The Amazon S3 Express One Zone storage class is purpose-built to deliver the fastest cloud object storage for performance-critical applications that demand consistent single-digit millisecond request latency. S3 Express One Zone can improve data access speeds by 10x and reduce request costs by 50% compared to S3 Standard. It enables workloads such as machine learning training, interactive analytics, and media content creation to achieve single-digit millisecond data access speed with high durability and availability.

You can now use Mountpoint for Amazon S3 to access objects stored in the new Amazon S3 Express One Zone storage class using file system operations. The new S3 Express One Zone storage class is purpose-built to deliver the fastest cloud object storage for performance-critical applications that demand consistent single-digit millisecond request latency.

Starting today, you can use Amazon Athena to query data stored in the Amazon S3 Express One Zone storage class for up to 2.1x faster query performance than S3 Standard. S3 Express One Zone is a high-performance, single-Availability Zone storage class purpose-built to deliver consistent, single-digit millisecond data access for your most frequently accessed data and latency-sensitive applications.

Starting today, new memory optimized Amazon Elastic Compute Cloud (Amazon EC2) R8g instances, powered by the latest-generation custom-designed AWS Graviton4 processors, are available in preview. R8g 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. R8g instances offer larger instances with up to 3x more vCPUs and memory than R7g instances. Amazon EC2 R8g instances are ideal for memory-intensive workloads such as databases, in-memory caches, and real-time big data analytics.

Now generally available, fully managed Agents for Amazon Bedrock enables generative AI applications to execute multi-step tasks across company systems and data sources. Agents can plan and perform business tasks, such as answering questions about product availability or taking orders. Customers can create an agent in just a few clicks by writing a few instructions in natural language, providing access to the company’s systems, and defining AWS Lambda functions. Agents analyze the user request and break it down into a logical sequence using the FM’s reasoning capabilities to determine what information is needed, the APIs to call, and the sequence of execution to fulfill the request. After creating the plan, Agents call the right APIs and retrieve the information needed from company systems and data sources to provide accurate and relevant responses. Agents automatically perform this process in the background—securely by encrypting data in transit and at rest—each time. This relinquishes customers from having to engineer prompts, train models, or manually connect systems. With Agents for Amazon Bedrock, customers can easily integrate generative AI into their businesses, simplifying and accelerating how they perform and execute tasks without the undifferentiated heavy lifting.

Amazon Bedrock provides you with an easy way to build and scale generative AI applications with leading foundation models (FMs). Continued pre-training in Amazon Bedrock is a new capability that allows you to train Amazon Titan Text Express and Amazon Titan Text Lite FMs and customize them using your own unlabeled data, in a secure and managed environment. As models are continually pre-trained on data spanning different topics, genres, and contexts over time, they become more robust and learn to handle out-of-domain data better by accumulating wider knowledge and adaptability, creating even more value for your organization.

Amazon Bedrock is an easy way to build and scale generative AI applications with leading foundation models (FMs). Amazon Bedrock now supports fine-tuning for Meta Llama 2 and Cohere Command Light, along with Amazon Titan Text Lite and Amazon Titan Text Express FMs, so you can use labeled datasets to increase model accuracy for particular tasks.

Today, we are announcing Guardrails for Amazon Bedrock in preview that enables customers to implement safeguards across foundation models (FMs) based on their use cases and responsible AI policies. Customers can create multiple guardrails tailored to different use cases and apply them across multiple FMs, providing a consistent user experience and standardizing safety controls across generative AI applications.

Now generally available, fully managed Knowledge Bases for Amazon Bedrock securely connects foundation models (FMs) to internal company data sources for Retrieval Augmented Generation (RAG) to deliver more relevant, context-specific, and accurate responses. Knowledge bases extend the FM’s powerful capabilities to make it more knowledgeable about your business, customers, and offerings.

We are excited to announce the public preview of Amazon Q in AWS Chatbot, which provides summarized and concise answers to customers’ AWS-related queries in a conversational experience in Microsoft Teams and Slack. Customers receive concise and trustworthy answers to their questions to accelerate their understanding of the AWS services, architect solutions, and troubleshoot issues.