This blog post is the third part of a blog series called Azure IaaS which will share best practices and guidance to help you build a trusted infrastructure platform—from performance, resiliency, and security to scalability and cost efficiency.

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As organizations modernize infrastructure, migrate mission-critical workloads, build cloud-native applications, and scale AI—cost efficiency remains a foundational principle of cloud architectures.

Yet cloud costs are rarely driven by a single decision. More often, across Azure Infrastructure-as-a-Service (IaaS) environments, they are the result of many compounded architectural choices across compute, storage, and networking.

Common examples include overprovisioning infrastructure when selecting a larger virtual machine than a workload requires or keeping infrequently accessed data on premium storage, building resilient architectures that introduce unnecessary overhead, or collecting and retaining more operational data than is needed. Individually, these decisions may seem minor, but over time they can significantly impact both cost and operational efficiency.

These challenges become even more important as organizations expand AI initiatives, modernize applications, and support growing performance and resiliency requirements.

The opportunity lies in addressing these inefficiencies before they become entrenched. By making informed infrastructure decisions during planning, deployment, and ongoing operations, organizations can improve resource utilization, reduce total cost of ownership (TCO), and create a more scalable foundation for future growth.

In this blog, we’ll explore some of the most common infrastructure cost challenges organizations face today and examine how Azure IaaS capabilities across compute, storage, and networking can help improve efficiency, reduce TCO, and highlight resources available in the Azure IaaS Resource Center to help you make more informed decisions.

Many of the most impactful optimization opportunities originate long before a workload enters production. To better understand where these opportunities exist, let’s examine common efficiency challenges (and solutions) across compute, storage, and networking.

Compute: Matching resources to workload requirements

Compute inefficiencies are often the easiest to identify because they directly affect both performance and infrastructure spend.

The goal is not simply to select the lowest-cost compute option, but rather to align infrastructure resources with workload requirements while preserving flexibility for future growth.

Azure provides a broad portfolio of virtual machine options, enabling organizations to select the architecture, processor type, performance profile, and scale characteristics that best match workload needs; allowing organizations to align infrastructure investments with workload needs rather than paying for unused capacity.

Equally important is taking advantage of Azure’s flexible pricing options. Depending on workload characteristics, organizations can combine Pay-As-You-Go pricing, Azure savings plans, Azure Reservations, and Azure Spot Virtual Machines to better align costs with actual usage patterns.

For highly scalable environments, services such as Azure Virtual Machine Scale Sets automatically balance compute demand with available capacity by scaling resources up or down in real time, ensuring the environment is right-sized while optimizing cost efficiency. Azure Compute Fleet help organizations intelligently balance capacity, availability, and price-performance across large deployments; reducing the operational complexity associated with managing infrastructure at scale.

The result is a compute environment that is not only cost-efficient, but also better aligned to application requirements and business outcomes.

Storage: Balancing performance and lifecycle management

Storage inefficiencies often develop gradually, at times making them difficult to identify until environments reach significant scale. The key is to ensure that performance, capacity, and data access requirements remain aligned.

Choose the right storage service for the workload

Storage performance requirements vary dramatically across workloads. Some applications demand consistent low-latency block storage, while others prioritize storage capacity, durability, or long-term retention. Selecting the appropriate storage service and performance tier is critical to maximizing both efficiency and value.

For example:

• Business applications may benefit from Premium SSD v2 offerings.
• Business-critical transactional databases may require Ultra Disk to meet stringent low-latency performance requirements.
• Large-scale block storage environments can benefit from consolidated architectures using Azure Elastic Storage Area Network (SAN).
• Linux/Windows file shares, home directories, and shared storage scenarios may benefit from Azure Files or Azure NetApp Files.
• Object storage workloads often benefit from the alignment between Azure Blob Storage tiers and data access patterns.

Automate data lifecycle management

Equally important is ensuring data remains on the appropriate storage tier throughout its lifecycle. In many environments, data access patterns change significantly over time, yet storage configurations remain static. This disconnect often results in organizations paying for performance they no longer need.

Azure Blob Storage provides capabilities that help organizations automatically align storage costs with data access patterns. Automated tiering and lifecycle policies maintain low-latency access for frequently used data while optimizing costs by transitioning infrequently accessed data to lower-cost tiers.

The result is a storage strategy that continuously adapts as usage patterns evolve, without requiring ongoing manual intervention.

Improve visibility across your storage estate

Optimization starts with understanding where costs are being generated.

Tools such as Azure Storage Discovery and Azure Storage Actions can help organizations gain visibility into their storage environments, uncover optimization opportunities, and automate actions across large-scale deployments.

Rather than managing storage account by account, teams can identify patterns and implement cost-saving actions consistently across their entire data estate.

Together, these capabilities help organizations move beyond storage provisioning and toward ongoing storage optimization.

Networking: Improving efficiency without compromising resiliency

Networking presents a unique optimization challenge because organizations must balance connectivity, performance, resiliency, and operational visibility.

Achieve resiliency more efficiently

Historically, improving resiliency often requires duplicating infrastructure components, creating additional cost and management overhead. Today, organizations increasingly seek architectures that deliver resiliency while minimizing complexity and excess infrastructure.

Azure networking capabilities help organizations evaluate these tradeoffs more effectively. Services such as ExpressRoute Metro, Zone Redundant NAT Gateway, and scalable networking architectures provide opportunities to improve resiliency and scalability while maintaining operational efficiency.

Reduce operational and logging expenses

Operational visibility is another important consideration. Network and firewall logs are essential for troubleshooting, security, and governance, but collecting every possible data point can create significant storage and operational costs over time.

Modern filtering and analytics capabilities help teams focus on the most relevant network data, reducing both storage consumption and investigation complexity.

This gives organizations the information they need while avoiding excessive log growth and long-term retention costs.

By implementing filtering, automation, and intelligent logging strategies, organizations can focus on the data that provides actionable insights while reducing unnecessary information collection and retention.

Continuous optimization is where long-term savings happen

Infrastructure efficiency is not achieved through a single migration, architecture review, or pricing decision.

As workloads evolve, usage patterns shift, and new platform capabilities become available, opportunities for optimization continuously emerge.

The organizations that realize the greatest value from cloud investments are often those that treat optimization as an ongoing operational discipline. They regularly evaluate infrastructure utilization, revisit architectural assumptions, automate lifecycle management processes, and adopt new capabilities that improve efficiency across their environments.

While individual improvements may appear incremental, the cumulative impact can be substantial. A right-sized virtual machine (VM), a more appropriate storage tier, an automated lifecycle policy, or a more efficient networking architecture may each deliver modest savings independently. Together, they create a more efficient, scalable, and resilient infrastructure foundation.

Azure continues to deliver important capabilities such as Azure Copilot to help customers optimize cloud costs by combining real-time insights, AI-driven recommendations, and automated optimization actions, empowering teams to quickly identify waste, right-size resources, and forecast spend with minimal effort.

Continue your Azure IaaS optimization journey

Whether you’re supporting AI workloads, modernizing existing applications, migrating existing workloads, or planning future growth, building efficiency into cloud architectures has never been more important.

The Azure IaaS Resource Center provides guidance, best practices, technical resources, and optimization strategies across compute, storage, and networking to help you design, build, and optimize Azure environments with confidence.

Visit the Azure IaaS Resource Center to explore cost optimization guidance, architectural best practices, product resources, and tools that can help you maximize value from your Azure infrastructure investments.

To go deeper, explore the Azure IaaS Resource Center for tutorials, best practices, and guidance across compute, storage, and networking to help you design and operate resilient infrastructure with greater confidence.

Did you miss these posts in the Azure IaaS series?

• Explore new resources for building a stronger, more efficient infrastructure
• Keep critical applications running with built-in resiliency at scale
• Defense in depth built on secure-by-design principles 
• Deploy high-performance workloads with a system-level approach 

The post Azure IaaS: How to design, build, and optimize cloud infrastructure for long-term cost optimization efficiency appeared first on Microsoft Azure Blog.

Claude in Microsoft Foundry is the production path enterprises have been asking for: true frontier model choice, Azure-native controls, simplified procurement, and faster time to value.

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Most enterprise AI projects do not stall because of model quality. They stall because of everything around the model: procurement, governance, networking, and data. Claude in Microsoft Foundry is now generally available, hosted on Azure, giving teams a faster path from agent experimentation to production.

Enterprises can build with Claude through their existing Azure account, using the authentication, billing, networking, governance, and data controls their teams already trust. Instead of solving for infrastructure, teams can focus on building agentic applications that run their work with Claude, in the environment where they already operate.

This is a real step forward for customers building agentic applications and want to move from AI experimentation to production. Claude brings leading capabilities for coding, agentic workflows, and complex reasoning. Microsoft Foundry brings the enterprise harness to build, evaluate, deploy, and scale those agents on Azure. Together, they give teams a trusted path to production AI with frontier model quality and the Azure controls they already trust.

Today’s announcement builds on the strategic partnership Microsoft, NVIDIA, and Anthropic announced in November 2025 to expand enterprise access to Claude on NVIDIA accelerated computing. Claude runs on NVIDIA Blackwell Ultra systems, connected by InfiniBand networking, bringing the rack-scale AI infrastructure designed for inference performance and efficiency.

Build with Claude through your Azure account

Developers can access Claude through the Messages API and use core capabilities including prompt caching, extended thinking, and tool streaming. For teams building agents, Foundry Agent Service uses Claude as the reasoning core to orchestrate multi-step planning, tool use, and task execution across enterprise systems.

Inference is processed in Azure, and customers can choose between Global and US data zones, for teams with data residency requirements. Anthropic operates the inference and is the data processor and SLA provider. Because Claude is available natively through Foundry, teams can work inside the Azure environment they already use. They can authenticate with Microsoft Entra ID, apply Azure role-based access controls, manage access through existing governance policies, and track usage through familiar Azure management experiences.

For high-sensitivity workloads, zero data retention is also available, so prompts and completions are not retained by Anthropic after the API call completes. For commercial teams, it also simplifies how Claude is purchased and consumed. Claude usage is billed in Claude Consumption Units (CCU), a single, consolidated line on your Azure bill, with MACC drawdown and per-model detail in Foundry unchanged.

For many enterprises, that matters as much as model capability. The barrier to production isn’t only whether a model is powerful enough, it’s whether teams can procure it, govern it, secure it, and operate it at scale inside their existing cloud. With Claude in Foundry, they get frontier capabilities in an Azure environment that aligns with enterprise requirements for security, compliance posture, governance, and data residency.

Running Anthropic’s models on Azure has given us the sustained throughput and reliability our enterprise customers expect. The combination of frontier model quality and enterprise-grade infrastructure is what makes Bolt viable for the Fortune 500.

—Gary Ballabio, Vice President, Partnerships, Bolt

Customers are already building with Claude in Foundry

Enterprises aren’t just running isolated pilots; they’re building production systems and agents that need throughput, reliability, governance, security, and scale.

At NVIDIA, we use autonomous AI agents every day to help our teams move faster and think bigger. Anthropic’s Claude models bring strong reasoning, coding and enterprise capabilities that are valuable for complex technical work. With Claude now available in Microsoft Foundry running on NVIDIA GB300 GPUs, more organizations can run advanced, specialized AI agents with the performance, scale and security needed for production.

—Justin Boitano, Vice President and GM of Enterprise Computing, NVIDIA

Our customers describe their tests in plain English, and Momentic runs through the interface to verify everything works before a release ships. We found Claude’s Opus models especially suited to this, and running them on Microsoft Foundry we now serve millions of tokens per minute with the reliability our customers depend on.

—Jeff An, Co-Founder and CEO, Momentic

Built for coding, agents, and complex reasoning

Claude models are especially well-suited to some of the fastest-growing enterprise AI workloads. For software teams, Claude supports code generation, refactoring, debugging, test creation, and large-scale development workflows. For teams building agents, it powers multi-step reasoning, tool use, planning, and task execution. For business teams, it supports document-heavy analysis, research synthesis, and complex decision support.

In Microsoft Foundry, these capabilities connect to the broader Azure ecosystem. With Foundry Agent Service, teams orchestrate multi-step, goal-driven agents that use Claude as their reasoning core, planning, calling tools, and executing tasks across enterprise systems. Features like model router enable customers to automatically route queries to the most appropriate Claude model, saving up to 50% while improving user satisfaction. All this governed and monitored by Foundry Control Plane which continuously runs evaluations to ensure agent responses match customer expectations, even blocking responses that violate rules before they reach users.

Between Anthropic and Azure, we get the best capabilities in the world and we get the best security in the world. And that’s exactly what nuclear needs. It’s how we compressed a safety analysis that would have taken 200 human days into a single day.

—Matt Huang, Founding Product Lead, Everstar

And with Microsoft IQ, agents have access to live enterprise context which radically improves value per token, and helps Foundry amplify the impact customers can have: tools like agent optimizer in Foundry Agent Service tune the prompts which define agents so they perform better regardless of what model is under the hood.

A stronger foundation for enterprise AI on Azure

The next phase of enterprise AI will be defined by production systems: coding agents, business process agents, research assistants, customer-facing applications, and domain-specific workflows that operate reliably at scale. That takes more than access to a model. It takes a platform.

With Claude now generally available in Microsoft Foundry and hosted on Azure, customers can build with Anthropic’s leading models, orchestrating them as agents with Foundry Agent Service and grounding them in enterprise knowledge with Microsoft IQ, while using the Azure controls, commitments, and infrastructure they already trust.

The post Claude in Microsoft Foundry is now generally available appeared first on Microsoft Azure Blog.

A couple of months ago, I sat across from my nine-year-old daughter’s teachers at a parent-teacher conference. They were kind but concerned. She takes her time on assignments, they said—often deep in thought. How would she do on timed tests next year? I told them I wasn’t worried. What they described as a problem is, to me, one of the most important things she can learn: the ability to take a hard problem and reason through it from beginning to end. In a world optimized for efficiency, qualities like patience, perseverance, and attention to detail are not deficiencies. They are the foundation of sound judgment, which will become the skills we need most.

The more time I spend working with AI, the more convinced I become: the question that matters for her future isn’t how quickly she can answer. It’s whether she has the judgment to know when an answer can be trusted.

I’ve spent decades at Microsoft watching this tension play out: first building tools for other developers, then working across AI as models moved from research curiosities to systems deployed at scale. Now we’re building Microsoft IQ, where we’re exploring how an organization’s collective intelligence can become its greatest advantage. Through every one of those chapters, one thing has remained true: it’s never enough for a system to be powerful, it must also be trustworthy.

Trust is what turns assistance into delegation. When we can trust an agent to do what we intend, within the limits we set, we can hand off the work we never wanted to spend our lives on: the repetitive tasks that drain attention, the mundane work that fills a day without moving anything meaningful forward, the dangerous work humans should not have to do, the work too vast for any individual or team. Agents should take on that toil, extend our reach, and give us back our time for the work that calls for something only humans bring.

My daughter doesn’t know any of this yet. But by the time she’s grown, most of the work that rewards speed and repetition will be work we delegate. What will matter then is exactly what gave her teachers pause: the patience to stay with a hard problem, reason through it, and decide when she’s reached a conclusion she can trust. The very thing they feared might hold her back could be exactly what the next era prizes most.

So no, I’m not worried about the timed test. I hope she grows up in a world where software carries the toil and people are freed for the work that is unmistakably ours—to think, to judge, to create, to care for one another. That is the future I want agents to make real.

But my hope is not evidence it will happen. The future I just described turns on a single question: can we trust agents to do the work? Trust is earned one task at a time. So, I went looking for evidence of where it’s been earned, and where it hasn’t.

For the past year, the conversation around AI agents has circled the same promise: eliminate toil so people can focus on what matters. But I keep coming back to sharper questions. What, exactly, is toilsome? Where does toil actually live in people’s work? What are the technical leaders closest to this shift willing to hand off—and what gives them the confidence to do it? To find out, we partnered with MIT Technology Review Insights on new research that draws directly from the people building this frontier. Not the people talking about it, the people doing it. We surveyed 300 technical experts across AI, data, and cloud domains, spanning 12 industries and 4 regions of the world, asking them to rank their confidence across 101 of the top tasks. What we got back is the 2026 Agent Confidence Index, an honest map of where agents are delivering real value, so our community can see what’s working and move forward together with conviction.

Learn from where confidence is highest

Across the 101 tasks measured, average confidence already lands at 64 out of 100, and thirty tasks clear 70. The highest scores cluster on work that is both predictable and draining: the late nights, the interruptions, the low-value repetition. Automated report generation leads at 83.5. Boilerplate code generation for new features sits at 82.5, the hours a developer no longer spends rewriting the same patterns, freed for the work that challenges them. Certificate expiration monitoring and renewal, at 81.5, ends the scramble that pulls engineers off high-stakes problems for something entirely routine. Real-time data stream monitoring follows at 80.5, and release note generation from commit history at 79.5, the manual end-of-sprint commit review, gone. This is where frontier teams are already delegating to agents, regularly.

The pattern holds across every discipline. In developer and AI workflows it extends to API client maintenance and code identification; in cloud operations, to ticket routing and cost optimization; in data, to anomaly detection. Wherever it sits in the stack, this is work technical teams now trust agents to own.

What matters most here isn’t what the data says about the tasks, it’s what it says about the people delegating them. When technical experts believe in something deeply enough to hand it real work, that belief ripples outward. It becomes the recommendation they make to their leadership, the solution they build for their customers, and the culture they create for their teams.

Even the toughest agent tasks are gaining traction

Here’s what strikes me most: the tasks ranked lower on the index are still high in absolute terms. Service mesh configuration and troubleshooting sits at 37.5, database schema migration scripting at 46.5, memory leak detection at 48.5. These sit at the very frontier, the interconnected, high-stakes work where investment and innovation are concentrated right now.

Consider what they demand. Service mesh configuration touches many systems at once. Database migration carries real stakes, requiring precision across data, application, and infrastructure layers at the same time. Memory leak detection means diving deep into a system’s behavior under load, accounting for conditions that shift from one deployment to the next. These are the challenges that have separated great engineers from exceptional ones—and even here, experts see agents helping. Not carrying the work alone, but contributing where it used to be unthinkable. That confidence is still climbing, and that’s telling.

We’re shipping new capabilities constantly to support this momentum. Database migration tooling in GitHub Copilot now covers not just scripts but the full application and infrastructure migration story. The Azure Site Reliability Engineering (SRE) Agent brings decades of experience operating Azure at scale and deep profiling capabilities directly into memory analysis and performance diagnosis.

Why human judgment remains paramount

When we asked technical experts how they’re navigating agent adoption, 59% named “keeping humans in the loop” as their top priority—ahead of better observability, ahead of governance documentation, and ahead of everything else. That’s a mark of maturity. Teams moving forward with clarity treat agent oversight as non-negotiable, regardless of how capabilities evolve.

The boundary itself is straightforward. Agents excel at well-specified, high-volume, reversible work: they synthesize data, automate known workflows, and surface anomalies at a speed and scale no human team could match. The moment a decision becomes high-stakes, context-dependent, or hard to undo, a human signs off. That isn’t a limitation of the technology, it’s the architecture of a trustworthy system.

What’s changing, and what remains underappreciated, is the skill it takes to draw that boundary well: the discipline of full-lifecycle evaluations and guardrails. Success means measuring agent output against intent and keeping behavior inside your business strategy. It’s new territory for most engineering teams, and it’s becoming table stakes for modern software faster than most organizations realize. The good news: the same tools generating the work can help you build the harness. Ask GitHub Copilot to write the evals and it will. Frontier teams are already doing this, and it’s why they’re pulling ahead.

Agents are opening career doors for engineering

Across system reliability and site operations, evaluations and quality assurance, and data pipeline management, 80% or more of respondents see meaningful career opportunity ahead. We believe this is one of the most significant moments in the history of building software, not because agents replace what technical people do, but because what’s left when they take on the toil is the work that defines a career: the judgment calls, the architectural vision, the reasoning to navigate complexity under pressure. That fluency will define the next generation of technical leadership.

We’re living this shift at Microsoft, right alongside our customers. Junior developers are using agents to explore codebases on their own and arriving at mentoring conversations with sharper, more sophisticated questions. Senior engineers are covering more ground because the repetitive work that used to fill their days is now delegated, and the work that’s left is harder, more interesting, and more consequential. Both are growing into more capable versions of themselves. For me, that’s the outcome I’ve always believed technology could deliver.

An integrated approach to intelligence and trust

Designing more sophisticated agent systems has made one thing clear: agents thrive in well-integrated environments, working best when your whole stack draws on a single source of truth. The high-confidence tasks are the ones we’ve already figured out; the meaningful frontier is the harder, interconnected work, and that’s exactly where observability, governance, security, and unified intelligence have to operate as one.

Microsoft IQ brings your enterprise context into a single, continuous intelligence layer. Within it, Work IQ builds semantic understanding of how your business operates across email, calendar, meetings, chats, files, people, and collaboration patterns. Such depth of knowledge is the reason technical teams choose us and it’s what drives my focus and passion in learning how people actually work so their agents get them. My colleague Kim Manis, CVP of Product for Microsoft Fabric, has written specifically about what this means for data professionals, and the integral role of Fabric IQ.

It’s all part of the Microsoft Agent Platform, which is becoming the operating system for enterprise AI at scale. From building in GitHub and contextualizing with Microsoft IQ, to running in Microsoft Foundry and governing in Microsoft Agent 365, Microsoft is uniquely positioned to help customers bring together data, models, agents, and human judgment into a continuously improving and secure system.

Frontier transformation is being led by builders like you.

Next steps:

• Download The 2026 Agent Confidence Index from our partners at MIT Technology Review Insights.It is a free, ungated deep dive into all 101 tasks, broken out by role and workflow, with the patterns and reasoning behind where confidence is strongest and the frontier is expanding.
• Join us at the AI Engineering World’s Fair (June 29-July 2) where our very own Pablo Castro will keynote, and our teams will offer 16 breakout sessions and 4 labs. Swing by the Microsoft booth as well to explore an interactive 3D visualization of the Index data. We want to hear what’s working for you right now.
• Learn more about Microsoft IQ and how it connects across Work IQ, Fabirc IQ, Foundry IQ, and the newly announced Web IQ. You can catch up on all the developer innovation from Microsoft Build through Satya Nadella’s keynote, Kyle Daigle’s blog post, and the Microsoft Build CLI.

The post The 2026 Agent Confidence Index: Where 300 builders see real momentum appeared first on Microsoft Azure Blog.

Linux workloads are evolving quickly as organizations modernize on-premises Linux applications, adopt cloud-native architectures, and build new AI and data-intensive pipelines in the cloud. As this shift accelerates, teams need a managed file platform that delivers the performance, resilience, security, and flexibility required to support these workloads in Azure.

Azure Files helps teams make that transition with fully managed file storage for Linux workloads. It combines familiar file access with built-in performance, data protection, security, and Azure service integration, making it easier to run shared file storage in the cloud without managing file infrastructure directly.

In this blog, we explore how Azure Files NFS supports modern Linux workloads across AI, cloud-native, enterprise modernization, and partner or ISV scenarios.

Speed up AI and data-intensive workloads

AI inferencing is where a trained model is loaded and serves predictions. Before any request is answered, the model’s weights, which capture what the model learned during training, must be read from storage into the serving instance. As models have grown to tens or hundreds of gigabytes, that loading step heavily influences how quickly an endpoint starts and scales. It happens every time a replica spins up, and because GPUs are among the most expensive resources in the stack, every minute they sit idle waiting on weights is money wasted.

Traditionally, teams embed model weights directly in the container image or have each replica download its own copy—producing bloated images, duplicated data, and cold starts that stretch to minutes just when an inference fleet needs to scale fast. With Azure Files, the weights live once on a file share that every replica mounts and reads simultaneously: images stay lean, there’s a single copy to manage, and new replicas come online in seconds for small and mid-sized models. Support for the Linux NFS nconnect mount option allows a client to establish multiple parallel TCP connections to the same file share, which can improve throughput at scale.

The new Zonal placement lets you co-locate file shares in the same availability zone as your GPU virtual machines, reducing round-trip latency for the data-intensive reads these workloads depend on. The provisioned v2 billing model lets you size IOPS and throughput independently to keep GPUs fed. The result is faster scaling, higher GPU utilization, lower cost per inference, and a more responsive experience for your customers.

Scale cloud-native applications with shared storage

Cloud-native applications often need shared storage that works directly with Kubernetes-based deployment models. This is especially important for workloads that need ReadWriteMany (RWX) access, persistent shared state, or storage that evolves with the application.

Azure Files integrates with Azure Kubernetes Service through the Azure Files CSI driver, enabling Kubernetes-native workflows such as dynamic provisioning through StorageClasses, expandable persistent volumes, and shared storage across multiple pods with ReadWriteMany access.

The cloud-native story is also about scale and speed. The new file share experience supports up to 10,000 file shares per subscription per region, helping teams support large multi-tenant or high-density environments. It also provisions about 2.5 times faster, with even greater gains in batch deployments, making it easier to scale shared storage quickly as application demand grows. The provisioned v2 model also helps teams manage total cost of ownership as they scale: you can start with smaller shares, grow capacity incrementally as demand rises, and use percentage-based metrics to monitor utilization and right-size shares over time.

Together, these capabilities make Azure Files well suited for cloud-native scenarios such as shared content repositories, configuration and artifact stores, CI/CD workflows, and containerized services that need persistent file storage across replicas or nodes.

Zooniverse, the world’s largest platform for people-powered research, puts this into practice. After migrating from AWS to a fully managed Azure Kubernetes Service environment, the team runs self-hosted Redis instances backed by Azure Files to provide caching and persisted data storage. The platform supports roughly 100 active projects and 10,000 to 15,000 daily users, with traffic that can spike to tens of thousands of API calls per minute. Deployments that once took an hour now run in three to 10 minutes, and the move to managed services on Azure reduced the team’s operational workload by about one full-time engineer.

Modernize enterprise applications with less disruption

Many enterprise applications built on NFS still depend on familiar Linux and POSIX-style behaviors, stable file semantics, and operational continuity. That makes wholesale refactoring expensive and risky.

Azure Files helps organizations modernize these applications by providing managed NFS file shares for Linux workloads while preserving familiar access patterns. That makes it easier to move existing Linux line-of-business applications like SAP and other workloads that depend on POSIX-compliant file shares, case sensitivity, or Unix-style permissions without forcing teams to refactor them first.

Organizations can use the new Azure Storage Mover and Azure Migrate support for NFS to assess, plan, and move Linux-based file workloads into Azure with less disruption to existing operations. Teams that prefer their existing tooling can also run third-party migrations from on-premises NAS using partners such as Komprise, giving Linux file estates more than one path into Azure. The result is a practical, end-to-end path for bringing existing Linux and NFS environments into Azure, where assessment, migration, and modernization happen as one motion rather than a series of disconnected steps.

Once applications are running in Azure, organizations can also strengthen business continuity and security. Features such as snapshots and soft delete help improve recovery options and protect business-critical data, while encryption in transit for NFS helps secure data moving between clients and shares. Combined with Azure networking options such as VPN and ExpressRoute, Azure Files provides resilience, and operational foundation enterprises expect for long-running Linux applications.

The new file share experience also simplifies governance, improves cost tracking at the share level, and enables more granular network configuration and role-based access control for different teams and applications.

Medline, an over $25 billion leader in medical-surgical supply manufacturing and distribution, shows this modernization path in action. As part of its migration from an on-premises SAP ECC on HANA environment to Azure, Medline built a cloud-native SAP solution leveraging fully managed Azure Files shares to provide zonally resilient shared storage. The re-architecture improved transaction times for key SAP workloads by more than 80 percent, cut response times for the most essential transactions by nearly 60 percent, and accelerated IDoc processing by more than 50 percent.

Build more with partners and ISVs

A key reason Azure Files fits partner ecosystems is that it speaks standard protocols rather than a proprietary interface. By exposing industry-standard SMB 3.x and NFS 4.1 endpoints, Azure Files works with existing applications, tools, and frameworks without code changes.

Azure Files NFS also expands what partners, ISVs, and platform teams can build on top of the service. Capabilities such as the NFS Snapshot, Soft Delete, and REST API for NFS shares help ecosystem partners integrate Azure Files into broader solutions for BCDR, data movement, management, and application enablement.

In the SaaS and ISV space, Azure Files can also serve as shared storage behind partner applications that need familiar POSIX-style access patterns without forcing customers to manage file infrastructure themselves. These same protocol and data-protection primitives also make Azure Files a target for database backup workloads—including SAP and Oracle backups—that partners and ISVs increasingly run against NFS or REST endpoints.

Azure Files also fits into adjacent developer and platform scenarios. For example, new GitHub-based workflows on Azure Kubernetes Service may use shared storage for runners, caching, artifacts, or job state. These scenarios show how Azure Files can bring together application platforms, developer tooling, and shared file storage.

Across AI, cloud-native, enterprise, and ecosystem scenarios, Azure Files NFS gives organizations a single managed file platform for modern Linux workloads. It helps teams support shared storage needs in Azure without stitching together separate solutions for each use case. To get started, explore Azure Files documentation. If you would like to learn more or discuss your scenario, contact us at azurefiles@microsoft.com.

The post Accelerate modern Linux workloads with Azure Files appeared first on Microsoft Azure Blog.

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Earlier this month, we announced that Windows 11, version 26H2—the next annual feature update for Windows 11—is now available for early testing and validation via the Windows Insider Program. As this release will share the same servicing branch as versions 25H2 and 24H2, devices can be updated using an enablement package, quickly and with minimal disruption to users. Microsoft also announced major Windows 365 updates at Build 2026, introducing ready-to-code Cloud PCs, expanded developer-focused capabilities, and new support for secure enterprise AI agents.

Now let's dive into more developments in the world of Windows for IT admins from the month of June.

New in Windows update and device management

• [RECOVERY] – Now generally available, point‑in‑time restore for Windows 11 can help users recover in minutes instead of hours by safely rolling a device back to a previous state. This built-in recovery capability is available for Windows Enterprise, Pro, and Home editions of Windows 11.
• [W365] – An optimized Teams experience for Remote App scenarios is now available, offering improved audio and video performance, reliability, and security.
• [PRINTING] – What was formerly the Modern Print Platform is now Windows Ready Print. It's now Windows preferred means of communicating to printers, including the Internet Printing Protocol (IPP), eSCL scanning, and Universal Print. Starting in July 2026, new printer installations will default to Windows Ready Print where supported, enabling a simpler and more reliable setup experience.
• [APPS] – Auto-updates in Microsoft Intune Enterprise Application Management are now available. Keep managed applications on the latest incremental release, such as 4.1 to 4.2, without manual packaging or admin intervention.

New in Windows security

• [SECURE BOOT] [TIPS] – If your organization hasn't yet finished updating Secure Boot certificates for client devices, servers, or virtual machines, find out which best practices can help. If you run into devices that are blocked from receiving updated Secure Boot certificates, explore actions you can consider.
• [SECURE BOOT] [LINUX] – New guidance is now available to help you manage Secure Boot certificate updates for Linux on Azure virtual machines. This includes Trusted Launch and Confidential VMs with Secure Boot enabled.
• [SECURE BOOT] [EVENTS] – In response to your feedback, two specialized Q&A events for Secure Boot will take place in July. Join Secure Boot Office Hours for virtualized environments (July 8). During OEM Secure Boot Office Hours (July 15), get answers from the OEM ecosystem, Broadcom, and Windows cloud experience experts. (Note: There is no on-camera or meeting component to these events. All Q&A will take place in the comments on the Tech Community.)
• [IDENTITY] – IAKerb and LocalKDC are new capabilities that expand Kerberos authentication across enterprise and local account scenarios. That's how Microsoft is advancing efforts to reduce NTLM dependency and strengthen security. Available today in the Windows Insider Program, a public preview is coming for both client and server.
• [W365] [DATA PROTECTION] – Context-based redirections for Windows 365 can now be explored in public preview. Apply more granular controls to device and resource redirection based on contextual signals. Available signals are device management state, compliance posture, user or group membership, and network conditions.
• [HARDENING] – The final deployment phase for Kerberos RC4 hardening begins with the July 2026 Windows security update. This phase completes the transition from legacy encryption types such as RC4. It removes Audit mode and leaves Enforcement mode as the only supported behavior for Kerberos RC4 usage on Windows domain controllers.

To explore what's new in security across the Microsoft platform, see What's new in Microsoft Security: June 2026.

New in AI

• [AGENTS] [SECURITY] – At Build 2026, Windows introduced new security foundations for AI agents. They're designed to provide governance, containment, and enterprise-grade controls for autonomous agent workloads.
• [AGENTS] [W365] – Windows 365 for Agents is generally available within Agent 365. With this update, Cloud PCs that enable AI agents can execute multi-step workflows across software. Use it to open apps, navigate interfaces, enter inputs, and process data.

New in Windows Server

For the latest features and improvements for Windows Server, see the Windows Server 2025 release notes and Windows Server, version 23H2 release notes.

• [NETWORKING] – DoH support for Windows DNS Server is generally available on Windows Server 2025. You can now deploy encrypted and authenticated client-to-resolver DNS traffic directly within your existing on-premises DNS infrastructure.
• [HOTPATCH] – Hotpatch update support for Windows Server 2022 Datacenter: Azure Edition has been extended through October 2027.

New in productivity and collaboration

Install the June 2026 security update for Windows 11, versions 25H2 and 24H2 to get these and other capabilities, which will be rolling out gradually:

• [AUDIO] – Shared Audio can keep people productive on the move. Two people can now to listen to the same audio from a single Windows 11 PC at the same time.
• [SECURITY] – When Windows Hello face or fingerprint is set up and available, it's now the default sign-in method every time you sign in. Even if you used a different method previously.
• [FILES] – Windows Search will now find and prioritize files with as few as two characters.
• [BATTERY] – This update improves resiliency against apps that could keep the sensor hub powered on and drain power. Enjoy a better battery life.

New features and improvements are coming in the July 2026 security update. You can preview them by installing the June 2026 optional non-security update for Windows 11, versions 25H2 and 24H2. This update includes the gradual rollout of:

• [WIDGETS] – A quieter, more focused Widgets experience helps reduce interruptions and improves default settings and notification controls. For example, Widgets no longer open on hover, and notifications and taskbar badges are minimized by default.
• [ACCESSIBILITY] – You can now apply a full-screen color overlay to help reduce eye strain and improve readability. You can also enter a zoom percentage directly and change it in increments in the Magnifier window for more precise, flexible control. And, you can now use voice access and voice typing in French, German, and Spanish.

To learn about planned productivity, security, and reliability updates for Windows 11, visit the Windows Roadmap.

Lifecycle reminders

• [SERVER] – DirectAccess has been deprecated and will be removed in a future version of Windows Server. It has been replaced by a more modern, flexible solution: Always On VPN. For migration guidance, see Remote Access Always On VPN migration.
• [WINDOWS 10] [ESU] – The Windows 10 Extended Security Updates (ESU) program for personal use devices is being provided for an additional year. Coverage is now available through Oct. 12, 2027.

Check out our lifecycle documentation for the latest updates on Deprecated features in the Windows client and Features removed or no longer developed starting with Windows Server 2025.

Additional resources

Looking for the latest news and previews for Windows, Copilot, Copilot+ PCs, the Windows and Windows Server Insider Programs, and more? Check out these resources:

• Windows Roadmapfor new Windows features – filter by platform, version, status, and channel or search by feature name
• Microsoft 365 Copilot release notesfor latest features and improvements
• Windows Insider Blogfor what's available in the Beta and Experimental channels
• Windows Server Insiderfor feature preview opportunities
• Understanding update history for Windows Insider preview features, fixes, and changesto learn about the types of updates for Windows Insiders

Join the conversation

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