2/24/2026, 12:00:00 AM ~ 2/25/2026, 12:00:00 AM (UTC)
Recent Announcements
Announcing AWS Elemental Inference
AWS Elemental Inference, a fully managed Artificial Intelligence (AI) service that enables broadcasters and streamers to automatically generate vertical content and highlight clips for mobile and social platforms in real time, is now generally available. The service applies AI capabilities to live and on-demand video in parallel with encoding and helps companies and creators to reach audiences in any format without requiring AI expertise or dedicated production teams.\n With Elemental Inference you can process video once and optimize it everywhere—creating main broadcasts while simultaneously generating vertical versions for TikTok, Instagram Reels, YouTube Shorts, Snapchat, and other mobile platforms in parallel with live video. For example, sports broadcasters can automatically generate vertical highlight clips during live games and distribute them to social platforms in real-time, capturing viral moments as they happen rather than hours later.
The service launches with two AI features: vertical video cropping that transforms live and on-demand landscape broadcasts into mobile-optimized formats, and advanced metadata analysis that identifies key moments to generate highlight clips from live content. Using an agentic AI application that requires no prompts or human-in-the-loop intervention, broadcasters can scale content production without adding manual workflows or production staff—the system automatically adapts content for each platform. In beta testing, large media companies achieved 34% or more savings on AI-powered live video workflows compared to using multiple point solutions.
AWS Elemental Inference is available in the following AWS Regions: US East (N. Virginia), US West (Oregon), Asia Pacific (Mumbai), and Europe (Ireland).
For more information, visit the AWS News Blog or explore the AWS Elemental Inference documentation.
Amazon RDS Snapshot Export to S3 now available in AWS GovCloud (US) Regions
Amazon RDS Snapshot Export to S3 is now available in AWS GovCloud (US) regions, enabling you to export snapshot data in Apache Parquet format for analytics, data retention, and machine learning use cases.\n Snapshot export to S3 supports all DB snapshot types (manual, automated system, and AWS Backup snapshots) and runs directly on the snapshot without impacting database performance. The exported data in Apache Parquet format can be analyzed using other AWS services such as Amazon Athena, Amazon SageMaker, or Amazon Redshift Spectrum, or with big data processing frameworks such as Apache Spark. You can create a snapshot export with just a few clicks in the Amazon RDS Management Console or by using the AWS SDK or CLI. Snapshot Export to S3 is supported for Amazon Aurora PostgreSQL - Compatible Edition and Amazon Aurora MySQL, Amazon RDS for PostgreSQL, Amazon RDS for MySQL, and Amazon RDS for MariaDB snapshots. For more information, including instructions on getting started, read Aurora documentation or Amazon RDS documentation.
AWS Deadline Cloud now supports running tasks together in chunks
Today, AWS Deadline Cloud announces support for grouping tasks into chunks to efficiently execute multiple tasks together. AWS Deadline Cloud is a fully managed service that simplifies render management for computer-generated 2D/3D graphics and visual effects for films, TV shows, commercials, games, and industrial design.\n When your job has short tasks, or tasks that need to run in an environment with a long startup time, chunking them together for execution reduces the time and cost for completing the job. When creating a job, you can now manually specify a chunk size for the number tasks to group together for execution, or alternately specify a target run time for the execution of a chunk of tasks. The target run time will be used to dynamically change the number of tasks grouped together as the job completes to improve execution efficiency and achieve the target run time. Running tasks together in chunks is now available in all AWS Regions where AWS Deadline Cloud is supported. To get started, visit the Deadline Cloud developer guide.
Amazon EC2 R7a instances are now available in the Asia Pacific (Hyderabad) Region
Starting today, the general-purpose Amazon EC2 R7a instances are now available in AWS Asia Pacific (Hyderabad) Region. R7a instances, powered by 4th Gen AMD EPYC processors (code-named Genoa) with a maximum frequency of 3.7 GHz, deliver up to 50% higher performance compared to R6a instances.\n These instances can be purchased as Savings Plans, Reserved, On-Demand, and Spot instances. To get started, visit the AWS Management Console, AWS Command Line Interface (CLI), and AWS SDKs. To learn more, visit the R7a instances page.
Starting today, Amazon Elastic Compute Cloud (Amazon EC2) C8i and C8i-flex instances are available in the Asia Pacific (Malaysia) and South America (Sao Paulo) regions. These instances are powered by custom Intel Xeon 6 processors, available only on AWS, delivering the highest performance and fastest memory bandwidth among comparable Intel processors in the cloud. These C8i and C8i-flex instances offer up to 15% better price-performance, and 2.5x more memory bandwidth compared to previous generation Intel-based instances. They deliver up to 20% higher performance than C7i and C7i-flex instances, with even higher gains for specific workloads. The C8i and C8i-flex are up to 60% faster for NGINX web applications, up to 40% faster for AI deep learning recommendation models, and 35% faster for Memcached stores compared to C7i and C7i-flex.\n C8i-flex are the easiest way to get price performance benefits for a majority of compute intensive workloads like web and application servers, databases, caches, Apache Kafka, Elasticsearch, and enterprise applications. They offer the most common sizes, from large to 16xlarge, and are a great first choice for applications that don’t fully utilize all compute resources. C8i instances are a great choice for all memory-intensive workloads, especially for workloads that need the largest instance sizes or continuous high CPU usage. C8i instances offer 13 sizes including 2 bare metal sizes and the new 96xlarge size for the largest applications. To get started, sign in to the AWS Management Console. Customers can purchase these instances via Savings Plans, On-Demand instances, and Spot instances. For more information about the new C8i and C8i-flex instances visit the AWS News blog.
AWS AppConfig integrates with New Relic for automated rollbacks
AWS AppConfig today launched a new integration that enables automated, intelligent rollbacks during feature flag and dynamic configuration deployments using New Relic Workflow Automation. Building on AWS AppConfig’s third-party alert capability, this integration provides teams using New Relic with a solution to automatically detect degraded application health and trigger rollbacks in seconds, eliminating manual intervention.\n When you deploy feature flags using AWS AppConfig’s gradual deployment strategy, the AWS AppConfig New Relic Extension continuously monitors your application health against configured alert conditions. If issues are detected during a feature flag update and deployment, such as increased error rates or elevated latency, the New Relic Workflow automatically sends a notification to trigger an immediate rollback, reverting the feature flag to its previous state. This closed-loop automation reduces the time between detection and remediation from minutes to seconds, minimizing customer impact during failed deployments.
Amazon EC2 M8a instances now available in AWS Europe (Frankfurt) region
Starting today, the general-purpose Amazon EC2 M8a instances are available in AWS Europe (Frankfurt) region. M8a instances are powered by 5th Gen AMD EPYC processors (formerly code named Turin) with a maximum frequency of 4.5 GHz, deliver up to 30% higher performance, and up to 19% better price-performance compared to M7a instances.\n M8a instances deliver 45% more memory bandwidth compared to M7a instances, making these instances ideal for even latency sensitive workloads. M8a instances deliver even higher performance gains for specific workloads. M8a instances are up to 60% faster for GroovyJVM benchmark, and up to 39% faster for Cassandra benchmark compared to Amazon EC2 M7a instances. M8a instances are SAP-certified and offer 12 sizes including 2 bare metal sizes. This range of instance sizes allows customers to precisely match their workload requirements. M8a instances are built using the latest sixth generation AWS Nitro Cards and ideal for applications that benefit from high performance and high throughput such as financial applications, gaming, rendering, application servers, simulation modeling, mid-size data stores, application development environments, and caching fleets. To get started, sign in to the AWS Management Console. Customers can purchase these instances via Savings Plans, On-Demand instances, and Spot instances. For more information visit the Amazon EC2 M8a instance page.
Amazon EKS Node Monitoring Agent is now open source
Amazon Elastic Kubernetes Service (Amazon EKS) Node Monitoring Agent is now open source. You can access the Amazon EKS Node Monitoring Agent source code and contribute to its development on GitHub.\n Running workloads reliably in Kubernetes clusters can be challenging. Cluster administrators often have to resort to manual methods of monitoring and repairing degraded nodes in their clusters. The Amazon EKS Node Monitoring Agent simplifies this process by automatically monitoring and publishing node-level system, storage, networking, and accelerator issues as node conditions, which are used by Amazon EKS for automatic node repair. With the Amazon EKS Node Monitoring Agent’s source code available on GitHub, you now have visibility into the agent’s implementation, can customize it to fit your requirements, and can contribute directly to its ongoing development. The Amazon EKS Node Monitoring Agent is included in Amazon EKS Auto Mode and is available as an Amazon EKS add-on in all AWS Regions where Amazon EKS is available. To learn more about the Amazon EKS Node Monitoring Agent and node repair, visit the Amazon EKS documentation.
MediaConvert Introduces new video probe API and UI
Introducing Probe API, a powerful and free metadata analysis tool for AWS Elemental MediaConvert. Optimized for efficiency, Probe API reads header metadata to quickly return essential information about your media files, including codec specifications, pixel formats, color space details, and container information - all without waiting to process the actual video content. This analysis capability makes it an invaluable tool for content creators, developers, and media professionals who need to quickly validate files, automate workflows, or utilize Elementals’ Step Functions to make encoding decisions based on source material characteristics.\n For complete implementation details and usage examples, please visit the MediaConvert API Reference documentation. The Probe API can be utilized in any region where AWS Elemental MediaConvert is available, making it a versatile tool for streamlining your media workflow analysis. To get started with Probe API and explore its capabilities, visit the AWS Elemental MediaConvert product page or consult the User Guide for comprehensive documentation.
AWS Blogs
AWS Japan Blog (Japanese)
- AWS Weekly — 2026/2/16
- Implement data mesh patterns in Amazon SageMaker Catalog without changing applications
- Amazon EC2 HPC8a instances with 5th generation AMD EPYC processors are now generally available
- Announcing Amazon SageMaker Inference for custom Amazon Nova models
- Weekly Generative AI with AWS — February 16, 2026/2016
- Using Default Storage Encryption for New Amazon Aurora Clusters
- Showcase your skills more with Microcredentials
AWS News Blog
AWS Compute Blog
Containers
- Deep dive: Simplifying resource orchestration with Amazon EKS Capabilities
- Running containerized hybrid nodes with Amazon Elastic Kubernetes Service
AWS Database Blog
AWS Developer Tools Blog
AWS for Industries
- Agentic GraphRAG for Capital Markets
- Migrating enterprise ML workloads from Databricks to AWS for large scale ML
- Nissan Collaborates with AWS to Accelerate SDV Development
Artificial Intelligence
- Build an intelligent photo search using Amazon Rekognition, Amazon Neptune, and Amazon Bedrock
- Train CodeFu-7B with veRL and Ray on Amazon SageMaker Training jobs
- Generate structured output from LLMs with Dottxt Outlines in AWS
- Global cross-Region inference for latest Anthropic Claude Opus, Sonnet and Haiku models on Amazon Bedrock in Thailand, Malaysia, Singapore, Indonesia, and Taiwan
- Introducing Amazon Bedrock global cross-Region inference for Anthropic’s Claude models in the Middle East Regions (UAE and Bahrain)