AWS Glue Data Quality makes ETL anomaly detection free and improves anomaly predictions

AWS Glue Data Quality now offers improved anomaly detection with a new observation mode that reduces detection of false anomalies and removes pricing for anomaly detection in ETL jobs. Customers using notebook-based or exploratory workflows now benefit from smarter anomaly detection that gracefully handles irregular data arrival intervals. This new capability avoids over-extrapolating trends by using a constant baseline instead of a linear trend, delivering more accurate alerts and reducing noise so teams can focus on genuine anomalies. The new anomaly detection observation mode is particularly useful for exploratory data analysis, datasets with flat or random patterns, workloads without predictable trends and cases where you run data quality checks on varying schedules or in interactive environments like notebooks. Additionally, anomaly detection for AWS Glue ETL jobs is now available at no additional cost, so you can monitor data quality anomalies across all your Glue pipelines without worrying about pricing. These improvements are available in all AWS commercial regions and AWS GovCloud (US) regions. To get started, visit the https://docs.aws.amazon.com/glue/latest/dg/data-quality.html. To learn more about pricing, see the https://aws.amazon.com/glue/pricing/.

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AWS Lambda announces scalable network bandwidth up to 3,000 Mbps for functions outside a VPC

https://aws.amazon.com/lambda/ now supports scalable network bandwidth for Lambda functions, enabling faster data transfer to and from your execution environment for latency-sensitive workloads. This feature enables functions outside a VPC configured with 2 GB of memory or more to access network bandwidth that scales proportionally, from 625 Mbps at 2 GB up to 3,000 Mbps at 10 GB. Customers use Lambda to build latency-sensitive data processing workloads, which need to transfer large volumes of data - up to several terabytes - from external data sources into the function’s execution environment for processing. As data volume and performance requirements grow, the existing limit of 625 Mbps can constrain data transfer speeds to and from an execution environment. With this launch, network throughput increases proportionally from 625 Mbps at 2 GB up to 3,000 Mbps at 10 GB, helping reduce function execution times and per-invocation costs while improving end-user experience. To get started, submit a request through https://console.aws.amazon.com/servicequotas/home/services/lambda/quotas under the Network bandwidth per execution environment quota to enable scalable network bandwidth on your account. Once enabled, bandwidth will scale automatically based on your function's memory configuration for all functions outside a VPC in your account. Scalable network bandwidth for functions outside a VPC is available at no additional charge in all commercial https://aws.amazon.com/about-aws/global-infrastructure/regions_az/. To learn more, visit the https://docs.aws.amazon.com/lambda/latest/dg/gettingstarted-limits.html page. 

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Amazon Keyspaces (for Apache Cassandra) is now available in the Canada West (Calgary) Region (ca-west-1)

https://aws.amazon.com/keyspaces/ is now available in the Canada West (Calgary) Region (ca-west-1), allowing customers in the Canada West Region to build Cassandra-compatible applications with lower latency while keeping their data within the Region to meet data residency requirements.  Amazon Keyspaces (for Apache Cassandra) is a scalable, highly available, and managed Apache Cassandra–compatible database service. Amazon Keyspaces is serverless, so you pay for only the resources that you use and you can build applications that serve thousands of requests per second with virtually unlimited throughput and storage.  This regional expansion enables organizations in Canada to build highly scalable, low-latency applications using familiar Cassandra Query Language (CQL) without the operational burden of managing Cassandra clusters. To learn more about on Keyspaces, visit the https://docs.aws.amazon.com/keyspaces/latest/devguide/what-is-keyspaces.html.

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AWS IAM Identity Center makes managment of AWS account access optional for new organization instances

AWS IAM Identity Center now lets you decide whether to enable management of AWS account access when you create a new organization instance. This allows you to use IAM Identity Center to manage access to AWS applications only, without the need to manage access to AWS accounts. This feature is available at the time of initial configuration of an IAM Identity Center instance and does not affect existing IAM Identity Center instances. IAM Identity Center enables you to connect your workforce identities to AWS once and offer AWS application owners across your organization streamlined access management. Application end users benefit from single sign-on, user awareness, and consistent authentication experience across AWS applications. Previously, this meant you also needed to manage access to AWS accounts. With this release, account management is now optional. When you choose not to enable management of AWS accounts, IAM Identity Center does not provision its service-linked role into your member accounts, which reduces the access surface in your environment. You can enable account management permissions later through instance settings or the UpdateInstance API.  This capability is available in all AWS Regions where IAM Identity Center is available. To get started, see Configure instance settings in the IAM Identity Center User Guide.

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Amazon Aurora serverless now scales faster to support agentic AI and other bursty workloads

https://aws.amazon.com/rds/aurora/serverless/ now delivers higher initial capacity during scale-up events, reaching up to 12 ACUs within a second and continuing to scale up to 256 ACUs as your workload grows. When the workload finishes, Aurora serverless automatically scales down to zero. This makes it especially well-suited for agentic AI applications, which typically have bursts of activity, long idle windows, and unpredictable traffic patterns. Aurora serverless handles all of it automatically, scaling capacity with your agents, so you only pay for what you use. This enhancement is enabled by default on all Aurora serverless clusters running on platform version 3 or 4, with no configuration changes required. Existing clusters on platform versions 1 and 2 can upgrade directly to the latest platform version 4 to benefit from these improvements. You can verify your cluster's platform version in the AWS Management Console under the instance configuration section, or via the RDS API's ServerlessV2PlatformVersion parameter. For pricing details and Region availability, visit https://aws.amazon.com/rds/aurora/pricing/. To learn more, read the Aurora serverless scaling https://docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/aurora-serverless-v2.html, and get started by creating an Aurora serverless database in just a few steps in the https://signin.aws.amazon.com/signin?redirect_uri=https%3A%2F%2Fconsole.aws.amazon.com%2Fconsole%2Fhome%3FhashArgs%3D%2523%26isauthcode%3Dtrue%26state%3DhashArgsFromTB_us-east-2_a53ff6382623e9a3&client_id=arn%3Aaws%3Asignin%3A%3A%3Aconsole%2Fcanvas&forceMobileApp=0&code_challenge=SCavOinSDkGpz8021yaDgWGy_E5aIbgB7HoRsIR47N0&code_challenge_method=SHA-256.

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Amazon DynamoDB now supports real-time vector search

Today, AWS announces the general availability of vector search for Amazon DynamoDB, a new feature to index and search vectors in real time. As vector datasets grow into the billions or trillions, vector search at scale traditionally trades off search speed, scale, and accuracy: latency climbs with vector count unless you accept lower recall or throughput. DynamoDB now supports native vector search with single-digit millisecond latency at 99%+ recall and is designed for any scale, even trillions of vectors. With DynamoDB vector search, you store vector embeddings alongside your other attributes and generate them using a model of your choice, including models available on Amazon Bedrock. You create a vector index and run approximate nearest neighbor searches, pick the vector index partition key to scale, and filter on attributes to scope results. You get the same serverless benefits you rely on today: zero infrastructure management, zero downtime, zero maintenance windows, and pay for only what you use. You can already use DynamoDB to store memory for AI agents, and with vector search you can now add semantic retrieval over that memory for agentic grounding, along with product similarity search, personalized advertising, retrieval augmented generation, and recommendation systems, with predictable performance. To learn more, visit the https://aws.amazon.com/blogs/aws/amazon-dynamodb-now-supports-real-time-vector-search-at-any-scale, https://aws.amazon.com/dynamodb, and https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/VectorSearch.html.

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AWS Marketplace adds AI Insights so buyers can understand pricing before they buy

You can now understand how a product's pricing works before you buy it. Available in the pricing section of the listing in AWS Marketplace, AI Insights explains each product's pricing in plain language: what a pricing unit maps to, how your bill changes as usage scales, how multiple pricing dimensions combine into one cost, and what is and isn't included. Answering these questions used to mean having to visit multiple websites and piecing together pricing details on your own. Now the context sits on the listing, so you can evaluate pricing and move to purchase without switching tabs. AI Insights cites sources so you can see where the explanations come from. AI Insights draws from the pricing the seller publishes on the Marketplace listing, and additional pricing context on the seller's public website. AI Insights is live today on most listings where external pricing context is available. It is available in all commercial AWS Regions where AWS Marketplace is available. To see it, open any product listing on the https://aws.amazon.com/marketplace/ website and scroll to the pricing section. Sellers can review what appears on their listing and request edits at any time through the Contact Us form linked in the https://docs.aws.amazon.com/marketplace/latest/userguide/ai-insights.html page on the AWS Marketplace Seller Guide.

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Amazon DynamoDB now supports real-time vector search at any scale

DynamoDB now supports native vector search with single-digit millisecond latency at 99%+ recall. It is designed for any scale, even trillions of vectors and requires zero infrastructure management.

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[Preview Announcement] Re-introducing Forward Proxy as AWS Network Firewall Functionality

You can now use your Network Firewall with all its existing filtering capabilities and features as an explicit forward proxy. On Nov 25, 2025, AWS introduced Network Firewall proxy in public preview to help customers exert centralized security controls against data exfiltration and malware injection. At the time, the Network Firewall proxy was introduced as a standalone product, separate from Network Firewall transparent firewall and used its own separate proxy security policy. Customers who tested it in preview shared that they want the Network Firewall proxy to maintain parity with Network Firewall’s existing set of capabilities and use the same security policy across the two functionalities. In keeping with customer feedback, we are reintroducing explicit proxy as a functionality of Network Firewall. With this launch, you can configure Network Firewall with your existing Firewall policy in a new no-source-preservation deployment where it can be used as an explicit proxy with all its existing features including managed rule groups, active threat defense, Geo-IP filtering, URL and domain category filtering, container attribute-based rules for Amazon EKS and Amazon ECS, etc. You can create a single security policy and use it for both explicit proxy and transparent firewall functionalities. Try out AWS Network Firewall in no-source-preservation deployment with proxy functionality in your test environment today in US East (Ohio) region. no-source-preservation Network Firewall is available for free during public preview. For more information, check https://docs.aws.amazon.com/network-firewall/latest/developerguide/nfw-no-source-preservation.html.

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Amazon Connect Customer now supports capacity planning in 15 or 30 minute intervals

Amazon Connect Customer now lets you generate capacity plans at the interval level (15-minute or 30-minute intervals), giving workforce planners visibility into more granular staffing requirements across Voice, Chat, Task, and Email channels. Interval-level plans capture how demand shifts throughout the day — such as a lunchtime surge in chat contacts or an end-of-day change in call volume — so you can align agent capacity with demand as it changes and staff precisely for each part of the day. You can also provide shrinkage assumptions and available headcount at the interval level for more accurate plans. Together, these capabilities improve planning accuracy and help you reduce over- and under-staffing, improving service levels and operational efficiency. This feature is available in all https://docs.aws.amazon.com/connect/latest/adminguide/regions.html#optimization_region where Amazon Connect Customer agent scheduling is available. To learn more about Amazon Connect Customer agent scheduling, click https://docs.aws.amazon.com/connect/latest/adminguide/forecasting-capacity-planning-scheduling.html.

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RDS SQL Server now supports publishing SQL Server Audit logs to CloudWatch

https://aws.amazon.com/rds/sqlserver/ now supports publishing SQL Server Audit logs to CloudWatch. SQL Server Audit is a native SQL Server feature that allows tracking and logging events that occur on the Database Engine. On Amazon RDS, you can create audits and audit specifications in the same way that you create them for on-premises SQL Server database servers. Now you can publish the audit logs to S3, CloudWatch, or both. If you enable both S3 and CloudWatch options, the audit log publication will be marked as "completed" only after the audit log files are uploaded to both S3 and CloudWatch. Once the audit logs are in CloudWatch, you can perform real-time analysis of the log data. If you enable retention, RDS keeps your audit logs on your DB instance for the configured period of time. For more information, see https://docs.microsoft.com/sql/relational-databases/security/auditing/sql-server-audit-database-engine in the SQL Server documentation. For detailed configuration instructions, see the https://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/Appendix.SQLServer.Options.Audit.html. This feature is available in all AWS Commercial and AWS GovCloud (US) Regions where Amazon RDS for SQL Server is available.

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Amazon S3 Vectors is now available in the AWS European Sovereign Cloud (Germany) Region

Amazon S3 Vectors is now available in the AWS European Sovereign Cloud (Germany) Region. Amazon S3 Vectors is purpose-built vector storage for AI agents, inference, Retrieval Augmented Generation (RAG), and semantic search at billion-vector scale. S3 Vectors is designed to provide the same elasticity, durability, and availability as Amazon S3, with a dedicated set of APIs that let you store, access, and query vectors without provisioning any infrastructure. For a full list of AWS Regions where Amazon S3 Vectors is available, see https://docs.aws.amazon.com/AmazonS3/latest/userguide/s3-vectors-regions-quotas.html. To learn more, visit the https://aws.amazon.com/s3/features/vectors/, https://docs.aws.amazon.com/AmazonS3/latest/userguide/s3-vectors.html, and the https://aws.amazon.com/s3/pricing/.

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Amazon Connect Customer now lets you export cases to CSV from the agent workspace

Amazon Connect Customer now supports exporting cases to a CSV file directly from the agent workspace, making it easier to share case data with internal teams and external stakeholders such as vendors, legal teams, or business partners. Agents can filter and select cases and choose which case fields to include in the export. Admins can control access using a security profile permission. Cases is available in the following AWS regions: US East (N. Virginia), US West (Oregon), Canada (Central), Europe (Frankfurt), Europe (London), Asia Pacific (Seoul), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), and Africa (Cape Town). To learn more and get started, visit the Cases https://aws.amazon.com/connect/cases/ and https://docs.aws.amazon.com/connect/latest/adminguide/case-bulk-export.html.http://aws.amazon.com

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Amazon RDS for SQL Server supports Developer Edition in additional AWS regions

https://aws.amazon.com/rds/sqlserver/ Developer Edition is now available in 13 additional AWS Regions: Asia Pacific (Hong Kong, Taipei, Hyderabad, Jakarta, Malaysia, Melbourne, New Zealand, Thailand), Canada West (Calgary), Europe (Milan, Spain), Israel (Tel Aviv), and Mexico (Central). SQL Server Developer Edition includes all the functionality of Enterprise Edition and is free of license charges for use as a development and test system. With this expansion, you can build and test applications in more regions closer to your data and end users, reducing latency and simplifying your development workflows. For more information about these features and region availability, see https://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/sqlserver-dev-edition.html. For pricing details, see https://aws.amazon.com/rds/sqlserver/pricing/. 

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Run interactive workloads on Amazon EMR on EC2 with Spark Connect

Amazon EMR on EC2 now supports interactive Apache Spark sessions with Spark Connect. Data engineers and data scientists can develop and debug Apache Spark applications interactively from managed notebooks in Amazon SageMaker Unified Studio and their own IDEs, such as Jupyter and Visual Studio Code, with each session running on dedicated EMR on EC2 clusters. You can also monitor and debug active and completed sessions in the EMR console.   An interactive session provides a persistent Spark context that spans across cells and scripts, letting you blend local Python code execution with remote Spark operations. Spark Connect's client-server architecture decouples your application client from the Spark driver and allows you to maintain your preferred development environment and tooling while Spark infrastructure runs on the cluster. This architecture supports workflows including ad hoc data exploration, iterative step-by-step debugging, and incremental PySpark job development before deploying to production. For observability, you get real-time session monitoring via the Spark UI, history tracking through the Spark History Server, and session management from the EMR console or API/CLI/SDK.   Interactive Sessions is available on Amazon EMR on EC2 with AWS runtime for Apache Spark (emr-spark-8.0) and later, in all AWS Regions where Amazon EMR is available, except the AWS GovCloud Regions and the China Regions. The Amazon SageMaker Unified Studio experience is available in https://docs.aws.amazon.com/sagemaker-unified-studio/latest/adminguide/supported-regions.html. To get started, visit the https://docs.aws.amazon.com/emr/latest/ManagementGuide/emr-spark-connect-sessions.html guide or the https://docs.aws.amazon.com/sagemaker-unified-studio/latest/userguide/what-is-sagemaker-unified-studio.html.

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Amazon Bedrock launches Web Search for OpenAI GPT models

Today, we are announcing the general availability of Web Search on Amazon Bedrock, a built-in server side tool that performs web search entirely within AWS, enabling OpenAI models (GPT-5.4, GPT-5.5, and GPT-5.6 Sol/Terra/Luna) to ground responses with current web knowledge while maintaining data residency within your secured AWS environment with zero data egress. Previously, adding web grounding required onboarding a third-party search provider, managing separate API keys and billing, building custom orchestration, and conducting additional compliance reviews for each external vendor. Web Search removes this heavy lifting by enable grounding with a single parameter in an existing API call, with no vendor onboarding, no external APIs to orchestrate, and no additional vendor security reviews to conduct. Web Search is built by Amazon, informed by years of experience across Alexa+, Amazon Quick and Kiro. It combines a web index operated by Amazon, spanning tens of billions of documents refreshed continually, with a built-in knowledge graph that provides verified facts. Rather than returning raw pages, Web Search performs semantic snippet extraction, delivering context-efficient results optimized for the model's context window with low latency. Web Search integrates through a standardized tool-use interface, compatible with the OpenAI Responses API. Simply add the web search tool to your API call, and Bedrock handles the entire search lifecycle server-side; a single API call returns a grounded response with citations. Web Search on Amazon Bedrock is generally available today in US East (N. Virginia), US East (Ohio), and US West (Oregon). To get started, read our blog post https://aws.amazon.com/blogs/machine-learning/introducing-web-search-on-amazon-bedrock-for-foundation-model-grounding/, review the https://docs.aws.amazon.com/bedrock/ for technical documentation, and visit the https://aws.amazon.com/bedrock/pricing/ for cost details.

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AWS Application and Network Load Balancers now support RFC 9151 compliant security policies

AWS Application Load Balancer (ALB) and Network Load Balancer (NLB) now support new TLS-based security policies that comply with RFC 9151 TLS server requirements for Commercial National Security Algorithm (CNSA) 1.0 suite requirements. These policies implement the cryptographic requirements defined by the US National Security Agency (NSA) for secure communications using TLS 1.2 and TLS 1.3 protocols. Customers who are required to meet CNSA 1.0 TLS security requirements can now use ALB and NLB with RFC 9151 compliant security policies. Broader interoperability policies are also supported, allowing you to implement CNSA by default while maintaining compatibility with non-CNSA clients during their transition to RFC 9151 compliance, minimizing service disruption. This feature is available for ALB and NLB in all AWS Commercial Regions, the AWS GovCloud (US) Regions, and the China region at no additional cost. To use this capability, update your existing ALB HTTPS listeners or NLB TLS listeners to a RFC 9151 compliant security policy, or select a compliant policy when creating new listeners through the AWS Management Console, CLI, API, or SDK. To learn more, visit the https://docs.aws.amazon.com/elasticloadbalancing/latest/application/describe-ssl-policies.html and https://docs.aws.amazon.com/elasticloadbalancing/latest/network/describe-ssl-policies.html documentation. Get started with https://aws.amazon.com/elasticloadbalancing/.

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Amazon EC2 C8g instances now available in additional regions

Starting today, Amazon Elastic Compute Cloud (Amazon EC2) C8g instances are available in AWS Europe (Paris), AWS Africa (Cape Town), AWS Israel (Tel Aviv), and AWS Canada West (Calgary) regions. These instances are powered by AWS Graviton4 processors and deliver up to 30% better performance compared to AWS Graviton3-based instances. Amazon EC2 C8g instances are built for compute-intensive workloads, such as high performance computing (HPC), batch processing, gaming, video encoding, scientific modeling, distributed analytics, CPU-based machine learning (ML) inference, and ad serving. These instances are built on the https://aws.amazon.com/ec2/nitro/, which offloads CPU virtualization, storage, and networking functions to dedicated hardware and software to enhance the performance and security of your workloads. AWS Graviton4-based Amazon EC2 instances deliver the best performance and energy efficiency for a broad range of workloads running on Amazon EC2. These instances offer larger instance sizes with up to 3x more vCPUs and memory compared to Graviton3-based Amazon C7g instances. AWS Graviton4 processors are up to 40% faster for databases, 30% faster for web applications, and 45% faster for large Java applications than AWS Graviton3 processors. C8g instances are available in 12 different instance sizes, including two bare metal sizes. They offer up to 50 Gbps enhanced networking bandwidth and up to 40 Gbps of bandwidth to the Amazon Elastic Block Store (Amazon EBS). To learn more, see https://aws.amazon.com/ec2/instance-types/c8g/. To get started, see the https://console.aws.amazon.com/.

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AWS Security Hub Extended adds supply chain security as its 10th category

The AWS Security Hub Extended plan now includes Supply Chain Security as its 10th security category, with Chainguard and Socket as the curated partners. As developers adopt open-source libraries at scale, security teams need confidence that the packages entering their environments are trustworthy and free from malicious code. With this addition, you can detect and block malicious dependencies before they are built into your applications, with the same streamlined activation and pay-as-you-go pricing as every other Extended category. This brings the Extended plan to 23 curated partner solutions. All solutions are on a single AWS bill and no required long-term commitments. Security Hub Extended is a plan within AWS Security Hub that helps simplify how you procure, deploy, and integrate a full-stack enterprise security solution across endpoint, identity, email, network, data, browser, cloud, AI, security operations, and supply chain. Security findings from all participating solutions are emitted in the Open Cybersecurity Schema Framework (OCSF) and automatically aggregated in AWS Security Hub. With the Extended plan, you can combine AWS and curated partner solutions to quickly identify and respond to risks that span boundaries. We will continue to expand the Extended plan based on customer feedback. The two new curated partner solutions are available today in all AWS commercial Regions where Security Hub is available. For a list of supported Regions, see the/about-aws/global-infrastructure/regional-product-services/?p=ngi&loc=4&refid=d8ec3b19-0f37-4f8c-8c12-189f913e205c For more information about pricing, visit the/security-hub/pricing/. To get started, visit the https://console.aws.amazon.com/securityhub/v2/home or /security-hub/

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Amazon RDS for SQL Server now support BYOM in additional commercial regions

https://aws.amazon.com/rds/sqlserver/ now supports Bring Your Own Media (BYOM) in 10 additional AWS Regions: Asia Pacific (Taipei, Hyderabad, Jakarta, Malaysia, Melbourne, New Zealand, Thailand), Europe (Milan, Spain), and Mexico (Central). With BYOM, you can adopt Amazon RDS as a managed database service and reuse your existing Microsoft SQL Server licenses with active Software Assurance, through Microsoft's License Mobility program. BYOM is supported on SQL Server 2019, 2022, and 2025. Amazon RDS for SQL Server BYOM is integrated with AWS License Manager so you can track license usage across your AWS environment for compliance. To learn more, see https://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/sqlserver-byom.html For pricing and regional availability, see https://aws.amazon.com/rds/sqlserver/pricing/.

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Amazon EC2 I8g instances now available in AWS Europe (Paris), Asia Pacific (Jakarta) regions

AWS announces the general availability of Amazon EC2 Storage Optimized I8g instances in AWS Europe (Paris) and Asia Pacific (Jakarta) regions. I8g instances are powered by AWS Graviton4 processors and offer the best compute performance in Amazon EC2 for storage-intensive workloads. I8g instances use the third generation AWS Nitro SSDs, local NVMe storage that deliver up to 65% better real-time storage performance per TB while offering up to 50% lower storage I/O latency and up to 60% lower storage I/O latency variability compared to I4g instances. These instances are built on the https://aws.amazon.com/ec2/nitro/, which offloads CPU virtualization, storage, and networking functions to dedicated hardware and software enhancing the performance and security for your workloads. Amazon EC2 I8g instances are designed for I/O intensive workloads that require rapid data access and real-time latency from storage. These instances excel at handling transactional, real-time, distributed databases, including MySQL, PostgreSQL, Hbase and NoSQL solutions like Aerospike, MongoDB, ClickHouse, and Apache Druid. They're also optimized for real-time analytics platforms such as Apache Spark, data lakehouse and AI LLM pre-processing for training. I8g instances are available in eleven different sizes including two metal sizes, 1.5 TiB of memory, and 45 TB local instance storage. They deliver up to 100 Gbps of network performance bandwidth, and 60 Gbps of dedicated bandwidth for Amazon Elastic Block Store (EBS). To learn more, visit https://aws.amazon.com/ec2/instance-types/i8g/ To begin your Graviton journey, visit the https://aws.amazon.com/ec2/graviton/level-up-with-graviton/. To get started, see https://console.aws.amazon.com/, https://aws.amazon.com/cli/, and https://docs.aws.amazon.com/AWSJavaScriptSDK/latest/AWS/EC2.html.

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OpenAI GPT-5.6 Sol, Terra, and Luna now support 1 million token context windows on Amazon Bedrock

GPT-5.6 Sol, Terra, and Luna now support 1 million token context windows on Amazon Bedrock, enabling you to process full codebases, lengthy documents, and multi-turn agent histories in a single request. Models reason over broader context and return more accurate, coherent responses without chunking or information loss. With a context window size of 1 million tokens, you can analyze entire repositories in a single pass for code review and migration, process long-form legal or regulatory documents end-to-end, and maintain full conversation history in multi-step agentic workflows. Prompt caching with explicit cache breakpoints applies to long context requests, so repeated context is billed at a 90% discount. Pricing matches OpenAI first-party rates and usage counts toward your AWS commitments. GPT-5.6 Sol is available in the following https://docs.aws.amazon.com/bedrock/latest/userguide/models-region-compatibility.html: US East (N. Virginia) and US East (Ohio). GPT-5.6 Terra and Luna are available in US East (N. Virginia), US East (Ohio), and US West (Oregon). Get started with Sol, Terra, and Luna using the https://us-east-1.console.aws.amazon.com/bedrock-mantle/home?region=us-east-1# or the Responses API on the bedrock-mantle endpoint. To learn more, see the https://docs.aws.amazon.com/bedrock/latest/userguide/model-cards-openai.html and read the https://aws.amazon.com/blogs/machine-learning/openai-gpt-5-6-sol-terra-and-luna-are-now-generally-available-on-amazon-bedrock/.

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AWS Lambda Provisioned Mode for Amazon SQS event source mappings now supports up to 10,000 event pollers

AWS Lambda now supports a maximum of 10,000 event pollers for Provisioned Mode for Amazon SQS event source mappings (ESMs), a 5x increase from the previous limit of 2,000. This enables you to reach up to 100,000 concurrent invocations, so you can build highly responsive and scalable event-driven applications with stringent performance requirements. Customers use Amazon SQS as an event source for Lambda to build mission-critical applications such as real-time order processing, financial transaction pipelines, IoT telemetry ingestion, and large-scale fan-out workloads, where delays in event processing can directly impact business outcomes. Provisioned Mode for SQS ESM allows you to configure a minimum and maximum number of event pollers and optimize the throughput for your application. However, the maximum number of pollers was previously limited to 2,000, which constrained the number of concurrent invocations that could be reached per ESM, and required customers to split their workload across multiple ESMs. With this launch, customers can reach up to 10,000 event pollers and 100,000 concurrent invocations per ESM, enabling them to build at-scale workloads with stringent latency and throughput requirements. This feature is generally available in all AWS Commercial Regions. You can activate Provisioned Mode for SQS ESM by configuring a minimum and maximum number of event pollers using the ESM API, AWS Console, AWS CLI, AWS SDK, AWS CloudFormation, and AWS SAM. You pay for the usage of event pollers based on a billing unit called Event Poller Unit (EPU). To learn more, read the https://docs.aws.amazon.com/lambda/latest/dg/with-sqs.html#sqs-provisioned-mode and https://aws.amazon.com/lambda/pricing/.

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AWS Organizations now provides maximum account quota visibility in Service Quotas

AWS Organizations customers can now view their maximum number of accounts quota and its utilization directly through AWS Service Quotas instead of relying on AWS Support or AWS account teams to determine their current account limit. Customers can now proactively plan account growth with ease by monitoring their current account quota utilization and requesting increases before reaching their limit. They can view this quota by logging into management account and accessing the Service Quotas console or calling the Service Quotas https://docs.aws.amazon.com/servicequotas/2019-06-24/apireference/API_GetServiceQuota.html This quota visibility is available now available in US East (N. Virginia). To learn more, see https://docs.aws.amazon.com/servicequotas/latest/userguide/gs-request-quota.html in the AWS Service Quotas documentation. For more information about AWS Organizations quotas and service limits, see the https://docs.aws.amazon.com/organizations/latest/userguide/orgs_reference_limits.html.

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AWS Transform continuous modernization is now generally available

https://aws.amazon.com/transform/continuous-modernization/ is now generally available in all https://docs.aws.amazon.com/transform/latest/userguide/regions.html. This capability helps engineering teams analyze and remediate technical debt across source code repositories at scale. Teams can connect GitHub organizations, GitLab groups, and Bitbucket workspaces, run analyses on demand or on a recurring schedule, and prioritize findings across technical debt, security, agentic readiness, modernization readiness, and custom analysis criteria. With today's launch, you can connect source code providers, initiate and schedule analyses, review findings, and create remediations directly from the AWS Transform web application. For findings with an associated remediation, continuous modernization creates branches and opens pull requests or merge requests containing validated code changes for review. Analysis and remediation run in your AWS account using your credentials, while your source code remains under your control. You can also use the AWS Transform Kiro Power, agent plugins, or AWS Transform CLI to work from your IDE or terminal, analyze local repositories, organize repositories using labels, and run analyses locally or remotely using Amazon EC2 or AWS Batch. To get started, open the https://console.aws.amazon.com/transform/home or use the https://docs.aws.amazon.com/transform/latest/userguide/ct-developer-tools.html. To learn more, see https://docs.aws.amazon.com/transform/latest/userguide/continuous-modernization.html in the AWS Transform User Guide.

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Amazon EC2 I7i instances now available in Asia Pacific (Thailand) and Israel (Tel Aviv) Regions

Amazon Web Services (AWS) announces the availability of high performance Storage Optimized Amazon EC2 I7i instances in the Asia Pacific (Thailand) and Israel (Tel Aviv) Regions. Powered by 5th Gen Intel Xeon Processors with an all-core turbo frequency of 3.2 GHz, these new instances deliver up to 23% better compute performance and more than 10% better price performance over previous generation I4i instances. Powered by 3rd generation AWS Nitro SSDs, I7i instances offer up to 45TB of NVMe storage with up to 50% better real-time storage performance, up to 50% lower storage I/O latency, and up to 60% lower storage I/O latency variability compared to I4i instances. I7i instances offer compute and storage performance for x86-based storage optimized instances in Amazon EC2 ideal for I/O intensive and latency-sensitive workloads that demand very high random IOPS performance with real-time latency to access the small to medium size datasets. Additionally, torn write prevention feature support up to 16KB block sizes, enabling customers to eliminate database performance bottlenecks. I7i instances are available in eleven sizes - nine virtual sizes up to 48xlarge and two bare metal sizes - delivering up to 100Gbps of network bandwidth and 60Gbps of Amazon Elastic Block Store (EBS) bandwidth. To learn more, visit the I7i instances https://aws.amazon.com/ec2/instance-types/i7i/.

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Amazon GameLift Streams now supports sharing streams with stream URLs

Amazon GameLift Streams now offers stream URLs, which give end users temporary, unauthenticated access to a playable stream session in a supported web browser. Recipients need no AWS account, no credentials, and no software install. To share a playable stream, create a stream URL for a stream group and one of its applications, set how long the stream URL stays valid and how many sessions it can start, and send the link. Each person who opens the link starts an independent stream session, and Amazon GameLift Streams routes them to a nearby streaming location from the locations you selected. No client integration or backend service is required. You can create, monitor, and revoke stream URLs in the Amazon GameLift Streams console or with the new CreateStreamUrl, GetStreamUrl, ListStreamUrls, and RevokeStreamUrl APIs. There is no additional charge for stream URLs. You are charged for the stream capacity that sessions started from a stream URL consume, as described on the https://aws.amazon.com/gamelift/streams/pricing/. For a full list of supported Regions, see the https://docs.aws.amazon.com/gameliftstreams/latest/developerguide/regions-quotas-rande.html. To get started, see https://docs.aws.amazon.com/gameliftstreams/latest/developerguide/stream-urls.html in the Amazon GameLift Streams Developer Guide and the https://docs.aws.amazon.com/gameliftstreams/latest/apireference/API_CreateStreamUrl.html. To learn more about the service, see the https://aws.amazon.com/gamelift/streams/. 

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Amazon SageMaker Unified Studio now supports Teradata Vantage

Amazon SageMaker Unified Studio now supports Teradata Vantage as a data source, enabling you to query and analyze your enterprise data warehouse data directly alongside your other data assets. With this new connection, you can combine business-critical data stored in Teradata with data from sources like Amazon Redshift, Amazon S3, and relational databases — all within a single, governed environment. This integration is particularly valuable when you need to correlate data across analytical and operational workloads. For example, you can join historical enterprise data and business metrics stored in Teradata with real-time datasets to gain deeper insights into customer behavior, financial performance, and operational trends. Data engineers and analysts can build data pipelines that bring together enterprise data warehouse assets with cloud-native datasets, streamlining cross-source workflows without switching between tools. To get started, add a Teradata Vantage connection under your project’s data section. Your Teradata data now appears in the data explorer alongside your other project data. From there, you can query it directly using the query editor, explore it in notebooks, or incorporate it into a visual ETL job, all without leaving the studio. Support for Teradata Vantage connections is available in https://docs.aws.amazon.com/sagemaker-unified-studio/latest/adminguide/supported-regions.html. To get started, see https://docs.aws.amazon.com/sagemaker-unified-studio/latest/userguide/connecting-new-data-source.html in the https://docs.aws.amazon.com/sagemaker-unified-studio/latest/userguide/what-is-sagemaker-unified-studio.html.  

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Amazon SageMaker AI serverless model customization now supports full fine-tuning

Amazon SageMaker AI serverless model customization now supports full fine-tuning for over 25 open-source models. These include popular models from gpt-oss, Gemma, Llama, Nemotron, and Qwen model families. In addition to parameter-efficient methods such as LoRA, which update a small subset of model weights, you can now update all parameters in the model for deeper adaptation when your use case requires it. Full fine-tuning allows the model to more thoroughly learn your domain-specific patterns, terminology, and task structure. This is particularly valuable when you need the model to acquire capabilities beyond surface-level style adjustments, such as learning specialized reasoning patterns, adopting complex output formats, or internalizing domain knowledge from large proprietary datasets. With serverless model customization, SageMaker manages all infrastructure provisioning and training orchestration, so you can run full fine-tuning jobs without provisioning or managing any infrastructure and you pay only for what you use. Serverless full fine-tuning on SageMaker is available in US East (N. Virginia), US West (Oregon), Asia Pacific (Tokyo), and Europe (Ireland). To get started, navigate to the JumpStart and Models page in Amazon SageMaker Studio to launch a customization job, or use the https://sagemaker.readthedocs.io/en/stable/model_customization/index.html. To learn more, and see the supported list of models, see the Amazon SageMaker AI https://docs.aws.amazon.com/sagemaker/latest/dg/model-customize-open-weight.html

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AWS Resilience Hub now provides recommended resilience tests

AWS Resilience Hub now offers recommended resilience tests that help platform engineering and site reliability teams validate how their services respond to and recover from known failure scenarios.  Resilience Hub provides pre-configured tests based on your service's architecture, configuration, and resilience policy. It uses AWS Fault Injection Service (FIS) to inject controlled faults and then evaluates whether your service recovers within your defined recovery objectives. With the AWS-recommended resilience tests, teams can validate readiness for scenarios such as Availability Zone impairment, Regional impairment, and dependency failure. Each test automatically targets resources in the service, injects the required faults, produces a pass or fail outcome based on alarm evaluation and recovery objectives, then generates a detailed test report. The recommended testing on the next generation of the AWS Resilience Hub is available in the following AWS Regions: US East (N. Virginia), US East (Ohio), US West (Oregon), Canada (Central), Europe (Ireland), Europe (London), Europe (Frankfurt), Europe (Paris), Europe (Stockholm), Asia Pacific (Mumbai), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), Asia Pacific (Seoul), and South America (São Paulo). To get started, visit the https://console.aws.amazon.com/resiliencehub/v2/home. To learn more about recommended resilience testing see the product page for the next generation of https://aws.amazon.com/resilience-hub/. 

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