Amazon SageMaker(AMZN)

Amazon Bedrock now supports the OpenAI GPT-5.6 models (Terra and Luna) in India, with India Geo cross-Region inference. Customers with regulatory requirements of in-country inferencing can now use OpenAI models at scale ensuring that inferencing is processed within India. Cross-Region inference automatically routes inference requests across multiple AWS Regions to give you higher throughput, without you needing to manage capacity across multiple Regions. The new India Geo inference profiles—in.openai.gpt-5.6-terra for Terra and in.openai.gpt-5.6-luna for Luna—route requests only within the India geography, across AWS Regions such as Asia Pacific (Mumbai) and Asia Pacific (Hyderabad), so you can scale to meet demand while keeping data processed within India to meet data residency requirements. The models run on the bedrock-runtime endpoint with support for the Responses, Chat Completions, and Converse APIs, and work with the same account-level controls you already use for other models o

AWS Identity and Access Management (IAM) now enables AWS workloads in the AWS European Sovereign Cloud (Germany) Region to securely authenticate with external services using short-lived JSON Web Tokens (JWTs). The AWS European Sovereign Cloud is an independent cloud for Europe entirely located within the European Union (EU), designed to help customers meet their evolving sovereignty requirements. With outbound identity federation, AWS workloads can securely authenticate with third-party cloud providers, SaaS providers, and self-hosted applications without using long-term credentials or implementing complex workarounds. Customers can exchange their AWS IAM credentials for cryptographically signed, short-lived JWTs, providing a simple and secure mechanism to access external services. These tokens contain rich context about the AWS workloads, enabling external services to implement fine-grained access control. Administrators can control access to token generation and enforce token propert

On Aug 18, 2026, Amazon announced critical security patch update (CSPU) for Amazon Corretto Long-Term Support (LTS) and Feature Release (FR) versions of OpenJDK. Corretto 26.0.2.11.1, 25.0.4.8.1, 21.0.12.9.1, 17.0.20.10.1, 11.0.32.10.1, and 8u504 are now available for download . Amazon Corretto is a no-cost, multi-platform, production-ready distribution of OpenJDK. Visit Corretto home page to download Corretto 26, Corretto 25, Corretto 21, Corretto 17, Corretto 11, or Corretto 8. You can also get the updates on your Linux system by configuring a Corretto Apt, Yum, or Apk repo . Feedback is welcomed !

Today, AWS announces the general availability of AgentCore payments, a capability within Amazon Bedrock AgentCore that enables AI agents to autonomously discover, access, and pay for paid APIs, MCPs, and content with just a few lines of code. AgentCore payments provides the security, guardrails, and observability enterprises need to deploy transacting agents in production at scale. AgentCore payments integrates with Coinbase and Stripe Privy wallets for microtransactions, provides payment orchestration across protocols, enforces configurable payment limits at the infrastructure layer, and delivers end-to-end observability through AgentCore Observability. At general availability, AgentCore payments includes Quick Create for Coinbase credential provisioning directly within the AgentCore console, a curated Coinbase Bazar MCP server of pay-per-use x402 endpoints via AgentCore gateway, support for the Machine Payment Protocol (MPP), and the "upto" scheme in the x402 protocol for pay-per-inf

Amazon SageMaker Unified Studio now supports data profiling and anomaly detection, powered by AWS Glue Data Quality. Data stewards, engineers and analysts can generate statistical profiles of their data to understand its shape and completeness, and track how these statistics change over time. Anomaly detection helps identify when data points drift from historical patterns without requiring predefined thresholds or custom rules. These capabilities are available for both data at rest in catalog tables and data in transit within Visual ETL jobs. With this launch, a dedicated Data profile tab on catalog tables provides on-demand and scheduled profiling that computes dataset-level and column-level statistics. As profile history accumulates, anomaly detection builds a baseline of expected behavior and flags data points that fall outside the predicted range. This is particularly useful when you may not be aware of specific thresholds, or when expected values change over time and fixed rules c

Amazon Managed Workflows for Apache Airflow (Amazon MWAA) Serverless now supports running custom Python functions and shell scripts directly in the serverless runtime using PythonOperator and BashOperator. With this launch, data engineering teams can execute the code patterns they rely on daily, including data transformations, format conversions, and data quality checks, without provisioning additional infrastructure. Package your Python modules or shell scripts as code packages, upload them to Amazon S3, and reference them when creating or updating a workflow. The service snapshots your code at workflow creation time and uses that snapshot for all subsequent runs, ensuring consistency across executions. This feature is available in all AWS Regions where Amazon MWAA Serverless is available. To learn more, visit Using Python and Bash operators .

Starting today, Amazon Elastic Compute Cloud (Amazon EC2) R8i instances are available in the Israel (Tel Aviv) region. These instances are powered by custom Intel Xeon 6 processors, available only on AWS, delivering the highest performance and fastest memory bandwidth among comparable Intel processors in the cloud. The R8i instances offer up to 15% better price-performance, and 2.5x more memory bandwidth compared to previous generation Intel-based instances. They deliver 20% higher performance than R7i instances, with even higher gains for specific workloads. They are up to 30% faster for PostgreSQL databases, up to 60% faster for NGINX web applications, and up to 40% faster for AI deep learning recommendation models compared to R7i.   R8i instances are a great choice for all memory-intensive workloads, especially for workloads that need the largest instance sizes or continuous high CPU usage. R8i instances offer 13 sizes including 2 bare metal sizes and the new 96xlarge size

IAM Policy Autopilot can now generate baseline IAM policies directly from a Terraform plan file. IAM Policy Autopilot is an open source tool, launched at re:Invent 2025, that analyzes your code to deterministically create scoped-down IAM policies you can refine as your application evolves, reducing the time you spend writing IAM policies and troubleshooting access issues. Until now the tool analyzed application source code, but it was not possible to generate policies for deploying AWS infrastructure defined via Infrastructure as Code. Now you can pass a Terraform plan file as input, and IAM Policy Autopilot applies a deterministic analysis to produce a policy scoped to the CRUD functions of the resources in that plan. The generated policies reference specific resource ARNs rather than wildcards, when possible. Supporting policy generation for deploying AWS infrastructure defined via Terraform has been the most requested capability since IAM Policy Autopilot launched, and it complement

Starting today, Amazon RDS for PostgreSQL 19 Beta 3 is available in the Amazon RDS Database Preview Environment , allowing you to evaluate the pre-release of PostgreSQL 19 on Amazon RDS for PostgreSQL. PostgreSQL 19 Beta 3 adds new capabilities for query performance and autovacuum management. The new pg_stat_autovacuum_scores view helps you monitor and tune autovacuum prioritization. Parallel autovacuum can now use multiple workers to speed up maintenance on large tables. The new pg_plan_advice module lets you lock in efficient query plans to avoid unexpected slowdowns. Eager aggregation improves analytical queries by grouping data earlier, so queries process fewer rows and complete faster. Beta 3 also includes bug fixes and stability improvements from the Beta 2 testing period. Refer to the PostgreSQL community announcement for more details. Amazon RDS Database Preview Environment database instances are retained for a maximum period of 60 days and are automatically deleted after the r

Amazon WorkSpaces now supports nested virtualization for Amazon WorkSpaces Personal and Amazon WorkSpaces Core Managed Bundles. With nested virtualization enabled, developers can run Docker Desktop, Windows Subsystem for Linux (WSL2), and other hypervisor-dependent tools directly on their Windows WorkSpaces, and KVM-based workloads, Android emulators, and nested containers on their Linux WorkSpaces - without needing separate physical hardware or workaround environments. Nested virtualization is supported on license-included (public and custom) bundles, Bring Your Own License (BYOL) bundles, and Bring Your Own Protocol (BYOP) bundles running Windows Server 2019, Windows Server 2022, Windows Server 2025, Windows 11, Ubuntu 22.04, Ubuntu 24.04, Red Hat Enterprise Linux 8, Red Hat Enterprise Linux 9, Rocky Linux 8, or Rocky Linux 9 with DCV protocol. Power (4 vCPU) or higher is recommended. Nested virtualization is not supported on GPU bundles, the PCoIP protocol, or Amazon WorkSpaces

Amazon S3 Metadata and annotations are now available in AWS GovCloud (US-East) and AWS GovCloud (US-West) Regions, giving you the easiest and fastest way to discover, understand, and enrich your S3 data. S3 Metadata automatically captures system-defined details like object size and source, and stores them in Amazon S3 Tables, optimized for near real-time tabular queries that simplify business analytics and real-time inference applications. Annotations extend S3 Metadata so you can attach rich business context directly to your objects in JSON, XML, or YAML, with up to 1 GB per object. This gives your AI agents and analytics tools the context they need to find and use the right data without building or maintaining separate metadata systems. Annotations share the same durability and consistency properties as the object, move with the object during copy and replication operations, and are removed when the object is deleted. This makes it easy to keep context current as your data evolves. A