7/13/2026, 12:00:00 AM ~ 7/14/2026, 12:00:00 AM (UTC)
Recent Announcements
OpenAI GPT-5.6 Sol, Terra, and Luna now generally available on Amazon Bedrock
GPT-5.6 Sol, Terra, and Luna are now generally available on Amazon Bedrock, bringing the smartest family of models from OpenAI yet to Bedrock’s next-generation inference engine built for high-performance, security and reliability. GPT-5.6 sets a new standard for intelligence and efficiency, allowing you to solve harder problems in less time and with more intelligence per token. The three models span capability tiers from flagship reasoning (Sol) to balanced performance (Terra) to fast, cost-efficient inference (Luna), all accessible through the Responses API on Amazon Bedrock. \n With GPT-5.6, you can build autonomous coding agents, run long-horizon genomics and biology analyses, and perform advanced cybersecurity research. Sol delivers state-of-the-art results on agentic coding benchmarks, Terra provides GPT-5.5-level performance at half the cost, and Luna brings fast, affordable inference at the lowest price point. GPT-5.6 also supports prompt caching with explicit cache breakpoints, so repeated context across agentic workflows is billed at a 90% discount and doesn’t compound cost as you scale. Pricing matches OpenAI first-party rates and usage counts toward your AWS commitments.
GPT-5.6 Sol is available in the following AWS Regions: 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 Amazon Bedrock Console or the Responses API on the bedrock-mantle endpoint. To learn more, see the Amazon Bedrock documentation and read the launch blog post.
Amazon Managed Service for Prometheus is now available in Asia Pacific (New Zealand) Region
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. \n 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.
Amazon DocumentDB (with MongoDB compatibility) now available as a skill in the Agent Toolkit for AWS
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.\n 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.
Gemma-4-E2B-it for is now available in Amazon SageMaker JumpStart
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.\n 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.
OpenAI privacy-filter for PII detection and masking is now available in Amazon SageMaker JumpStart
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.\n 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.
Qwen3 embedding and reranking models for retrieval are now available in Amazon SageMaker JumpStart
Today, AWS announced the availability of Qwen3-VL-Embedding-2B and Qwen3-Reranker-4B in Amazon SageMaker JumpStart, expanding the portfolio of foundation models available to AWS customers. These models from Qwen are designed for information retrieval and cross-modal understanding, enabling customers to build comprehensive search pipelines on AWS infrastructure. The two models are typically used in tandem: the embedding model performs efficient initial recall, while the reranker refines results in a subsequent re-ranking stage.\n These models address different stages of the retrieval pipeline with specialized capabilities:
Qwen3-VL-Embedding-2B accepts diverse inputs including text, images, screenshots, and videos, as well as inputs containing a mixture of these modalities, and generates semantically rich vectors that capture both visual and textual information in a shared space. It delivers performance across diverse multimodal tasks such as image-text retrieval, video-text matching, visual question answering, and multimodal content clustering, with support for over 30 languages.
Qwen3-Reranker-4B takes a query and document pair as input and outputs a precise relevance score to refine retrieval results. It supports text retrieval, code retrieval, text classification, text clustering, and bitext mining across over 100 languages, with user-defined instructions to enhance performance for specific tasks, languages, or scenarios.
With SageMaker JumpStart, customers can deploy any of these models with just a few clicks to address their specific AI use cases. To get started with these models, navigate to the Models section of SageMaker Studio or use the SageMaker Python SDK to deploy the models 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.\n 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 SageMaker HyperPod now supports custom AMIs (Amazon Machine Images) for Slurm clusters
Amazon SageMaker HyperPod now supports custom AMIs for Slurm-orchestrated clusters, enabling customers to deploy clusters with pre-configured, security-hardened environments that meet their specific organizational requirements. Customers deploying AI/ML workloads on HyperPod Slurm clusters need customized environments that meet strict security, compliance, and operational requirements while maintaining fast cluster startup times, but often struggle with complex lifecycle configuration scripts that slow deployment and create inconsistencies across cluster nodes.\n This capability allows customers to build upon HyperPod’s performance-optimized base AMIs while incorporating customized security agents, compliance tools, proprietary libraries, and specialized drivers directly into the image, delivering faster startup times, improved reliability, and enhanced security compliance. Security teams can embed organizational policies directly into base images, allowing AI/ML teams to use pre-approved environments that accelerate time-to-training while meeting enterprise security standards. You can specify custom AMIs when creating new HyperPod Slurm clusters using the CreateCluster API, adding instance groups with the UpdateCluster API, or patching existing clusters with the UpdateClusterSoftware API. Custom AMIs must be built using HyperPod’s public base AMIs to maintain compatibility with distributed training libraries and cluster management capabilities. This feature is available in all AWS Regions where Amazon SageMaker HyperPod is supported. To learn more about custom AMI support for Slurm clusters, see the Amazon SageMaker HyperPod User Guide.
AWS Blogs
AWS Japan Blog (Japanese)
- Weekly Generative AI with AWS — Week 07/2026/6
- AWS Transform for migrations now supports 13 language localizations
- VPC Resource Gateways implementation patterns and use cases
- AWS Weekly — 2026/7/6
AWS News Blog
- Amazon SQS turns 20: Two decades of reliable messaging at scale
- AWS Weekly Roundup: AWS Builder Center at 1 year, Network Scanning in Security Hub, Loom for AWS, and more (July 13, 2026)
AWS Architecture Blog
AWS Cloud Financial Management
AWS Big Data Blog
- Multi-cloud lakehouse architecture on AWS for Agentic AI, Part 1: Architecture and best practices
- How Razorpay Built Real-Time Anomaly Detection with Amazon MSK
AWS Compute Blog
AWS Contact Center
Containers
AWS Database Blog
- Rebuild large indexes on Aurora PostgreSQL with Blue/Green Deployments
- Provisioning DMS Schema Conversion via AWS CloudFormation
AWS for Industries
Artificial Intelligence
- OpenAI GPT-5.6 Sol, Terra, and Luna are now generally available on Amazon Bedrock
- When your brain works differently, AI isn’t a luxury—it’s accessibility
- Building an agentic AI solution at Bluesight with Amazon Bedrock
- Implement on-behalf-of token exchange for multi-tenant agents with Amazon Bedrock AgentCore Gateway
- Launching UI for generative AI inference recommendations in Amazon SageMaker AI
AWS Messaging Blog
- Build an AI campaign orchestrator with Amazon Bedrock and AWS End User Messaging
- Build an autonomous ecommerce assistant with AWS End User Messaging, Amazon Bedrock AgentCore, and OpenClaw