The AI-Driven Development Life Cycle (AI-DLC) methodology marks a significant change in software development by strategically assigning routine tasks to AI while maintaining human oversight for critical decisions. Amazon Q Developer, a generative AI coding assistant, supports the entire software development lifecycle and offers the Project Rules feature, allowing users to tailor their development practices [...]

AI-Driven Development Life Cycle (AI-DLC) holds the promise of unlocking the full potential of AI in software development. By emphasizing AI-led workflows and human-centric decision-making, AI-DLC can deliver velocity and quality. However, realizing these gains hinges on how organizations effectively integrate AI into their engineering workflows. Through our work with engineering teams across industries, we [...]

Streamline your AWS infrastructure development with AI-powered documentation search, validation, and troubleshooting Introduction Today, we’re excited to introduce the AWS Infrastructure-as-Code (IaC) MCP Server, a new tool that bridges the gap between AI assistants and your AWS infrastructure development workflow. Built on the Model Context Protocol (MCP), this server enables AI assistants like Kiro CLI, [...]

This post shows how Amazon EMR 7.12 can make your Apache Spark and Iceberg workloads up to 4.5x faster performance.

In this post, we analyze the results from our benchmark tests comparing the Amazon EMR 7.9 optimized Spark runtime against Spark 3.5.5 without encryption optimizations. We walk through a detailed cost analysis and provide step-by-step instructions to reproduce the benchmark.

In this post, we demonstrate the write performance benefits of using the Amazon EMR 7.12 runtime for Spark and Iceberg compares to open source Spark 3.5.6 with Iceberg 1.10.0 tables on a 3TB merge workload.

In this post, we show you how Medidata created a unified, scalable, real-time data platform that serves thousands of clinical trials worldwide with AWS services, Apache Iceberg, and a modern lakehouse architecture.

In this post, we explore how Myriad Genetics partnered with the AWS Generative AI Innovation Center to transform their healthcare document processing pipeline using Amazon Bedrock and Amazon Nova foundation models, achieving 98% classification accuracy while reducing costs by 77% and processing time by 80%. We detail the technical implementation using AWS’s open-source GenAI Intelligent Document Processing Accelerator, the optimization strategies for document classification and key information extraction, and the measurable business impact on Myriad’s prior authorization workflows.

In this post, CBRE and AWS demonstrate how they transformed property management by building a unified search and digital assistant using Amazon Bedrock, enabling professionals to access millions of documents and multiple databases through natural language queries. The solution combines Amazon Nova Pro for SQL generation and Claude Haiku for document interactions, achieving a 67% reduction in processing time while maintaining enterprise-grade security across more than eight million documents.

In this post, we introduce Managed Tiered KV Cache and Intelligent Routing for Amazon SageMaker HyperPod, new capabilities that can reduce time to first token by up to 40% and lower compute costs by up to 25% for long context prompts and multi-turn conversations. These features automatically manage distributed KV caching infrastructure and intelligent request routing, making it easier to deploy production-scale LLM inference workloads with enterprise-grade performance while significantly reducing operational overhead.

Amazon EKS introduces Provisioned Control Plane, a new capability that allows you to pre-allocate control plane capacity for predictable, high-performance Kubernetes operations at scale. In this post, we explore how this enhanced option complements the Standard Control Plane by offering multiple scaling tiers (XL, 2XL, 4XL) with well-defined performance characteristics for API request concurrency, pod scheduling rates, and cluster database size—enabling you to handle demanding workloads like ultra-scale AI training, high-performance computing, and mission-critical applications with confidence.

The Amazon Quick Suite User Group in Chicago brought together over 90 innovators to explore how agentic AI is transforming workplace productivity through hands-on workshops and live demonstrations. In this post, we share highlights from the meetup where attendees learned how Quick Suite’s unified AI agents enable users to research across data sources, gain business insights, and automate workflows.

Netflix operates a global streaming service that serves hundreds of millions of users through a distributed microservices architecture. In this post, we examine the technical and operational challenges encountered by their Online Data Stores (ODS) team with their current self-managed distributed PostgreSQL-compatible database, the evaluation criteria used to select a database solution, and why they chose to migrate to Amazon Aurora PostgreSQL to meet their current and future performance needs. The migration to Aurora PostgreSQL improved their database infrastructure, achieving up to 75% increase in performance and 28% cost savings across critical applications.

Network infrastructure resources like NAT Gateways represent a substantial portion of cloud spending, yet optimizing these costs presents unique challenges. Unlike compute resources, NAT Gateways often play critical roles in high availability and disaster recovery architectures, making it difficult to confidently identify which ones are truly unused versus those serving as disaster recovery or backup components. Starting today, AWS Compute Optimizer expands its idle resource detection capabilities to include NAT Gateways. Building on our recent launch of idle recommendations for compute, storage, and database resources, you can now identify and clean up unused NAT Gateway resources to drive additional cost savings while maintaining application reliability.

Organizations face a critical challenge in data protection: how to retain ever-increasing volumes of backup data for extended periods while maintaining cost efficiency. Regulatory mandates, internal governance policies, and comprehensive disaster recovery strategies often necessitate preserving backups for months or even years. At the same time, the threat landscape continues to evolve, with sophisticated ransomware [...]

At AWS re:Invent 2025, we’re excited to share latest innovations designed to empower organizations to thrive in the transformative AI era. This year’s top Cloud Operations announcements address the most pressing challenges our customers face today—from gaining comprehensive visibility into generative AI workloads to significantly accelerating incident resolution and efficiently managing the exponential growth of [...]

We are launching a new feature: gateway interceptors for Amazon Bedrock AgentCore Gateway. This powerful new capability provides fine-grained security, dynamic access control, and flexible schema management.

Amazon EKS Blueprints for CDK now supports EKS Auto Mode, enabling developers to deploy fully managed Kubernetes clusters with minimal configuration while AWS automatically handles infrastructure provisioning, compute scaling, and core add-on management. In this post, we explore how this integration combines EKS Blueprints’ declarative infrastructure-as-code approach with EKS Auto Mode’s hands-off cluster operations, providing three practical deployment patterns—from basic clusters to specialized ARM-based and AI/ML workloads—that let teams focus on application development rather than infrastructure management .

In 2025, Amazon Redshift delivered several performance optimizations that improved query performance over twofold for Iceberg workloads on Amazon Redshift Serverless, delivering exceptional performance and cost-effectiveness for your data lake workloads. In this post, we describe some of the optimizations that led to these performance gains.

With the Letta Developer Platform, you can create stateful agents with built-in context management (compaction, context rewriting, and context offloading) and persistence. Using the Letta API, you can create agents that are long-lived or achieve complex tasks without worrying about context overflow or model lock-in. In this post, we guide you through setting up Amazon Aurora Serverless as a database repository for storing Letta long-term memory. We show how to create an Aurora cluster in the cloud, configure Letta to connect to it, and deploy agents that persist their memory to Aurora. We also explore how to query the database directly to view agent state.

AWS Glue now supports catalog federation for remote Iceberg tables in the Data Catalog. With catalog federation, you can query remote Iceberg tables, stored in Amazon S3 and cataloged in remote Iceberg catalogs, using AWS analytics engines and without moving or duplicating tables. In this post, we discuss how to get started with catalog federation for Iceberg tables in the Data Catalog.

In this post, we walk you through the new capabilities in Iceberg V3, explain how deletion vectors and row lineage address these challenges, explore real-world use cases across industries, and provide practical guidance on implementing Iceberg V3 features across AWS analytics, catalog, and storage services.

The decentralized web has unlocked significant opportunities for user-owned digital experiences, with Web3 applications built on public blockchains (onchain) processing billions of dollars in daily transaction volume across thousands of protocols. However, despite this rapid growth, most Web3 platforms still rely on rudimentary content discovery mechanisms—chronological feeds, basic search, and manual curation—don’t utilize the rich behavioral data available onchain. While traditional web and mobile applications such as social media platforms and e-commerce stores (‘Web2’ platforms) leverage recommendation systems to drive engagement and user satisfaction, Web3 applications struggle to deliver personalized experiences that improve user retention and platform value due to the cost and technical complexity involved in adapting these systems to the Web3 space.

Organizations often have large volumes of documents containing valuable information that remains locked away and unsearchable. This solution addresses the need for a scalable, automated text extraction and knowledge base pipeline that transforms static document collections into intelligent, searchable repositories for generative AI applications.

In this post, we explore how Condé Nast used Amazon Bedrock and Anthropic’s Claude to accelerate their contract processing and rights analysis workstreams. The company’s extensive portfolio, spanning multiple brands and geographies, required managing an increasingly complex web of contracts, rights, and licensing agreements.