Archivi categoria: Azure Local

RAG on Azure Local: the evolution of generative AI in hybrid environments

In the era of Artificial Intelligence, companies are required to combine computational power with distributed data management, as data is increasingly located across cloud environments, on-premises infrastructures, and edge settings. In this context, Azure Local emerges as a strategic solution, capable of extending the benefits of cloud computing directly into local data centers—where the most sensitive and critical workloads reside. After exploring this topic in the previous article, “AI from Cloud to Edge: Innovation Enabled by Azure Local and Azure Arc,” this new piece focuses on a particularly significant evolution: the adoption of RAG Capabilities (Retrieval-Augmented Generation) within Azure Local environments. Thanks to Microsoft’s adaptive cloud approach, it is now possible to design, deploy, and scale AI solutions consistently and in a controlled manner, even in hybrid and multicloud scenarios. Azure Local thus becomes the enabler of a tangible transformation, bringing generative AI capabilities closer to the data, with clear benefits: reduced latency, preservation of data sovereignty, and greater accuracy and relevance of the generated results.

A Consistent AI Ecosystem from Cloud to Edge

Microsoft is building a consistent and distributed Artificial Intelligence ecosystem, designed to enable the development, deployment, and management of AI models wherever they are needed: in the cloud, on-premises environments, or at the edge.

This approach is structured into four key layers, each designed to address specific needs:

  • Application Development: With Azure AI Studio, developers can easily design and build intelligent agents and conversational assistants using pre-trained models and customizable modules. The development environment offers integrated tools and a modern interface, simplifying the entire AI application lifecycle.

  • AI Services: Azure offers a wide range of advanced AI services — including language models (based on OpenAI), machine translation, computer vision, and semantic search — which, until now, were limited to the cloud environment. With the introduction of RAG in Azure Local, these capabilities can now also be executed directly in local environments.

  • Machine Learning and MLOps: Azure Machine Learning Studio allows for efficient creation, training, optimization, and management of ML models. Thanks to the AML Arc Extension, all these features are now also available on local and edge infrastructures.

  • AI Infrastructure: Supporting all these layers is a solid and scalable technology foundation. Azure Local, together with Azure’s global infrastructure, provides the ideal environment for running AI workloads through containers and optimized virtual machines, ensuring high performance, security, and compliance.

Microsoft’s goal is clear: to eliminate the boundary between the cloud and the edge, enabling organizations to harness the power of AI where the data actually resides.

What is Retrieval-Augmented Generation (RAG)

Within the unified AI ecosystem Microsoft is building, one of the most impactful innovations is Retrieval-Augmented Generation (RAG) — an advanced technique poised to revolutionize the approach to generative AI in the enterprise space. Unlike traditional models that rely solely on knowledge learned during training, RAG enriches model responses by dynamically retrieving up-to-date and relevant content from external sources such as documents, databases, or vector indexes.

RAG operates in two distinct but synergistic phases:

  • Retrieve: The system searches and selects the most relevant information from external sources, often built using enterprise data.

  • Generate: The retrieved content is used to generate more accurate responses, consistent with the context and aligned with domain-specific knowledge.

This architecture helps reduce hallucinations, increase response accuracy, and work with updated and specific data without retraining the model, thereby ensuring greater flexibility and reliability.

RAG on Azure Local: Generative AI Serving On-Premises Data

With the introduction of RAG Capabilities in Azure Local environments, organizations can now bring the power of generative AI directly to their data—wherever it resides: in the cloud, on-premises, or across multicloud infrastructures—without needing to move or duplicate it. This approach roots artificial intelligence in enterprise data and enables the native integration of advanced capabilities into local operational workflows.

The solution is available as a native Azure Arc extension for Kubernetes, providing a complete infrastructure for data ingestion, vector index creation, and querying based on language models. Everything is managed through a local portal, which offers essential tools for prompt engineering, monitoring, and response evaluation.

The experience is designed in a No-Code/Low-Code fashion, with an intuitive interface that allows even non-specialized teams to develop, deploy, and manage RAG applications.

Key Benefits

  • Data Privacy and Compliance: Sensitive data remains within corporate and jurisdictional boundaries, allowing the model to operate securely and in compliance with regulations.

  • Reduced Latency: Local data processing enables fast responses, which are crucial in real-time scenarios.

  • Bandwidth Efficiency: No massive data transfers to the cloud, resulting in optimized network usage.

  • Scalability and Flexibility: Thanks to Azure Arc, Kubernetes clusters can be deployed, monitored, and managed on local or edge infrastructures with the same operational experience as the cloud.

  • Seamless Integration with Existing Environments: RAG capabilities can be directly connected to document repositories, databases, or internal applications, enabling scenarios such as enterprise chatbots, intelligent search engines, or vertical digital assistants—natively and without invasive infrastructure changes.

This capability represents a fundamental element in Microsoft’s strategy: to make Azure the most open, extensible, and distributed AI platform, capable of enabling innovation wherever data resides and transforming it into a true strategic asset for the digital growth of organizations.

Advanced RAG Capabilities on Azure Local

The RAG capabilities available in Azure Local environments go beyond simply bringing generative AI closer to enterprise data—they represent a comprehensive set of advanced tools designed to deliver high performance, maximum flexibility, and full control, even in the most demanding scenarios. Thanks to continuous evolution, the platform is equipped to support complex and dynamic use cases, while keeping quality, security, and responsibility at the forefront.

Here are the main advanced features available:

  • Hybrid Search and Lazy Graph RAG (coming soon): The combination of hybrid search with the upcoming support for Lazy Graph RAG enables the creation of efficient, fast, and low-cost indexes, providing accurate and contextual responses regardless of the nature or complexity of the query.

  • Performance Evaluation: Native evaluation pipelines allow structured testing and measurement of RAG system effectiveness. Multiple experimentation paths are supported—helpful for comparing different approaches in parallel, optimizing prompts, and improving response quality over time.

  • Multimodality: The platform natively supports text, images, documents, and—soon—videos. By leveraging the best parsers for each format, RAG on Azure Local can process unstructured data located on NFS shares, offering a unified and in-depth view across various content types.

  • Multilingual Support: Over 100 languages are supported during both ingestion and model interactions, making the solution ideal for organizations with a global presence or diverse language requirements.

  • Always-Up-to-Date Language Models: Each update of the Azure Arc extension provides automatic access to the latest models, ensuring optimal performance, enhanced security, and alignment with the latest advancements in generative AI.

  • Responsible and Compliant AI by Design: The platform includes built-in capabilities for managing security, regulatory compliance, and AI ethics. Generated content is monitored and filtered, helping organizations comply with internal policies and external regulations—without placing additional burden on developers.

Key Use Cases of RAG on Azure Local

The integration of RAG into Azure Local environments delivers tangible benefits across several sectors:

  • Financial Services: in the financial sector, RAG can analyze sensitive data that must remain on-premises due to regulatory constraints. It can automate compliance checks on documents and transactions, provide personalized customer support based on financial data, and create targeted business proposals by analyzing individual profiles and preferences.
  • Manufacturing: for manufacturing companies, RAG is a valuable ally for enhancing operational efficiency. It can offer real-time assistance in problem resolution through analysis of local production data, help identify process inefficiencies, and support predictive maintenance by anticipating failures through historical data analysis.
  • Public Sector: public administrations can leverage RAG to gain insights from the confidential data they manage. It’s useful for summarizing large volumes of information to support quick and informed decision-making, creating training materials from existing documentation, and enhancing public safety through predictive analysis of potential threats based on local data.
  • Healthcare: in the healthcare sector, RAG enables secure handling of clinical data, delivering value across multiple areas. It can support the development of personalized treatment plans based on patient data, facilitate medical research through clinical information analysis, and optimize hospital operations by analyzing patient flow and resource usage.
  • Retail: in the retail sector, RAG can enhance customer experiences and streamline business operations. It is effective for creating personalized marketing campaigns based on purchasing habits, optimizing inventory management through sales data analysis, and gaining deeper insights into customer behavior to refine product and service offerings.

Conclusion

The integration of RAG capabilities within Azure Local environments marks a significant milestone in the maturity of distributed Artificial Intelligence solutions. With an open, extensible, and cloud-connected architectural approach, Microsoft enables organizations to leverage the benefits of generative AI consistently—even in hybrid and on-premises scenarios. RAG capabilities, in particular, allow advanced language models to connect with the contextual knowledge stored in enterprise systems—without compromising governance, security, or performance. This evolution makes it possible to create intelligent, secure, and customized applications across any operational context, accelerating the time-to-value of AI across multiple industries. Azure Local with RAG represents a strategic opportunity for businesses that want to govern Artificial Intelligence where data is born, lives, and generates value.

On-Premises GPUs for AI and Virtual Desktop: How Azure Local is Changing the Game

In recent years, the adoption of technologies based on Artificial Intelligence, machine learning, and desktop virtualization has accelerated dramatically. However, behind the innovation visible to end users lies a fundamental requirement: high-performance IT infrastructures capable of efficiently handling complex workloads. While the cloud often appears to be the go-to solution, it is not always the only — or the most suitable — option for every need.

In certain scenarios, organizations are increasingly demanding local solutions that deliver high performance, low latency, and strict data control. This need is often driven by concerns related to security, regulatory compliance, and specific architectural constraints that require workloads to run directly in on-premises environments.

This is where Azure Local comes into play — a Microsoft offering that redefines the concept of hybrid infrastructure. Combining the power of Azure cloud with the flexibility of local deployment, Azure Local enables full utilization of GPUs directly within your datacenter, empowering AI and Virtual Desktop Infrastructure (VDI) scenarios with high performance and full operational control.

Why Use On-Premises GPUs?

Let’s start with a key point: enterprise-grade GPUs — those built for datacenters and heavy workloads—have become essential for handling complex tasks such as:

  • Training and inference of AI models
  • Real-time video and visual processing
  • Virtualization of graphic-intensive desktops and compute-heavy environments

But what happens when these workloads need to run in environments where:

  • Internet connectivity is absent, unstable, or unsuitable for continuous traffic
  • The data being processed is too sensitive or regulated to be moved to the public cloud
  • Applications require immediate response times without delays from round trips to Azure

In these cases, having on-premises GPUs fully integrated into your local infrastructure is not just a strategic choice — it’s often a necessity. This is where Azure Local steps in, enabling organizations to harness the power of enterprise GPUs right in their datacenter, with the simplicity and scalability of the Azure experience.

What is Azure Local?

Azure Local is essentially the extension of Microsoft’s cloud services directly into your own datacenter. It delivers a selection of Azure services, the same APIs, and the same management model—but with the option to run everything locally, wherever it’s needed: on-premises or at the edge.

With Azure Local, you can deploy applications, virtual desktops, and AI models within your own infrastructure while retaining complete data control — benefiting from the flexibility, scalability, and operational consistency of the cloud. No need to move sensitive data. No compromise on the Azure experience. Just the resources you need, right where you need them.

Azure Local + GPUs: A Powerful Combination

One of Azure Local’s most compelling features is its native GPU support, allowing you to tackle AI workloads and VDI environments with high performance and operational efficiency. You can choose between two usage modes:

  1. DDA – Discrete Device Assignment: The GPU is exclusively assigned to a single virtual machine. This is the most powerful mode, ideal for scenarios requiring maximum compute power, such as AI model training, deep learning, or advanced rendering.
  2. GPU-P – GPU Partitioning: In this mode, the GPU is divided into multiple virtual partitions, each assignable to a VM. Perfect for maximizing efficiency and supporting multi-user environments like VDI.

Both modes are fully compatible with NVIDIA drivers and support major compute and graphics libraries, including CUDA, OpenGL, and DirectX.

Which GPUs Are Supported?

Supported models currently include:

  • NVIDIA A2 and A16 – Supported in both DDA and GPU-P modes
  • NVIDIA A10, A40, L4, L40, L40S – Supported in GPU-P mode

All are centrally manageable through Azure Arc, ensuring full control and visibility — even in the most distributed environments.

What About Virtual Desktops?

This is where things get even more interesting.

With Azure Virtual Desktop on Azure Local, you can deliver modern, high-performance, and secure desktop experiences directly within your on-premises environments. This means bringing the benefits of cloud-native VDI to where it truly matters, with session hosts physically close to end users.

The result? A significantly improved user experience, thanks to:

  • Ultra-low latency, ideal for on-site users or limited connectivity environments
  • Optimized performance for graphics applications and compute-intensive workloads
  • Data that remains on-premises, ensuring security and compliance
  • Full compatibility with Windows 11 and 10 in multi-session mode
  • Native integration with traditional Active Directory and Microsoft Entra ID

All orchestrated via the Azure portal, with the same provisioning, monitoring, and management tools—simplifying administration and ensuring operational consistency between cloud and datacenter.

AI Where It Truly Matters

When it comes to Artificial Intelligence, Azure Local is a game changer. You can now train, deploy, and manage AI models directly on-premises or at the edge, without relying on the cloud for every step of the process.

How? With two key technologies:

  • Edge RAG (Retrieval-Augmented Generation): Enhances generative models by integrating your local data—without ever moving it out of your environment. An ideal solution for highly secure and confidential use cases such as healthcare, government, or regulated industries.
  • Azure Machine Learning with Azure Arc: A unified platform for managing the entire lifecycle of AI models — from training to deployment — whether in the cloud or on-premises, using the same tools, APIs, and capabilities.

The result? A hybrid, secure, scalable, and fully localized AI ecosystem, designed to bring intelligence right where it’s needed: close to your data, your users, and your business-critical processes.

But Is It Complicated to Set Up?

Absolutely not. One of Microsoft’s main goals has been to simplify the configuration and management experience of Azure Local — even in GPU scenarios.

To get started, you simply need:

  • GPU-compatible physical hosts (over 100 validated models available)
  • VMs configured according to recommended technical requirements for DDA or GPU-P
  • Connection to Azure Arc for centralized, consistent, and secure management

Once the environment is up and running, you can operate just as you would in the Azure cloud — but with your own data, your own network, and full infrastructure control. No added complexity—just greater operational flexibility.

Conclusion

In a constantly evolving tech landscape, where performance, security, and compliance demands are increasingly strict, Azure Local stands out as a true game changer. The ability to bring enterprise GPUs directly into local datacenters—while preserving the experience, scalability, and consistency of Azure cloud — empowers organizations to effectively tackle AI, VDI, and high-performance workloads.

Whether it’s about achieving ultra-low latency, protecting sensitive data, or operating in limited-connectivity environments, Azure Local offers a modern and tangible solution, with a flexible, manageable, and most importantly, accessible hybrid approach.

This is not simply about “bringing the cloud on-premises.” It’s about redefining how IT infrastructure supports core business processes, enabling advanced scenarios without compromise in control, performance, or security.

Ultimately, Azure Local is an excellent choice for those looking to bring innovation exactly where it’s needed most: close to the data, the users, and the everyday operational needs.

AI from Cloud to Edge: Innovation Powered by Azure Local and Azure Arc

In the era of Artificial Intelligence, which is significantly transforming business models, the adoption of local and distributed infrastructures is crucial for managing specific and mission-critical workloads. In this context, Azure Local emerges as an innovative solution capable of bridging the gap between cloud and edge computing, delivering applications, data, and AI services exactly where they are needed. This article will explore real-world scenarios where Azure Local, combined with Azure Arc, enables real-time data processing “at the source” and the deployment of advanced AI solutions. We will also delve into the new Azure AI services designed for Azure Local, focusing on maximizing the potential of on-premises data.

Real-World Scenarios of Local and Distributed Infrastructure with Azure Local

In the following sections, we will examine concrete examples that demonstrate how Azure Local, in synergy with Azure Arc, effectively addresses the needs of distributed infrastructure, ensuring low latency, security, and operational continuity across various business and industrial contexts.

Figure 1 – Real-World Scenarios for Local and Distributed Infrastructure with Azure Local

Local AI Inferencing

In many situations, analyzing data in real-time directly at the edge (e.g., within a retail store or an industrial facility) provides significant advantages in terms of latency and reduced bandwidth usage. Azure Local enables on-site data processing, eliminating the need to transfer all data to the cloud before performing critical analyses. Here are some examples:

  • Retail Loss Prevention: With AI integrated locally, suspicious behaviors and potential thefts can be identified in real-time, allowing retailers to act immediately and reduce losses.
  • Smart Self-Checkout: Video surveillance and visual analysis facilitate automatic item recognition, improving customer experience and reducing wait times.
  • Pipeline Monitoring: In sectors like oil & gas, real-time video monitoring of infrastructure helps detect anomalies and leaks, reducing environmental risks and ensuring timely interventions.

Operational Continuity in Mission-Critical Environments

The ability to ensure business continuity during network or power outages is a crucial aspect. With Azure Local, robust systems can be implemented to preserve operations even when cloud connectivity is limited or unavailable. Examples include:

  • Factory and Warehouse Operations: Production and inventory management cannot stop; having a local solution ensures that production lines and management systems continue functioning despite network disruptions.
  • Stadiums and Event Venues: Services like security, ticketing, and lighting must remain operational to safeguard both spectator experience and safety.
  • Transport Hubs: Constant operation of ticketing systems, scheduling, and communications is essential for passenger flow and safety in large transit hubs.

Control Systems and Near Real-Time Processing

Some industrial, financial, and healthcare environments demand extremely low response times to avoid errors, ensure safety, or maximize performance. Azure Local, combined with Azure Arc, can meet these latency requirements:

  • Manufacturing Execution Systems (MES): Continuous synchronization and monitoring of production machinery optimize processes and minimize downtime.
  • Industrial Quality Assurance (QA): Immediate quality checks and verifications identify defects before they reach the final stage of production, increasing compliance and reducing waste.
  • Financial Infrastructures: Low-latency transaction processing and rapid risk assessment are critical for market competitiveness and stability.

Regulatory Compliance and DDIL Connectivity (Disconnected, Degraded, Intermittent, Limited)

For many organizations (governmental, military, or those operating critical infrastructures), data protection and secure management, even in the absence of reliable connectivity, are top priorities. Azure Local supports the need for on-premises data and control:

  • Government and Military Sectors: Data confidentiality is paramount, requiring local management to ensure continuous access even in compromised network scenarios.
  • Energy Infrastructures: The stability of distribution networks and control of pipelines and refineries require resilience under limited connectivity conditions, while adhering to stringent regulations.

Azure’s Adaptive Cloud Approach

Microsoft’s adaptive cloud approach, enabled by Azure Arc, helps organizations unify hybrid, multicloud, and edge infrastructures within Azure. With Azure Arc, the same cloud-native experiences and capabilities—such as security, updates, management, and scalability—can be extended anywhere, from on-premises data centers to distributed locations.

Figure 2 – Adaptive Cloud Approach

Azure Local, connected to the cloud through Azure Arc, enables:

  • Operating and scaling distributed infrastructure via the Azure portal and the same APIs.
  • Running fundamental compute, network, storage, and application services locally, choosing hardware from the preferred vendor.
  • Strengthening the security of apps and data with Azure technologies, protecting them against advanced threats.

A key feature is the presence of Azure Kubernetes Service (AKS), Microsoft’s managed Kubernetes solution. On Azure Local, AKS can be configured and updated automatically, providing everything needed (storage drivers, container images for Linux and Windows, etc.) to support containerized applications. Moreover, each cluster is automatically enabled with Azure Arc, allowing integration with services like Microsoft Defender for Containers, Azure Monitor, and GitOps for continuous delivery.

Figure 3 – Bring Azure Apps, Data, and AI Anywhere

New Azure AI Services with Azure Local and Azure Arc

On-Premises Data Search with Generative AI

In recent years, generative AI has made significant strides, driven by the introduction of language models (like GPT) capable of interpreting and generating natural language text. Public tools like ChatGPT work well for general knowledge queries but cannot address questions about private business data on which they have not been trained. To bridge this gap, the concept of Retrieval Augmented Generation (RAG) was introduced, a technique that “enhances” language models with proprietary data, enabling more advanced and customized use cases.

Within the Azure Local framework, Microsoft has announced a new service that brings generative AI and RAG directly to the edge, where the data resides. Within minutes, organizations can deploy (via an Azure Arc extension) everything needed to query their on-premises data, including:

  • Small and large language models (SLM/LLM) running locally, with support for both CPUs and GPUs.
  • An end-to-end data ingestion and RAG pipeline that keeps all information on-premises, with RBAC (Role-Based Access Control) ensuring secure access.
  • An integrated tool for prompt engineering and result evaluation to optimize model settings and performance.
  • APIs and interfaces aligned with Azure standards, facilitating integration into enterprise applications, plus a preconfigured UI for immediate service use.

This feature is now available in private preview for Azure Local customers, with Microsoft planning to expand availability to other Arc-enabled platforms in the near future.

“Edge RAG”: The Local Retrieval-Augmented Generation Ecosystem

This new service, known as “Edge RAG”, integrates seamlessly into the Azure ecosystem and supports various input components, such as:

  • Azure AI Search: Provides document search and indexing functionality, enabling quick identification of relevant content within large datasets.
  • Azure OpenAI: Offers advanced AI models (like GPT) capable of generating, understanding, and summarizing text in natural language.
  • Azure AI Studio: A platform for developing and managing AI assets (datasets, models, pipelines) centrally.

Together, these components power an integrated flow—from data ingestion to inference and result presentation via chat or other development interfaces. This enables the creation of chatbots, knowledge discovery tools, and other AI-driven solutions that leverage internal business data in a secure, customizable, and compliant environment.

Deploying Open-Source AI Models via Azure Arc

Another key feature of Azure AI is the availability of a catalog of AI models tested, validated, and guaranteed by Microsoft. These models are ready for deployment and provide consistent inference endpoints. This functionality is now extended to the edge, where Microsoft makes selected models available directly from the Azure portal:

  • Phi-3.5 Mini (language model with 3.8 billion parameters)
  • Mistral 7B (language model with 7.3 billion parameters)
  • MMDetection YOLO (object detection)
  • OpenAI Whisper Large (speech-to-text recognition)
  • Google T5 Base (automatic translation)

These models can be deployed in just a few steps on an Arc AKS cluster running on-premises. Most models require only a CPU, but Phi-3.5 and Mistral 7B also support GPUs for enhanced performance in intensive inference scenarios.

Azure AI Offerings: From Cloud to Edge

Microsoft’s approach spans the full spectrum of AI capabilities, offering services and tools that can be delivered in the Azure cloud or extended to on-premises and edge environments via Azure Arc. The offering consists of four main pillars:

  • Application Development
    • Azure AI Studio: A development environment for AI applications (e.g., chatbots, virtual agents) with a complete set of APIs and interfaces for seamless AI integration.
  • AI Services
    • Azure AI Language and Model Services: Preconfigured services for NLP, computer vision, and other AI functionalities.
    • Solutions like Edge RAG, Video Indexer, and Managed AI Containers for local deployment of “ready-to-use” AI models.
  • Machine Learning & ML Ops
    • Azure Machine Learning Studio: A comprehensive platform for creating, training, optimizing, and managing machine learning models.
    • With Azure Arc, ML Ops capabilities can extend to the edge via extensions like the AML Arc Extension, enabling Azure ML tools on on-premises and edge infrastructures.
  • Infrastructure
    • Azure Global Infrastructure: Azure’s cloud foundation, including compute, storage, and networking resources.
    • Arc-Enabled Edge Infrastructure: Extends Azure capabilities to data centers or edge devices, managed as if they were cloud resources.

Conclusion

Microsoft’s strategy is built on delivering the best of the cloud “anywhere.” Azure Local epitomizes this vision: a solution that brings all the benefits of the cloud—agility, scalability, security—directly to local environments, meeting the needs for low latency, operational continuity, and regulatory compliance.

Thanks to Azure Arc, organizations can leverage Azure AI services such as advanced language models, Retrieval-Augmented Generation (RAG) pipelines, and ML Ops tools in a hybrid mode. Applications range from factory quality control to retail theft prevention, from critical government data centers to energy infrastructure monitoring.

In a world where data continues to grow exponentially and the need for on-site analysis becomes increasingly urgent, solutions like Azure Local represent the next step toward a new generation of distributed infrastructures. This is how Microsoft meets the challenge of uniting cloud potential with on-premises reality, creating opportunities for innovation and growth across all sectors.

The Evolution of High Availability and Disaster Recovery in Modern Infrastructures: The Azure Local Case

High availability and disaster recovery solutions are playing an increasingly central role in modern infrastructure adoption strategies. Azure Local, Microsoft’s on-premises cloud-connected platform, exemplifies this transformation.

Starting with version 23H2, Azure Local introduces a new generation of features, moving away from the traditional Stretched Cluster model to propose more modern and flexible approaches designed to optimize resilience and simplify management. Through new configurations such as Rack Aware Cluster and disaster recovery support via Azure Site Recovery, Azure Local positions itself as a strategic platform for organizations seeking robust, scalable solutions aligned with the Azure ecosystem. In this article, we will explore the key features introduced in Azure Local version 23H2, analyzing the new high-availability options, disaster recovery strategies, and the impact of transitioning from Stretched Clusters to a more advanced model.

Azure Local, Version 23H2: An Arc-Enabled Evolution

The new version 23H2 marks a significant leap forward, transforming from a simple cloud-connected operating system to an Azure Arc-enabled solution with integrated features such as Arc Resource Bridge, Arc VM, and AKS. This transformation expands the possibilities for managing and controlling distributed environments, providing a unified administrative experience. Moreover, multi-site management extends beyond the operating system level, rendering the functionality of previous Stretched Clusters obsolete and introducing new paradigms of resilience and reliability.

High Availability Options

Rack Aware Cluster: High Availability for Short Distances

The standout feature for short-distance scenarios is the Rack Aware Cluster, a configuration that enables:

  • Deploying the cluster across two racks or rooms within the same Layer-2 network (e.g., within a manufacturing plant or campus).
  • Functioning as a local availability zone, ensuring fault isolation and optimal workload placement.

Figure 1 – Rack Aware Cluster: Network Architecture

This configuration offers an ideal solution for combining efficiency and ease of management in local environments. By leveraging a single storage pool, it reduces complexity and enhances overall efficiency, avoiding the overhead caused by excessive data replication. The Rack Aware Cluster is particularly suited for edge locations and can scale up to 8 nodes (4 per rack). Currently in private preview, public availability is expected by 2025.

Notably, even within Azure Local, the concept of availability zones has been introduced, aligning significantly with the established Azure model to ensure maximum reliability and operational continuity.

Disaster Recovery Options

Cloud Replication with Azure Site Recovery

For long-distance disaster recovery scenarios, Azure Local leverages Azure Site Recovery (ASR) to replicate on-premises virtual machines to the Azure cloud. This solution enables:

  • Replication: Transferring VM disks to an Azure storage account, safeguarding data from potential disasters.
  • Failover: Running replicated VMs directly in Azure during a disaster, ensuring operational continuity.
  • Re-protect: Replicating VMs back to the local cluster, maintaining a continuous protection cycle.
  • Failback: Bringing workloads back from the cloud to the on-premises system with minimal disruption.

These operations are managed centrally through the Azure portal, ensuring simplicity and efficiency for system administrators.

Local Replication with Hyper-V Replica

For workloads that cannot be moved to the cloud, Azure Local supports Hyper-V Replica, a solution that replicates Arc VMs to a secondary site. This approach allows:

  • Ensuring operational continuity by replicating data to a remote location.
  • Managing VM recovery as Hyper-V virtual machines at the secondary site and reverting to Arc VMs upon restoration on the primary cluster.

This feature, integrated into the Hyper-V role, represents an essential option for resilience in multi-site scenarios.

The Transition from Stretched Clusters

Introduced with Azure Local version 22H2, Stretched Clusters utilized Storage Replica to ensure resilience between two node groups located in distinct sites. This configuration:

  • Required at least two nodes per site and replicated storage synchronously to ensure data integrity in the event of failures.
  • Supported live migration of VMs between sites, facilitating smooth transitions for planned maintenance.

However, this solution required manual operations to reverse the direction of storage replication, a process that could introduce complexity and impact performance. With version 23H2, Stretched Clusters are no longer supported. Clusters configured with version 22H2 can still be partially upgraded to the 23H2 operating system, maintaining compatibility but without benefiting from the new features of the latest version.

For customers still using this configuration, it is advisable to consider adopting the new high availability and disaster recovery options offered by Azure Local, which guarantee greater efficiency and reliability.

Conclusions

The new features in Azure Local version 23H2 reflect a significant evolution toward more flexible, modern management aligned with the Azure ecosystem. With solutions like Rack Aware Cluster and integration with Azure Site Recovery, organizations can enhance the resilience of their local environments and ensure scalable and integrated disaster recovery options. Furthermore, abandoning the Stretched Cluster model paves the way for more efficient and streamlined configurations, enabling customers to fully leverage the potential offered by Azure technologies.

Ladies and Gentlemen, Welcome Azure Local!

Microsoft Ignite 2024 brought several exciting announcements, but one of the most significant was undoubtedly Azure Local. This is not merely a rebranding of Azure Stack HCI; it is a platform that redefines how we think about hybrid and on-premises infrastructures. Azure Local is designed to bring the essence of the cloud directly to local datacenters, offering a rich experience highly integrated with Azure services. With a suite of innovative features and a flexible approach, Azure Local promises to redefine the future of local infrastructures. Below, we explore all the updates on this solution.

A Name that Reflects a Vision

The name Azure Local is straightforward and on point. It represents the idea of having core Azure services—compute, networking, storage, and applications—available directly in local datacenters. This vision materializes through a cloud-connected platform that offers flexibility, scalability, and operational control.

Hardware: Choice, Flexibility, and New Opportunities

One of the most intriguing features of Azure Local is its wide range of supported hardware. With over 100 validated platforms, including major vendors like Dell and Lenovo, businesses can select solutions that best meet their needs and budget. Compatibility with GPUs like Nvidia A2, A16, and L40 makes Azure Local ideal for advanced workloads like artificial intelligence and virtual desktops.

Cost-Effective Options for the Edge

For environments with lighter compute requirements or tighter budgets, Azure Local supports micro, tower, and rugged hardware. This is a great opportunity for companies operating in edge or industrial environments. The minimum requirements include a compatible machine with an additional SSD and a 1 Gbps Ethernet network, eliminating the need for expensive switches. These options open new possibilities for deployments in remote or hard-to-reach locations, ensuring performance and consistency even in challenging operating conditions.

Simplified Provisioning

Thanks to the FIDO Device Onboard (FDO) protocol, onboarding machines is automated, greatly simplifying the activation of new edge nodes or IoT devices. This approach eliminates the need for complex manual interventions, making infrastructure deployment faster and more efficient.

Identity Management: With or Without Active Directory

Azure Local introduces long-awaited flexibility in identity management. If you don’t want to use on-premises Active Directory, the new “Local Identity” feature is available. This solution uses local accounts and certificates while retaining advanced functionalities like live VM migration. Additionally, local secrets are safeguarded with Azure Key Vault, ensuring high security levels even without external identity systems.

Centralized Management and Monitoring

One of Azure Local’s key strengths is its integration with Azure Arc, which extends Azure services to on-premises and other cloud environments. Infrastructure management happens directly from the Azure portal, where you can configure clusters, networking, and storage. For those seeking operational consistency, Azure Local allows configurations to be defined using ARM (Azure Resource Manager) templates, ensuring scalable and repeatable management. Furthermore, the Infrastructure-as-Code approach simplifies deployment in distributed environments, ensuring consistency and reducing errors.

Simplified Updates

Azure Local software updates come in a single monthly package, including drivers, firmware, and software stacks. This method enables sequential updates of physical machines while ensuring workload continuity. The ability to automatically orchestrate updates in multi-node environments is a significant advantage for organizations needing to minimize downtime.

Integrated Monitoring

Azure Local integrates natively with Azure Monitor, providing a unified view of all distributed resources. With over 50 standard metrics, preconfigured dashboards, and alert rules, businesses can monitor CPU, memory, storage, and network usage, setting up email notifications or automated actions in case of failures. Furthermore, data collection rules can be customized, and advanced dashboards can be created via Workbooks.

Figure 2 – Centralized visibility across all your locations

New Features and Services

Azure Local doesn’t stop at enhancing infrastructure—it also introduces new features and services that expand its usability.

Figure 3 – Azure Apps, Data, and AI in Azure Local

Migration from VMware

For organizations looking to move away from VMware, Azure Local offers a migration solution (in preview) via Azure Migrate. This tool enables the transfer of VMDKs to Azure Local, eliminating dependence on Broadcom and its associated costs. The migration process uses the same portal and APIs as Azure, ensuring a seamless experience for those already familiar with Azure tools.

Figure 4 – Migrating from VMware to Azure Local

PaaS and AI Services

Azure Local enables the use of Azure PaaS services like Azure Virtual Desktop and SQL Managed Instance. Additionally, the new Azure IoT Operations service provides a unified platform for edge data collection and analysis. For companies interested in AI, Azure Local introduces local AI search capabilities (preview) that leverage advanced language models to analyze on-premises data. This innovation opens new opportunities for process automation and data valorization.

Figure 5 – Azure AI Services with Azure Local

Disconnected Operations

For customers who cannot connect to the cloud due to regulatory or other reasons, Azure Local offers a disconnected option (in preview). In this configuration, Azure services, including the portal and Azure Resource Manager, are hosted locally, ensuring a consistent experience even without connectivity.

Figure 6 – Disconnected operations

Advanced Security

Security is a cornerstone of Azure Local, with new features enhancing resource protection.

Network Security Groups (NSG)

This functionality allows granular access rules between resources, filtering traffic based on parameters like source IP, port, and protocol. NSGs offer precise control over network traffic, reducing the risk of unauthorized access.

Figure 7 – Network Security Group in Azure Local

Trusted Launch

Azure Local introduces Trusted Launch, which protects VMs from rootkits and bootkits through Secure Boot and BitLocker encryption. This feature also ensures secure VM migration within the cluster, preserving data integrity and enhancing infrastructure resilience. Azure’s attestation services will also provide continuous system integrity monitoring, offering advanced security and visibility.

Getting Started

Existing Customers

Existing Azure Stack HCI customers need to do nothing—software updates will ensure a smooth transition to Azure Local, granting immediate access to new features.

New Installations

Azure Local is available in version 2411 for new deployments.

Virtual Sandbox

For those wanting to try Azure Local without dedicated hardware, Azure Arc Jumpstart offers a virtual sandbox environment, accessible via an Azure subscription. This option is ideal for testing features before deploying in production environments.

Conclusion

Microsoft Ignite 2024 highlighted a significant milestone in the hybrid infrastructure landscape with Azure Local. It’s not just an evolution of Azure Stack HCI but a platform that redefines how businesses leverage the cloud in their datacenters. With a focus on flexibility, integration, and security, Azure Local combines the best of the on-premises and cloud worlds, enabling organizations to adopt a truly connected and coherent hybrid strategy.

Its distinctive features, such as simplified provisioning, centralized management with Azure Arc, and support for disconnected scenarios, make it an ideal solution for addressing complex business needs.

Moreover, its attention to specific workloads like AI and virtual desktops, along with advanced security features like Trusted Launch and NSGs, strengthens Azure Local’s ability to adapt to diverse operational contexts.

Azure Local represents a significant step toward the future of hybrid infrastructures, delivering a seamless cloud experience directly to local datacenters. For both existing and new customers, this solution marks the beginning of a new era in IT resource management, bringing the cloud closer to business needs.