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) 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.  

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. 

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.

 

​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. 
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 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.

 

​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.  

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 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.  

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 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.

 

​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.  

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 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 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.

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 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.
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.  

Amazon SageMaker HyperPod now supports AMI-based configuration for Slurm clusters that use continuous provisioning. Continuous provisioning adds nodes to the cluster as capacity becomes available, and this launch extends AMI-based configuration to clusters using this mode. With this support, clusters using continuous provisioning can be created without downloading, configuring, or uploading lifecycle configuration scripts to Amazon S3.

AMI-based configuration provisions nodes with the software and configurations needed for a production-ready environment to run AI/ML training workloads, including required software such as Docker, Enroot, and Pyxis, and configurations such as Slurm accounting, SSH key generation, and log rotation. When using continuous provisioning, each node is configured from the AMI as it is added to the cluster, without the need to manage lifecycle configuration scripts, so nodes become available to schedule jobs sooner. To enable AMI-based configuration, omit the LifeCycleConfig block from the instance group configuration when creating clusters via the API, or select «None» under Lifecycle scripts in Custom setup when using the SageMaker AI console. For additional customization on top of the AMI-based configuration baseline, an extension script can be provided by specifying the OnInitComplete parameter and SourceS3Uri in the LifeCycleConfig block via the API, or by providing the S3 URI in the «Extension script file in S3» field in Custom setup when using the console. Custom lifecycle configuration scripts remain fully supported for use cases that require full control over provisioning.

AMI-based node lifecycle configuration for Slurm clusters using continuous provisioning is available in all AWS Regions where SageMaker HyperPod is available. To get started, see Getting started with SageMaker HyperPod using the AWS CLI or Getting started with SageMaker HyperPod using the SageMaker AI console in the SageMaker user guide.

 

​Amazon SageMaker HyperPod now supports AMI-based configuration for Slurm clusters that use continuous provisioning. Continuous provisioning adds nodes to the cluster as capacity becomes available, and this launch extends AMI-based configuration to clusters using this mode. With this support, clusters using continuous provisioning can be created without downloading, configuring, or uploading lifecycle configuration scripts to Amazon S3. AMI-based configuration provisions nodes with the software and configurations needed for a production-ready environment to run AI/ML training workloads, including required software such as Docker, Enroot, and Pyxis, and configurations such as Slurm accounting, SSH key generation, and log rotation. When using continuous provisioning, each node is configured from the AMI as it is added to the cluster, without the need to manage lifecycle configuration scripts, so nodes become available to schedule jobs sooner. To enable AMI-based configuration, omit the LifeCycleConfig block from the instance group configuration when creating clusters via the API, or select «None» under Lifecycle scripts in Custom setup when using the SageMaker AI console. For additional customization on top of the AMI-based configuration baseline, an extension script can be provided by specifying the OnInitComplete parameter and SourceS3Uri in the LifeCycleConfig block via the API, or by providing the S3 URI in the «Extension script file in S3» field in Custom setup when using the console. Custom lifecycle configuration scripts remain fully supported for use cases that require full control over provisioning. AMI-based node lifecycle configuration for Slurm clusters using continuous provisioning is available in all AWS Regions where SageMaker HyperPod is available. To get started, see Getting started with SageMaker HyperPod using the AWS CLI or Getting started with SageMaker HyperPod using the SageMaker AI console in the SageMaker user guide.  

Starting today, Amazon Elastic Compute Cloud (Amazon EC2) R8in, R8ib, R8idn, and R8idb instances are available in the AWS Asia Pacific (Tokyo) and Europe (Frankfurt, Ireland) regions. These instances are powered by custom sixth generation Intel Xeon Scalable processors, available only on AWS and feature the latest sixth generation AWS Nitro cards. These instances deliver up to 43% better compute performance per vCPU compared to previous generation R6in and R6idn instances.

R8in, R8idn instances deliver 600 Gbps network bandwidth, the highest network bandwidth among enhanced networking EC2 instances. R8in instances are ideal for workloads such as real-time big data analytics, distributed web scale in-memory caches, caching fleets for AI/ML clusters, and Telco applications such as 5G User Plane Function (UPF). R8idn instances are ideal for network-intensive general purpose workloads requiring local storage, such as distributed compute, data analytics, and high-performance file systems.

R8ib, R8idb instances deliver up to 300Gbps EBS bandwidth, the highest among non-accelerated compute EC2 instances. R8ib instances are best suited for workloads that benefit from high block storage performance, such as high-performance file systems and NoSQL databases. R8idb instances are ideal for storage-intensive general purpose workloads such as large commercial databases, data lakes, and NoSQL databases that benefit from both high EBS throughput and low-latency local NVMe storage.

R8in, R8ib, R8idn, and R8idb instances support Elastic Fabric Adapter (EFA) networking on 48xlarge, 96xlarge, metal-48xl, and metal-96xl sizes. EFA networking enables lower latency and improved cluster performance for workloads deployed on tightly coupled clusters.

Amazon EC2 R8in an R8ib instances are available in US East (N. Virginia, Ohio), US West (Oregon), Asia Pacific (Tokyo), and Europe (Spain, Frankfurt, Ireland) regions, via Savings Plans, On-Demand, and Spot instances. For more information, visit the Amazon EC2 R8i instance page.

 

​Starting today, Amazon Elastic Compute Cloud (Amazon EC2) R8in, R8ib, R8idn, and R8idb instances are available in the AWS Asia Pacific (Tokyo) and Europe (Frankfurt, Ireland) regions. These instances are powered by custom sixth generation Intel Xeon Scalable processors, available only on AWS and feature the latest sixth generation AWS Nitro cards. These instances deliver up to 43% better compute performance per vCPU compared to previous generation R6in and R6idn instances.
R8in, R8idn instances deliver 600 Gbps network bandwidth, the highest network bandwidth among enhanced networking EC2 instances. R8in instances are ideal for workloads such as real-time big data analytics, distributed web scale in-memory caches, caching fleets for AI/ML clusters, and Telco applications such as 5G User Plane Function (UPF). R8idn instances are ideal for network-intensive general purpose workloads requiring local storage, such as distributed compute, data analytics, and high-performance file systems. R8ib, R8idb instances deliver up to 300Gbps EBS bandwidth, the highest among non-accelerated compute EC2 instances. R8ib instances are best suited for workloads that benefit from high block storage performance, such as high-performance file systems and NoSQL databases. R8idb instances are ideal for storage-intensive general purpose workloads such as large commercial databases, data lakes, and NoSQL databases that benefit from both high EBS throughput and low-latency local NVMe storage. R8in, R8ib, R8idn, and R8idb instances support Elastic Fabric Adapter (EFA) networking on 48xlarge, 96xlarge, metal-48xl, and metal-96xl sizes. EFA networking enables lower latency and improved cluster performance for workloads deployed on tightly coupled clusters.
Amazon EC2 R8in an R8ib instances are available in US East (N. Virginia, Ohio), US West (Oregon), Asia Pacific (Tokyo), and Europe (Spain, Frankfurt, Ireland) regions, via Savings Plans, On-Demand, and Spot instances. For more information, visit the Amazon EC2 R8i instance page.