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Starting today, Amazon EC2 M8i-flex instances are now available in Asia Pacific (Malaysia, Seoul, Singapore, Tokyo), Europe (Frankfurt) and Canada (Central) 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. The M8i-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% better performance than M7i-flex instances, with even higher gains for specific workloads. The M8i-flex instances are up to 30% faster for PostgreSQL databases, up to 60% faster for NGINX web applications, and up to 40% faster for AI deep learning recommendation models compared to M7i-flex instances. M8i-flex instances are the easiest way to get price performance benefits for a majority of general-purpose workloads like web and application server

Starting today, public certificates issued from AWS Certificate Manager (ACM) have a maximum validity period of 198 days, compared to previous validity period of 395 days. With this change, ACM-issued public certificates will be compliant with new Certification Authority Browser (CA/Browser) Forum mandate that certificates be no longer than 200 days starting 15th March 2026. No action is required from the customers to receive this change. All new and renewed public certificates will by default have a validity of 198 days. Existing certificates with 395 days validity continue to be valid and can be used until they renew or expire. All other certificate functionality remains in place. ACM will still continue to auto renew the certificates before expiry. The certificates are now renewed 45 days before expiry. Existing 398 day validity certificates will renew 60 days before expiry and will renew with 198 days validity period. We have reduced the pricing for ACM’s exp

Today, AWS announces Amazon Aurora DSQL integration with Kiro powers and AI agent skills, enabling developers to build Aurora DSQL-backed applications faster with AI agent-assisted development. These integrations bundle the Aurora DSQL Model Context Protocol (MCP) server with development best practices, so AI agents can help you with Aurora DSQL schema design, performance optimization, and database operations out of the box. Kiro powers is a registry of curated and pre-packaged MCP servers, steering files, and agent hooks to accelerate specialized software development and deployment use cases. With the Kiro power for Aurora DSQL, agents have instant access to specialized knowledge, so developers can work confidently without any prior context, reducing trial-and-error development cycles. The power is available within the Kiro IDE for one-click installation. The

Amazon Connect Cases now supports AWS Service Quotas, giving administrators a centralized way to view applied limits, monitor utilization, and scale case workloads without hitting unexpected service constraints. You can request quota increases directly from the Service Quotas console, and eligible requests are automatically approved without manual intervention. Amazon Connect Cases is available in the following AWS Regions: US East (N. Virginia), US West (Oregon), Canada (Central), Europe (Frankfurt), Europe (London), Asia Pacific (Seoul), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), and Africa (Cape Town). To learn more and get started, visit the Amazon Connect Cases webpage and documentation.

Amazon Managed Grafana now supports customer-managed keys (CMK) through AWS Key Management Service (KMS), enabling you to encrypt data stored in in your Amazon Managed Grafana workspaces with your own encryption keys. Amazon Managed Grafana is a fully managed service based on open-source Grafana that makes it easier for you to visualize and analyze your operational data at scale. Amazon Managed Grafana provides encryption at rest using AWS owned keys by default. With this launch, you now have an option to use a customer-managed key when creating an Amazon Managed Grafana workspace. This allows you to add a self-managed security layer, helping you meet your organization’s compliance and regulatory requirements. This feature is now available in all regions where Amazon Managed Grafana is generally available, except in AWS GovCloud (US) Regions. To get started with Amazon Managed Grafana, refer 

AWS Clean Rooms now supports catalog federation for remote Iceberg catalogs. This capability simplifies clean room setup by providing direct, secure access to Iceberg tables stored in Amazon S3 and cataloged in remote catalogs—without requiring table metadata replication. Organizations can now use AWS Glue catalog federation to provide direct access to their existing Iceberg REST catalog in a Clean Rooms collaboration. For example, a media publisher with data cataloged in the AWS Glue Data Catalog and an advertiser with data cataloged in a remote Iceberg catalog can analyze their collective datasets to evaluate advertising spend—without having to build ETL data pipelines or share underlying data with one another. AWS Clean Rooms helps companies and their partners easily analyze and collaborate on their collective datasets without revealing or copying one another’s un

Amazon OpenSearch Service expands support for the latest generation Graviton4-based Amazon EC2 instance families. These new instance types are compute optimized (c8g), general purpose (m8g), and memory optimized (r8g, r8gd) instances. AWS Graviton4 processors provide up to 30% better performance than AWS Graviton3 processors with c8g, m8g and r8g & r8gd offering the best price performance for compute-intensive, general purpose, and memory-intensive workloads respectively. To learn more about Graviton4 improvements, please see the blog on r8g instances and the blog on c8g & m8g instances. Amazon OpenSearch Service Graviton4 instances are supported for all OpenSearch versions

Amazon OpenSearch Service now supports latest generation x86 based high performance Storage Optimized i7i instances. Powered by 5th generation Intel Xeon Scalable processors, I7i instances deliver up to 23% better compute performance and more than 10% better price performance over previous generation I4i instances. I7i instances have 3rd generation AWS Nitro SSDs with up to 50% better real-time storage performance, up to 50% lower storage I/O latency, and up to 60% lower storage I/O latency variability compared to I4i instances. Built on the AWS Nitro System, these instances offload CPU virtualization, storage, and networking functions to dedicated hardware and software enhancing the performance and security for your workloads. Amazon OpenSearch Service supports i7i instances in following AWS Regions US East (N. Virginia, O

Starting today, Amazon Elastic Compute Cloud (Amazon EC2) R8i and R8i-flex instances are available in the Europe (Ireland) region. 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. The R8i and R8i-flex instances offer up to 15% better price-performance, and 2.5x more memory bandwidth compared to previous generation Intel-based instances. They deliver 20% higher performance than R7i instances, with even higher gains for specific workloads. They are up to 30% faster for PostgreSQL databases, up to 60% faster for NGINX web applications, and up to 40% faster for AI deep learning recommendation models compared to R7i. R8i-flex, our first memory-optimized Flex instances, are the easiest way to get price performance benefits for a majority of memory-intensive workloads. They offer the most common sizes, from large to 16xlarge, and are

Amazon Bedrock now extends reinforcement fine-tuning (RFT) support to popular open-weight models, including OpenAI GPT-OSS and Qwen models, and introduces OpenAI-compatible fine-tuning APIs. These capabilities make it easier for developers to improve open-weight model accuracy without requiring deep machine learning expertise or large volumes of labeled data. Reinforcement fine-tuning in Amazon Bedrock automates the end-to-end customization workflow, allowing models to learn from feedback on multiple possible responses using a small set of prompts, rather than traditional large training datasets. Reinforcement fine-tuning enables customers to use smaller, faster, and more cost-effective model variants while maintaining high quality. Organizations often struggle to adapt foundation models to their unique business requirements, forcing tradeoffs between generic models with limited performance and complex, expensive customization pipelines that require specialized infrastructu