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Today, AWS announces target coverage analysis in Savings Plans Purchase Analyzer, a capability in AWS Billing and Cost Management that helps you plan your Savings Plans purchases based on your coverage target. Savings Plans Purchase Analyzer helps you evaluate different purchase scenarios by estimating the potential impact of Savings Plans purchases on cost, coverage, utilization, and savings. With target coverage analysis, you can set a specific percentage of On-Demand spend to be covered by Savings Plans. Savings Plans Purchase Analyzer uses your historical usage to recommend a new purchase amount to help you reach that target. You can further customize your analysis using parameters such as custom lookback period or excluding expiring Savings Plans, and compare cost, coverage, utilization, and savings across different coverage targets. You can view your recommendations through interactive charts or access your target coverage analysis via the Purchase Analyzer API.

AWS Application Migration Service (MGN) is now available as AWS Transform MGN. This name change reflects MGN's role as the proven replication engine powering AWS Transform, the agentic migration service. You can choose between two rehosting experiences. Use the AWS Transform MGN console for direct control over replication and cutover. Or use the AWS Transform agentic workflow, where an agent handles discovery, wave planning, landing zone setup, network creation, and rehosting or containerization on your behalf, accelerating your path to AWS. AWS Transform MGN retains all of its existing compliance certifications, including FedRAMP High, HIPAA, PCI DSS, ISO, and SOC 1, 2, and 3, so you can migrate with confidence. It is available in all commercial regions and both GovCloud (US) Regions. Visit the AWS Transform MGN product page and

Amazon Aurora DSQL introduces support for the PostgreSQL JSONB data type with optional compression. You can now use code and tools that depend on PostgreSQL's JSONB type with Aurora DSQL, making it easier to store semi-structured data alongside relational data. You can use the JSONB data type when creating or modifying tables to store semi-structured data such as system configuration metadata, API parameters, and event logs. With PostgreSQL compression enabled by default, larger JSONB payloads are stored more efficiently, helping reduce storage costs. Get started with Aurora DSQL for free with the AWS Free Tier. For information about Regional availability, see the AWS Region table. You can learn more about Aurora DSQL data types, including JSONB,

AWS Cost Anomaly Detection now includes AI-powered cost investigation, which uses Amazon Q to analyze the root cause of detected cost anomalies. Investigating a cost change typically requires correlating cost data with AWS CloudTrail events and resource activity, which can take hours. Cost investigation delivers a plain-language explanation in minutes, helping FinOps practitioners and engineering teams move from alert to action faster. When you investigate an anomaly, Amazon Q determines whether the cost change is usage-driven or rate-driven, identifies the contributing services, accounts, and regions, and for usage-driven changes, correlates with AWS CloudTrail to attribute the change to specific API calls and IAM principals. For organizations with a CloudTrail organization trail, the investigation works across all member accounts automatically. You can continue the conversation with follow

Amazon Redshift announces a new billing model for manual snapshots on Amazon Redshift Serverless and Amazon Redshift RG instances. With this enhancement, Amazon Redshift now meters manual snapshot storage based on the unique data blocks stored across your snapshots rather than the total size of each individual snapshot. This results in lower manual snapshot costs for customers who maintain multiple snapshots. Customers who maintain multiple manual snapshots for disaster recovery, testing, or long-term retention will see reduced storage costs. With this new billing model, you can take more frequent manual snapshots to achieve a better recovery point objective (RPO) without proportional cost increases, enabling more robust disaster recovery strategies. The new billing model automatically applies to both ex

Amazon RDS for SQL Server now offers TCO assessment in AWS Transform, so you can estimate costs when migrating on-premises SQL Server databases to RDS for SQL Server. Using AI-powered agents, AWS Transform analyzes your on-premises SQL Server environment and provides optimal database instance recommendations that meet your workload requirements and reduce cost. With AI-powered what-if analysis, you can evaluate different options, compare costs, and choose the best option for your migration. RDS for SQL Server assessment within AWS Transform supports both Bring Your Own Media (BYOM), which allows you to use your existing SQL Server licenses, and License Included (LI) options. The assessments include cost optimization using Database Savings Plans, which offer up to 20% savings compared to On-Demand pricing, and eligibility for the AWS Migration Acceleration Program (MAP), which provides credits and support to offset migration costs. You can start your assessment with a

Amazon Bedrock AgentCore Runtime now supports interactive shells through a new InvokeAgentRuntimeCommandShell API, opening a persistent, PTY-backed terminal directly into a running agent session over WebSocket. This complements the existing InvokeAgentRuntimeCommand API for one-shot execution, giving developers a full terminal experience inside an isolated microVM with colors, tab completion, Ctrl+C, terminal resize, and automatic reconnect on network drop. This is particularly important for developers hosting coding agents such as Claude Code, OpenAI Codex, Amazon Kiro on AgentCore Runtime. In addition to the asynchronous command execution they already had, they can now authenticate, drop into the microVM hosting their coding agent, and interact with it like a local terminal: interact with the agent, inspect files, run ad-hoc commands, or debug the environment state. The shell carries persistent state across commands within the same session, so environment variables, worki

AWS Glue Data Catalog now supports AWS IAM-based authorization for Amazon S3 Tables and Apache Iceberg materialized views. With IAM-based authorization, you can define all necessary permissions across storage, catalog, and query engines in a single IAM policy. This capability simplifies the integration of S3 Tables or materialized views with any AWS Analytics service, including Amazon Athena, Amazon EMR, Amazon Redshift, and AWS Glue. You can also opt in to AWS Lake Formation at any time to manage fine-grained access controls using the AWS Management Console, AWS CLI, API, and AWS CloudFormation. This feature is now available in AWS GovCloud (US-East) and AWS GovCloud (US-West) Regions. To learn more, visit the S3 Tables documentation and the AWS Glue Data Catalog documentation

Amazon OpenSearch Service expands its modernized operational analytics experience to GovCloud regions, including AWS GovCloud (US-East) and AWS GovCloud (US-West), enabling users to gain insights across data spanning managed domains and serverless collections from a single endpoint. The expansion includes Workspaces to enhance collaboration and productivity, allowing teams to create dedicated spaces. Discover is revamped to provide a unified log exploration experience supporting languages such as Piped-Processing-Language (PPL) and SQL, in addition to DQL and Lucene. Discover now features a data selector to support multiple sources, new visual design and query autocomplete for improved usability. This experience ensures users can access the latest UI enhancements, regardless of version of underlying managed cluster or collection. The expanded OpenSearch analytics helps users gain insights from their operational data by providing purpose-built features for observability, sec

Amazon Elastic Container Service (Amazon ECS) with AWS Fargate now supports 32vCPU compute configurations, enabling customers to run more demanding applications with greater flexibility and performance. AWS Fargate offers 32vCPU tasks with the following memory configurations: 60 GiB, 120 GiB, or 244 GiB, for both x86-based and ARM-based workloads on Linux. These new task sizes extend Amazon ECS’s capability to support high-performance computing use-cases, large-scale data processing, AI inference, and other compute-intensive workloads. With 32vCPUs and up to 244 GiB of memory, Amazon ECS customers can now deploy larger containers and scale applications beyond previous limits, all while leveraging the reliability, security, and scalability of AWS Fargate. To use the new 32vCPU task sizes, simply configure your task definitions to specify 32 as the vCPU value and select one of the new memory options (60, 120, or 244 GiB), then deploy your Amazon ECS services or tasks as usu