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SQL Server on Azure Local is now generally available

Some of the world’s most critical databases run in places where cloud connectivity cannot be assumed. A remote industrial site, for example, may need production systems to continue operating when external connectivity is unavailable. A regulated organization may need sensitive data and processing to remain within sovereign boundaries.

Today, SQL Server on Azure Local is generally available for connected and disconnected operations. With this release, customers can modernize where their data resides while maintaining control over infrastructure, connectivity, and data placement. They can bring AI closer to their data with Foundry Local on Azure Local, currently in preview, and use eligible existing SQL Server licensing investments.

Explore SQL Server on Azure LocalRun SQL Server where your data needs to staySQL Server has a long history of supporting mission-critical workloads on customer infrastructure. Azure Local builds on that flexibility by bringing Azure infrastructure to customer-owned environments, giving organizations a consistent platform for running SQL Server across datacenters and edge locations.

Customers can choose the deployment model that fits each environment:

Connected environments: Run SQL Server locally on Azure Local, while using Azure Arc to manage SQL Server resources through Azure.Azure Local Disconnected Operations (ALDO): Run SQL Server in environments where external connectivity is restricted, intermittent, or unavailable, with operations continuing locally.This means organizations can choose the deployment approach that fits the requirements of individual environments rather than making the same connectivity decision across their entire data estate.

SQL Server on Azure Local – connected and disconnected operations.Bring AI closer to your dataModernizing infrastructure is only part of the opportunity. Organizations also want to bring generative AI to existing enterprise data, including information that cannot leave their environment.

With Foundry Local, customers can run AI models on Azure Local infrastructure alongside SQL Server, bringing inferencing closer to the data. This enables organizations to explore AI-powered applications while keeping sensitive information and processing within their environment.

This can be particularly valuable in sovereign, regulated, and edge scenarios, where organizations want the benefits of AI but need greater control over where models execute and where their data is processed.

The goal is simple: customers should not have to move sensitive data somewhere else simply to begin building intelligent applications around it.

Build on your existing SQL server investmentsModernization should also make it easier to build on existing investments.

SQL Server is licensed separately from the Azure Local infrastructure platform. For connected deployments, customers can use eligible existing SQL Server licenses, including licenses with Software Assurance or qualifying subscriptions, or use pay-as-you-go licensing through Azure Arc.

For fully disconnected deployments, customers can use eligible existing SQL Server licenses through Azure Hybrid Benefit.

This gives organizations options to modernize their SQL Server environments while taking advantage of eligible licensing investments they already own.

A foundation for sovereign and edge dataEnterprise data estates increasingly span cloud, datacenter, edge, and sovereign environments. The opportunity is not to force every workload into the same deployment model, but to give organizations a consistent path to modernization across them.

SQL Server on Azure Local extends that choice to mission-critical SQL Server workloads, helping customers modernize close to their data, applications, and operations while bringing Azure-consistent infrastructure and local AI capabilities into those environments.

SQL Server on Azure Local FAQWhat is SQL Server on Azure Local?SQL Server on Azure Local lets organizations run SQL Server on Azure Local infrastructure in their own datacenters and edge locations. It supports SQL Server workloads on virtual machines running Windows Server or Linux.

What is the difference between connected and disconnected deployments?Connected deployments use Azure connectivity and can use Azure Arc to manage SQL Server resources through Azure. Disconnected operations are designed for environments where external connectivity is restricted, intermittent, or unavailable, with operations continuing locally.

Who should consider SQL Server on Azure Local?It is designed for organizations that need local control because of sovereignty, regulatory, latency, operational continuity, or edge requirements, including public sector, defense, financial services, healthcare, manufacturing, energy, and other regulated or connectivity-constrained environments.

Can customers use existing SQL Server licenses?Eligible existing SQL Server licenses can be used according to applicable licensing terms. For connected deployments, customers can also use pay-as-you-go licensing through Azure Arc. Fully disconnected deployments use eligible existing licenses through Azure Hybrid Benefit.

Can organizations run AI close to their SQL Server data?Foundry Local on Azure Local brings AI inference to Azure Local environments and is designed to keep data processing on-premises. As of September 29, 2026, Foundry Local on Azure Local is currently available in preview, so availability and capabilities may change before general availability.

Where can I find deployment guidance?Microsoft Learn provides guidance for deploying SQL Server on Azure Local, including hardware selection, SQL Server installation, monitoring, performance tuning, high availability, and related Azure hybrid services.

Get Started with SQL Server on Azure LocalSQL Server on Azure Local is generally available for connected and disconnected deployment scenarios.

SQL Server on Azure Local – OverviewDeploy SQL Server on Azure LocalDeploy SQL Server on Azure Local Disconnected ModeLearn more about Azure Arc-enabled SQL ServerAdditional resources

Manage SQL Server licensing and billing with Azure ArcFoundry Local on Azure Local overviewFoundry Local deployment overview
The post SQL Server on Azure Local is now generally available appeared first on Microsoft Azure Blog.
Quelle: Azure

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AWS Parallel Computing Service now supports scaling logs

AWS Parallel Computing Service (AWS PCS) now supports scaling logs, which record how AWS PCS scales the compute node groups in your cluster. Each log entry records one state transition for one compute node—for example, an instance launch, a node registration, a scale-down, or a launch failure with its reason. Using scaling logs, you can more easily troubleshoot scaling issues. For example, you can determine why a compute node group did not reach its target size, which launches failed for capacity reasons, and when a specific node started or stopped. Scaling log delivery is opt-in, and you can configure AWS PCS to emit scaling logs to Amazon CloudWatch Logs, Amazon S3, and Amazon Data Firehose.
AWS PCS is a managed service that simplifies running and scaling HPC workloads on AWS using Slurm. You can build complete, elastic environments that integrate compute, storage, networking, and visualization tools, while the service handles cluster operations with managed updates and built-in observability features.
This feature is available in all AWS Regions where AWS PCS is available. To get started, see the AWS PCS User Guide.
Quelle: aws.amazon.com

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Amazon Managed Grafana now supports creating Grafana 13.2 workspaces

Amazon Managed Grafana now supports creating new workspaces with Grafana version 13.2. This release brings features from open-source Grafana versions 13.0 to 13.2, including Git Sync for dashboards, dynamic dashboards, and PromQL query support in the Amazon CloudWatch data source plugin.
With Git Sync, you can treat dashboards as code by linking a workspace to a Git repository, enabling version control for tracking, reviewing, and reverting dashboard modifications. Dynamic dashboards provide a more adaptable approach to layout and paneling, constructing responsive views driven by conditions that respond to data and variables. The Amazon CloudWatch data source plugin now supports PromQL queries, allowing you to use Prometheus Query Language to query metrics that CloudWatch ingests via its OpenTelemetry (OTLP) endpoint, complementing the existing Metric Search and Metrics Insights query types.
Grafana 13.2 is available in all AWS regions where Amazon Managed Grafana is generally available.
To learn more about creating Amazon Managed Grafana workspaces with version 13.2, visit the user documentation. For more information about Amazon Managed Grafana features and pricing, visit the product page and pricing page.
Quelle: aws.amazon.com

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Amazon S3 Vectors introduces metadata pre-filtering for up to 5x higher recall on filtered search

Amazon S3 Vectors now supports pre-filtering, which evaluates metadata filters before running similarity search, returning up to 5x more of the matching vectors when your filter is selective. S3 Vectors also adds a prefix match operator ($startsWith) for filtering on values like paths and URLs. Together, these improvements give your retrieval-augmented generation (RAG), agentic, and semantic-search applications more complete results when you filter, so your applications return more relevant answers.
S3 Vectors provides native support to store and query vectors in Amazon S3, delivering purpose-built, cost-optimized vector storage and query at billion-vector scale. With this launch, indexes in new vector buckets use metadata pre-filtering by default, with no change to how you write vectors with PutVectors or run filtered queries with QueryVectors. To use pre-filtering on an existing index, update it in place with the UpdateIndexMode API. You can also compare pre-filtering against your current filtering on the same index, using a per-query parameter on QueryVectors, before you update.
Metadata pre-filtering is available at no additional cost in all commercial AWS Regions where Amazon S3 Vectors is available, and in the AWS China Regions. We are in the process of deploying this change and plan to complete the deployment in the coming days. To get started, use the AWS CLI, AWS SDKs, or the Amazon S3 console. To learn more, visit the Amazon S3 Vectors documentation and the AWS News blog.
Quelle: aws.amazon.com

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AWS CLI now supports bulk skill updates and version checks for the Agent Toolkit for AWS

Today, AWS expanded the AWS Command Line Interface (CLI) commands for the Agent Toolkit for AWS with two new capabilities that make it easier to keep agent skills up to date. Customers can now run aws agent-toolkit check-skill-updates to compare all installed skills against the latest versions available in the registry, and use aws agent-toolkit update-skill –all to update every outdated skill in a single command.
The Agent Toolkit for AWS consists of the AWS MCP Server (which provides a secure, auditable agent interface to 15,000+ AWS APIs), agent skills (which give agents expert guidance across storage, networking, analytics, and more), and plugins (which bundle the MCP server and curate sets of skills into a single install). Previously, customers needed to check and update each skill individually. With these additions to AWS CLI, developers can quickly identify which skills have newer versions available and bring their entire skill set current without managing updates one at a time. This is especially useful for teams that have installed many skills across serverless, storage, networking, analytics, and other domains, and want to ensure their coding agents always operate with the latest guidance. These new commands are added to the existing set of AWS CLI capabilities for the Agent Toolkit, which already allows customers to install, search, and configure the AWS MCP Server and agent skills across Kiro, Claude Code, Codex, Cursor, and other popular coding agents. 
To get started, see AWS CLI in the Agent Toolkit for AWS user guide. Make sure you have AWS CLI version 2.37.0 or later installed. The AWS MCP Server is available in the US East (N. Virginia) and Europe (Frankfurt) Regions.
Quelle: aws.amazon.com

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Amazon WorkSpaces Core Managed Instances adds support for NVIDIA Blackwell GPU

Amazon WorkSpaces Core Managed Instances now supports Graphics G7 instances, powered by NVIDIA RTX PRO 4500 Blackwell Server Edition GPUs and Intel Xeon 6 processors. G7 instances deliver up to 2.1X better performance for graphics-intensive workloads compared to previous generation G6 instances.
With Graphics G7, customers can run demanding professional applications such as CAD/CAM, 3D rendering, scientific visualization, video editing, and AI-assisted design workflows at higher fidelity and frame rates. G7 instances feature 32 GB of GDDR7 GPU memory per GPU and 2.67X faster memory bandwidth, enabling larger, more complex 3D scenes and models. Six instance sizes are available with 1 to 8 GPUs, vCPUs ranging from 8 to 192, and system memory from 32 GB to 768 GB, with support for Linux and Windows, including bring-your-own-license.
Graphics G7 instances for WorkSpaces Core Managed Instances are available in US East (N. Virginia), US East (Ohio), US West (Oregon), and Europe (Spain). Additional AWS Regions will be added as availability expands.
To get started, specify an instance type from the G7 family when creating a new WorkSpaces Core Managed Instance, or look for G7 instance types when they become available through your WorkSpaces Core partner solution. For more information, see the following resources:
Amazon WorkSpaces Core
Amazon WorkSpaces Core Managed Instances
EC2 G7 Instance Types
Amazon WorkSpaces Core Managed Instances Pricing
Quelle: aws.amazon.com