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AWS Cost Explorer now supports 'Analyze with Amazon Q', a new capability that delivers comprehensive cost explanations for any report you configure in Cost Explorer. With a single button click you now can receive detailed analysis from Amazon Q Developer covering your cost trends, top cost drivers, and anomalies. All analysis uses your exact filters and time-period and provides guidance to discover optimization opportunities through follow-up questions. Previously, cost analysis required manual investigation across multiple filters and data points. With 'Analyze with Amazon Q', you simply configure your Cost Explorer view and click a single button. Amazon Q analyzes your current context and delivers explanations directly in its chat panel, adapting to what you're viewing: historical explanations for past dates, forecast explanations for future dates, or both for mixed periods. You can then ask follow-up questions to explore any insights related to your cost data in greater d

AWS Compute Optimizer now identifies idle resources for Amazon DynamoDB provisioned tables, Amazon ElastiCache (Redis and Valkey), Amazon MemoryDB, Amazon DocumentDB (provisioned and serverless), Amazon WorkSpaces, and Amazon SageMaker endpoints. This expansion enables you to detect unused resources across more of your AWS environment and identify potential cost savings. Compute Optimizer analyzes utilization metrics to determine whether a resource is idle. Customers can set this lookback period based on the nature of their workloads. For each resource type, Compute Optimizer evaluates service-specific signals such as consumed capacity, cache hits, active connections, and CPU utilization. When Compute Optimizer identifies potential idle resources, it surfaces these recommendations, along with detailed utilization metrics and estimated savings in the console, enabling you to evaluate recommendations before acting. You can also view idle resource recommendations across all AWS

Effective today, Amazon MSK Express Brokers support automatic topic creation with Kafka Streams. Customers can now deploy their Kafka Streams applications on Express Brokers without needing to manually pre-create or manage topics for stateful operations. MSK Express Brokers are designed to deliver up to three times more throughput per broker, scale up to 20 times faster, and reduce recovery time by 90 percent. Kafka Streams uses topics to store state and repartition data for stateful operations. Previously, customers running Kafka Streams with Express Brokers had to manually name and pre-create these topics before deploying their application. With this launch, these topics are created automatically when the application starts, simplifying deployment and reducing operational setup for Kafka Streams applications on Express Brokers. This capability is available today in all

Amazon DocumentDB (with MongoDB compatibility) now supports engine minor versions, starting with 5.0.1. This release delivers enhanced aggregation capabilities with new operators ($rand, $pow, $dateToParts, $dateFromParts), the active connections metric to monitor instances, and granular command-level performance metrics in CloudWatch (find, insert, findAndModify, update, etc.). For a full list of what's included, see release notes. Minor versions provide new features and bug fixes within the same major version, giving you more control over when and how you upgrade your clusters. We recommend upgrading to the latest minor version to benefit from these performance enhancements, bug fixes, and new capabilities. You can specify minor version 5.0.1 when creating a new cluster, or manually upgrade an existing 5.0.0 cluster to 5.0.1 using the AWS Management Console or AWS CLI (via the mo

Amazon CloudWatch Logs Insights query language now supports 23 new commands and functions that give you new ways to query, parse, transform, and analyze your logs. Customers analyzing logs in CloudWatch Logs Insights often need to do conditional processing, string conversions, process IP addresses, parse different file formats, and execute complex stats commands. With this launch, CloudWatch Logs Insights provides new hash functions (md5, sha256), string functions (strcontains supporting case-insensitive search, split), conditional logic (if statement), and conversion functions (toNumber, toInt, toLong, toDouble). It also adds IP functions (ipv4ToNumber, isPrivateIP, isPublicIP, isReservedIP), analytics functions (rate, count_over_time, sum_over_time, offset, histogram), and parse functions (parse CSV, parse XML, parse multi, values, addtotals). Additionally, queries now support “limit any N” to fetch the first N results, and can use up to 10 stats commands. Th

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.

Amazon RDS for PostgreSQL 19 Beta 1 is now available in the Amazon RDS Database Preview Environment, allowing you to evaluate the pre-release of PostgreSQL 19 on Amazon RDS for PostgreSQL. You can deploy PostgreSQL 19 Beta 1 in the Amazon RDS Database Preview Environment that has the benefits of a fully managed database. PostgreSQL 19 adds native graph query support via SQL Property Graph Queries (SQL/PGQ), so you can express complex relationship traversals directly in standard SQL instead of building separate application logic or syncing data across two databases. It also introduces support for concurrent table repacking that rebuilds tables and reclaims unused storage, so production databases stay accessible during routine table maintenance. Logical replication now synchronizes sequence values to the replica automatically, eliminating manual sequence reconciliati

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 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

AWS Lambda Managed Instances (LMI) is now available in all commercial AWS Regions, except Israel (Tel Aviv), Middle East (Bahrain), Middle East (UAE), and Asia Pacific (Auckland).   LMI lets you run Lambda functions on managed Amazon EC2 instances, giving you access to specialized compute configurations and EC2 pricing advantages while maintaining Lambda's operational simplicity. LMI fully manages instance lifecycle, OS and runtime patching, routing, load balancing, and auto-scaling, so you can focus on writing code. You can process parallel requests within each execution environment, maximizing resource utilization and improving price-performance. You can further improve costs by leveraging EC2 pricing models including Compute Savings Plans and Reserved Instances. LMI is ideal for customers requiring specialized hardware configurations, as well as those with steady-state or predictable workloads seeking to optimize costs. You can continue building functions wit