AWS News - 2026-07-14

Amazon Redshift Serverless now automatically preserves zero-ETL and Amazon S3 event integrations when restoring a namespace from a snapshot or recovery point to the same serverless namespace. Previously, restoring a snapshot marked associated integrations as failed, requiring you to manually recreate them after the restore completed. This meant additional time reconfiguring data pipelines and potential data ingestion gaps during the rebuild process.

With this enhancement, integrations are automatically maintained and resume operating after the restore completes, simplifying data ingestion workflows and reducing administrative overhead. This streamlines disaster recovery and testing workflows by reducing manual configuration steps and potential errors. This applies to restores within the same serverless namespace only. Restoring to a different namespace or restoring provisioned clusters does not maintain integrations.

This feature is available in all AWS regions where Amazon Redshift Serverless is available. To learn more about restoring Amazon Redshift Serverless namespaces, please visit our documentation or the Redshift behaviour change page.

Today, AWS announced the availability of gemma-4-E2B-it in Amazon SageMaker JumpStart, expanding the portfolio of foundation models available to AWS customers. This model from Google DeepMind is a multimodal, instruction-tuned model optimized for efficient local execution, enabling customers to build capable AI applications on AWS infrastructure.

Gemma-4-E2B-it processes text, image, and audio input and generates text output, with a built-in reasoning mode that lets the model think step-by-step before answering. It offers image understanding including object detection, document parsing, screen and UI understanding, chart comprehension, and OCR; video understanding; native function calling for agentic workflows; code generation, completion, and correction; and multilingual support across dozens of languages.

With SageMaker JumpStart, customers can deploy this model with just a few clicks to address their specific AI use cases. To get started with this model, navigate to the Models section of SageMaker Studio or use the SageMaker Python SDK to deploy the model to your AWS account. For more information about deploying and using foundation models in SageMaker JumpStart, see the Amazon SageMaker JumpStart documentation.

Today, AWS announced the availability of privacy-filter in Amazon SageMaker JumpStart, expanding the portfolio of foundation models available to AWS customers. This model from OpenAI is a bidirectional token-classification model for personally identifiable information (PII) detection and masking in text, enabling customers to build data sanitization workflows on AWS infrastructure.

Privacy-filter is fast, context-aware, and tunable, designed for high-throughput data sanitization workflows that teams can run on-premises. It labels an input sequence in a single forward pass and detects PII span categories including account numbers, addresses, emails, names, phone numbers, URLs, dates, and secrets. 

With SageMaker JumpStart, customers can deploy this model with just a few clicks to address their specific AI use cases. To get started with this model, navigate to the Models section of SageMaker Studio or use the SageMaker Python SDK to deploy the model to your AWS account. For more information about deploying and using foundation models in SageMaker JumpStart, see the Amazon SageMaker JumpStart documentation.

Today, AWS announced the availability of Voxtral-Mini-4B-Realtime-2602 in Amazon SageMaker JumpStart, expanding the portfolio of foundation models available to AWS customers. This model from Mistral AI is a multilingual, real-time speech-transcription model, enabling customers to build low-latency speech applications on AWS infrastructure.

Voxtral-Mini-4B-Realtime excels at high-quality transcription of audio to text with a natively streaming architecture that enables real-time transcription. It supports multilingual transcription across 13 languages and offers configurable transcription delays, allowing users to balance latency and accuracy based on their needs.

With SageMaker JumpStart, customers can deploy this model with just a few clicks to address their specific AI use cases. To get started with this model, navigate to the Models section of SageMaker Studio or use the SageMaker Python SDK to deploy the model to your AWS account. For more information about deploying and using foundation models in SageMaker JumpStart, see the Amazon SageMaker JumpStart documentation.

Amazon DocumentDB (with MongoDB compatibility) now supports 46 additional MongoDB aggregation operators and cursor methods starting from minor version 8.0.1. This release significantly expands query API compatibility, making it easier to migrate MongoDB workloads to Amazon DocumentDB without application code changes.

New capabilities span seven categories:

• Accumulators (13): $top, $topN, $bottom, $bottomN, $firstN, $lastN, $maxN, $minN, $count, $median, $percentile, $stdDevPop, $stdDevSamp

• Trigonometry (15): $sin, $cos, $tan, $asin, $acos, $atan, $atan2, $sinh, $cosh, $tanh, $asinh, $acosh, $atanh, $degreesToRadians, $radiansToDegrees 

• Bitwise aggregation (4): $bitAnd, $bitOr, $bitXor, $bitNot

• Arithmetic (3): $round, $trunc, $sigmoid

• Data size and type (4): $binarySize, $bsonSize, $isNumber, $toUUID

• Timestamp (2): $tsIncrement, $tsSecond 

• Stages and other (5): $sortByCount, $listSearchIndexes, $sampleRate, cursor.min(), cursor.max()

These operators are available starting from Amazon DocumentDB 8.0.1 in all regions where Amazon DocumentDB is available. To learn more, see Supported MongoDB APIs, operations, and data types and Amazon DocumentDB release notes.

Amazon DocumentDB (with MongoDB compatibility) is now available as a specialized database skill in the Agent Toolkit for AWS. With this skill, AI coding agents can set up, manage, migrate, optimize, and troubleshoot Amazon DocumentDB clusters using step-by-step best-practice workflows, reducing errors and helping developers move faster without needing to look up DocumentDB operations guidance manually.

The Amazon DocumentDB skill covers seven workflows: cluster provisioning, schema design, MongoDB compatibility assessment, DMS-based migration with change data capture, performance tuning, a 41-check well-architected review, and major version upgrades. When paired with the AWS MCP Server, agents can execute AWS CLI commands and run diagnostic queries with IAM-based guardrails, CloudTrail audit logging, and sandboxed execution. The skill also works standalone via the AWS CLI for teams that prefer local execution.

The Amazon DocumentDB skill is available at no additional charge as part of the Agent Toolkit for AWS. To get started, see the Amazon DocumentDB skill on GitHub or browse the Agent Toolkit Quick Start guide. For more information about Amazon DocumentDB, see the Amazon DocumentDB Developer Guide.

Amazon Managed Service for Prometheus is now available in Asia Pacific (New Zealand) Region. Amazon Managed Service for Prometheus is a fully managed, Prometheus-compatible monitoring service that makes it easy to monitor and alert on operational metrics at scale. 
 
Amazon Managed Service for Prometheus is available in multiple AWS Regions. Customers can send up to 1 billion active metric series to a single workspace and can create many workspaces per account, where a workspace is a logical space dedicated to the storage and querying of Prometheus metrics.

To learn about Amazon Managed Service for Prometheus pricing, visit the pricing page.

You can now write custom data directly into CloudFront access logs using a new helper method available from within CloudFront Functions. CloudFront Functions run lightweight JavaScript at the edge for tasks like URL rewrites, header manipulation, and request routing. Previously, you could only emit log data to Amazon CloudWatch Logs as a separate log file from your CloudFront access logs. With this launch, you no longer need to correlate function decisions with CloudFront access log data across separate logging systems.

You can call cf.logCustomData() from viewer request or viewer response functions to log values such as A/B test variant assignments, authentication outcomes, or routing decisions directly into the CloudFront access log record for that request. This works with both CloudFront real time log configurations and standard logging (v2), so you can analyze function behavior and request outcomes in a single query. The existing console.log() functionality remains available and the two methods can be used together in the same function.

Amazon CloudFront Functions custom log data is available today in all CloudFront edge locations. There is no additional charge for using cf.logCustomData(). Standard CloudFront Functions invocation pricing and access log delivery charges apply. To get started, visit CloudFront Functions helper methods.

AWS Storage Gateway now allows you to copy file shares across gateways directly from the Storage Gateway console. When you initiate a copy, the console reads the configuration of your source share and creates a new file share on the destination gateway with compatible settings preserved.

Previously, you had to manually recreate each file share on the destination gateway and re-enter configuration details one by one. Now you can copy a file share and have its configuration automatically applied to a destination gateway, significantly reducing the time and effort required for migrations such as upgrading to AL2023. A guided experience surfaces any configurations that require your attention before the new share is created, helping ensure a smooth transition without missing critical settings.

To get started, navigate to your file shares in the Storage Gateway console, select a share, and choose Copy to gateway. This capability is available in commercial AWS Regions. To check regional availability, see AWS Capabilities. For more information, visit the AWS Storage Gateway User Guide.  

AWS Builder Center turned one year old last week. Launched on July 9, 2025, the platform has grown from a community hub with Wishlist voting, community profiles, and a toolbox into a full ecosystem with sandbox environments, workshops, Spaces, and a Builders’ Library. To mark the anniversary, Rick Suttles published a full feature timeline covering […]

On July 13, 2006, we launched Amazon Simple Queue Service (Amazon SQS) as one of the first three services available to customers, alongside Amazon EC2 and Amazon S3. We had learned firsthand that distributed systems need a reliable way to pass messages between components without creating tight dependencies. If one service called another directly and […]

Kiro は誕生から 1 周年を迎え、AI コーディングからエージェンティックエンジニアリングへと進化しました。7 月 13 日から 17 日まで（太平洋時間）の 1 週間、コミュニティへの感謝を込めたバースデーウィークを開催します。ボーナスクレジットを獲得できるデイリーコーディングチャレンジ、7 月 15 日のバースデーパーティーライブストリーム、コミュニティスポットライト、そしていくつかのサプライズをご用意しています。#BuildWithKiro #TeamKiro #1YearOfKiro で、皆さんの 1 年目を一緒にお祝いしましょう。

みなさん、こんにちは。AWS ソリューションアーキテクトの池田です。 AI エージェントの活用に取り組む企業が […]

本ブログは ハイテクインター株式会社 様と Amazon Web Services Japan 合同会社が共同 […]

AWS SimuLearn に学習プランとデジタルバッジが追加されました。ロールや業界に合わせた体系的な学習パスで、顧客シミュレーション・ハンズオンラボ・DIY 検証を通じて実践的なクラウドスキルを習得できます。学習プランを完了すると Credly 経由でデジタルバッジも取得できます。

In this post, we introduce the UI for optimized generative AI inference recommendations in Amazon SageMaker AI Studio, a low-code no-code (LCNC) experience. The API already gives you programmatic access to recommendations, but it assumes you know which parameters to set and how to interpret raw benchmark output. The UI removes that assumption. It guides you through preset use-case profiles, visual comparisons of results, and one-click deployment, so teams without deep infrastructure expertise can get a validated configuration on their own.

Building multi-tenant agents with Amazon Bedrock AgentCore and Apply fine-grained access control with Bedrock AgentCore Gateway interceptors establish the conceptual foundation for on-behalf-of (OBO) token exchange in agentic systems. This post is the implementation guide. It walks through a complete multi-tenant OBO setup against Okta, shows the JSON Web Token (JWT) claim transformations on each hop, and demonstrates how audience binding produces defense in depth that scales across tenants.

In this post, we describe how Bluesight used two AWS engagements and Amazon Bedrock AgentCore to evolve from a single-product AI prototype to Prism, a unified agentic AI solution spanning six healthcare compliance products. Prism Assistant for ControlCheck launched in May 2026 and is already in use by 20 health systems. A more complex multi-product agentic solution is on track for later in 2026.

In this post, I share how AI serves as an accessibility tool for neurodivergent professionals. The system is built on Amazon Quick on your desktop, an AI-powered desktop and web assistant that compensates for executive function gaps every day.

Today, GPT-5.6 Sol, Terra, and Luna from OpenAI are generally available on Amazon Bedrock, bringing the smartest family of models from OpenAI yet to Amazon Bedrock’s next-generation inference engine built for high-performance, security and reliability.

A single cold start can push your Java Lambda function’s response time from milliseconds to seconds, enough to violate your p99 SLA, timeout a downstream service, and page your on-call. The Java Virtual Machine (JVM) performs best in long-running processes. Its Just-In-Time (JIT) compiler progressively optimizes code over thousands of invocations. Standard serverless execution environments […]