a workload from EU (Ireland) to US (Ohio), modernize from Amazon RDS for Oracle to Amazon Aurora PostgreSQL or from RDS to Amazon DynamoDB and still benefit from discounted pricing offered by Database Savings Plans. Database Savings Plans will be available starting today in all AWS Regions, except China Regions, with support for Amazon Aurora, Amazon RDS, Amazon DynamoDB, Amazon ElastiCache, Amazon DocumentDB (with MongoDB compatibility), Amazon Neptune, Amazon Keyspaces (for Apache Cassandra), Amazon Timestream, and AWS Database Migration Service (DMS). You can get started with Database Savings Plans from the AWS Billing and Cost Management Console or by using the AWS CLI. To realize the largest savings, you can make a commitment to Savings Plans by using purchase recommendations provided in the console. For a more customized analysis, you can use the Savings Plans Purchase Analyzer to estimate potential cost savings for custom purchase scenarios. For more information, visit the Database Savings Plans pricing page and the AWS Savings Plans FAQs.  

AWS launches the preview of Amazon EC2 C8ine instances, powered by custom sixth-generation Intel Xeon Scalable processors (Granite Rapids) and the latest AWS Nitro v6 card. These instances are designed specifically for dataplane packet processing workloads.

Amazon EC2 C8ine instance configurations can deliver up to 2.5 times higher packet performance per vCPU versus prior generation C6in instances. They can offer up to 2x higher network bandwidth through internet gateways and up to 3x more Elastic Network Interface (ENI) compared to existing C6in network optimized instances. They are ideal for packet processing workloads requiring high performance at small packet sizes. These workloads include security virtual appliances, firewalls, load balancers, DDoS protection systems, and Telco 5G UPF applications.

These instances are available for preview upon request through your AWS account team. Connect with your account representatives to signup.

 

​AWS launches the preview of Amazon EC2 C8ine instances, powered by custom sixth-generation Intel Xeon Scalable processors (Granite Rapids) and the latest AWS Nitro v6 card. These instances are designed specifically for dataplane packet processing workloads. Amazon EC2 C8ine instance configurations can deliver up to 2.5 times higher packet performance per vCPU versus prior generation C6in instances. They can offer up to 2x higher network bandwidth through internet gateways and up to 3x more Elastic Network Interface (ENI) compared to existing C6in network optimized instances. They are ideal for packet processing workloads requiring high performance at small packet sizes. These workloads include security virtual appliances, firewalls, load balancers, DDoS protection systems, and Telco 5G UPF applications. These instances are available for preview upon request through your AWS account team. Connect with your account representatives to signup.