Monat: August 2026

Amazon Aurora serverless now scales faster to support agentic AI and other bursty workloads

Amazon Aurora serverless now delivers higher initial capacity during scale-up events, reaching up to 12 ACUs within a second and continuing to scale up to 256 ACUs as your workload grows. When the workload finishes, Aurora serverless automatically scales down to zero. This makes it especially well-suited for agentic AI applications, which typically have bursts of activity, long idle windows, and unpredictable traffic patterns. Aurora serverless handles all of it automatically, scaling capacity with your agents, so you only pay for what you use. This enhancement is enabled by default on all Aurora serverless clusters running on platform version 3 or 4, with no configuration changes required. Existing clusters on platform versions 1 and 2 can upgrade directly to the latest platform version 4 to benefit from these improvements. You can verify your cluster’s platform version in the AWS Management Console under the instance configuration section, or via the RDS API’s ServerlessV2PlatformVersion parameter. For pricing details and Region availability, visit Amazon Aurora Pricing. To learn more, read the Aurora serverless scaling documentation, and get started by creating an Aurora serverless database in just a few steps in the AWS Management Console.
Quelle: aws.amazon.com

AWS IAM Identity Center makes managment of AWS account access optional for new organization instances

AWS IAM Identity Center now lets you decide whether to enable management of AWS account access when you create a new organization instance. This allows you to use IAM Identity Center to manage access to AWS applications only, without the need to manage access to AWS accounts. This feature is available at the time of initial configuration of an IAM Identity Center instance and does not affect existing IAM Identity Center instances.
IAM Identity Center enables you to connect your workforce identities to AWS once and offer AWS application owners across your organization streamlined access management. Application end users benefit from single sign-on, user awareness, and consistent authentication experience across AWS applications. Previously, this meant you also needed to manage access to AWS accounts. With this release, account management is now optional. When you choose not to enable management of AWS accounts, IAM Identity Center does not provision its service-linked role into your member accounts, which reduces the access surface in your environment. You can enable account management permissions later through instance settings or the UpdateInstance API. 
This capability is available in all AWS Regions where IAM Identity Center is available. To get started, see Configure instance settings in the IAM Identity Center User Guide.
Quelle: aws.amazon.com

Amazon DynamoDB now supports real-time vector search

Today, AWS announces the general availability of vector search for Amazon DynamoDB, a new feature to index and search vectors in real time. As vector datasets grow into the billions or trillions, vector search at scale traditionally trades off search speed, scale, and accuracy: latency climbs with vector count unless you accept lower recall or throughput. DynamoDB now supports native vector search with single-digit millisecond latency at 99%+ recall and is designed for any scale, even trillions of vectors. With DynamoDB vector search, you store vector embeddings alongside your other attributes and generate them using a model of your choice, including models available on Amazon Bedrock. You create a vector index and run approximate nearest neighbor searches, pick the vector index partition key to scale, and filter on attributes to scope results. You get the same serverless benefits you rely on today: zero infrastructure management, zero downtime, zero maintenance windows, and pay for only what you use. You can already use DynamoDB to store memory for AI agents, and with vector search you can now add semantic retrieval over that memory for agentic grounding, along with product similarity search, personalized advertising, retrieval augmented generation, and recommendation systems, with predictable performance. To learn more, visit the AWS News Blog, Amazon DynamoDB product page, and Amazon DynamoDB Developer Guide.
Quelle: aws.amazon.com

AWS Marketplace adds AI Insights so buyers can understand pricing before they buy

You can now understand how a product’s pricing works before you buy it. Available in the pricing section of the listing in AWS Marketplace, AI Insights explains each product’s pricing in plain language: what a pricing unit maps to, how your bill changes as usage scales, how multiple pricing dimensions combine into one cost, and what is and isn’t included. Answering these questions used to mean having to visit multiple websites and piecing together pricing details on your own. Now the context sits on the listing, so you can evaluate pricing and move to purchase without switching tabs.
AI Insights cites sources so you can see where the explanations come from. AI Insights draws from the pricing the seller publishes on the Marketplace listing, and additional pricing context on the seller’s public website.
AI Insights is live today on most listings where external pricing context is available. It is available in all commercial AWS Regions where AWS Marketplace is available. To see it, open any product listing on the AWS Marketplace website and scroll to the pricing section. Sellers can review what appears on their listing and request edits at any time through the Contact Us form linked in the AI Insights page on the AWS Marketplace Seller Guide.
Quelle: aws.amazon.com

Run interactive workloads on Amazon EMR on EC2 with Spark Connect

Amazon EMR on EC2 now supports interactive Apache Spark sessions with Spark Connect. Data engineers and data scientists can develop and debug Apache Spark applications interactively from managed notebooks in Amazon SageMaker Unified Studio and their own IDEs, such as Jupyter and Visual Studio Code, with each session running on dedicated EMR on EC2 clusters. You can also monitor and debug active and completed sessions in the EMR console.
 
An interactive session provides a persistent Spark context that spans across cells and scripts, letting you blend local Python code execution with remote Spark operations. Spark Connect’s client-server architecture decouples your application client from the Spark driver and allows you to maintain your preferred development environment and tooling while Spark infrastructure runs on the cluster. This architecture supports workflows including ad hoc data exploration, iterative step-by-step debugging, and incremental PySpark job development before deploying to production. For observability, you get real-time session monitoring via the Spark UI, history tracking through the Spark History Server, and session management from the EMR console or API/CLI/SDK.
 
Interactive Sessions is available on Amazon EMR on EC2 with AWS runtime for Apache Spark (emr-spark-8.0) and later, in all AWS Regions where Amazon EMR is available, except the AWS GovCloud Regions and the China Regions. The Amazon SageMaker Unified Studio experience is available in supported regions. To get started, visit the Interactive sessions with Spark Connect guide or the Amazon SageMaker Unified Studio Getting Started guide.
Quelle: aws.amazon.com