Kategorie: Microsoft Azure

Advancing hardware security with open-source Integrated HSM technology

At this year’s OCP Global Summit, Microsoft announced Project Tessera, the evolution of the Azure Integrated Hardware Security Module (HSM), into a fully open-source initiative developed in collaboration with Marvell®, Oracle, and Open Compute Project (OCP). Project Tessera advances our broader commitment to open, industrywide standards for platform security.

The motivation behind Project Tessera is straightforward: the entire industry benefits when we collaborate and advance hardware security together.

Project Tessera carries forward the workload directly attached to the HSM architecture introduced by Azure Integrated HSM, making the architecture, specifications, protocols, drivers, software, firmware, and reproducible build environments open source for the broader community. Over time, the initiative will advance toward a fully open-source silicon implementation, while making hardware broadly available, enabling organizations across the ecosystem to adopt, integrate, and contribute to the platform.

Explore the Tessera Integrated HSM GitHub

This approach builds on the model Microsoft established with other industry collaborators on Caliptra, a fully open-source silicon root-of-trust that has since been broadly adopted across the hardware ecosystem. Through Project Tessera, industry leaders including Microsoft, Marvell®, Oracle, and the Open Compute Project will apply that same model of transparency and collaboration to advance hardware key protection with the industry.

The cybersecurity landscape is rapidly changing, and protecting keys and credentials has never been more important. We are now facing two generational transformations that are fundamentally reshaping how we view the cybersecurity landscape:

AI is increasing the speed and scale of cyberattacksAI-orchestrated attacks are increasing the scale, speed, and sophistication of attacks, giving adversaries new ways to operate and compressing the time between vulnerability discovery and exploitation. These capabilities amplify risks such as credential theft, key exfiltration, and signing abuse.Keys and credentials are high-value control assets. When compromised, they can enable attackers to impersonate trusted entities, bypass security controls, authorize malicious operations, expose sensitive data, and undermine software security defenses. Protecting these assets requires infrastructure with layered, hardened protections capable of withstanding sustained attacks across multiple concurrent attack vectors.

Quantum computing is changing the cryptographic risk horizonQuantum computing promises transformative advances, but it also presents a risk to many of today’s cryptographic algorithms. A sufficiently powerful quantum computer could break widely used public-key cryptography, undermining digital signatures, authentication, and identity verification.Post-quantum cryptography was once viewed as an important but distant technology shift. Advances in quantum computing have changed that perspective as organizations recognize the urgency, scale, and complexity of the transition. HSM-backed signing infrastructure will be among the first critical systems to migrate, requiring HSMs that support post-quantum cryptography and are interoperable with classical HSMs to support migration.

These emerging threats motivated the development of Azure Integrated HSM, which was designed to meet rigorous security standards for hardware-based key protection, while also accelerating Microsoft’s transition to post-quantum cryptography through scale deployments of quantum resilient integrated HSMs.

Azure Integrated HSM introduced a workload-attached HSM that performs cryptographic operations closer to the workload while keys remain continuously protected within the HSM security boundary. This architecture strengthens key protection, improves cryptographic performance, offers crypto-agility, and provides a simpler operational model.

Tessera is an evolution of Azure’s first integrated HSM model, incorporating insights gained from deploying Azure Integrated HSM at scale, and advances the technology through collaboration in the Open Compute Project, with the goal of broadly addressing the challenges of key and credential management in the era of AI and quantum computing.

Eliminating key management anti-patterns

In central HSM deployment models, HSM infrastructure is separate from compute resources, requiring cryptographic operations to traverse the network. The resulting latency and operational dependencies can lead enterprises to adopt workarounds that improve performance or availability but unintentionally weaken key protections. Some of these common key-management anti-patterns include:

HSM keys may act as certificate authorities and endorse externally generated keys. These keys can then serve as trusted delegates without receiving the same hardware protection.

HSM generated keys may be created as extractable and exported from the HSM in wrapped form. When used outside of the HSM the keys may not receive assurance levels equivalent to a FIPS 140-3 Level 3 HSM and become more susceptible to exfiltration or misuse.

Other anti-patterns include caching keys outside of HSMs for resilience, using software-held keys to reduce network latency to remote HSMs, sharing keys and security domains to simplify governance, and relying on proprietary protocols for interoperability. These practices intend to prioritize operational flexibility but can make key protection, policy enforcement, and auditable provenance less consistent across environments, undermining the security value HSMs are designed to provide.

The goal of Project Tessera is to advance the integrated HSM model to address these challenges in the industry, without compromising on security. The project will deliver Tessera Integrated HSM, a fully open-source integrated HSM designed to consistently enforce FIPS 140-3 Level 3 key protection throughout the key’s lifecycle.

By combining strong hardware-rooted protection with an open-source implementation and open protocols, Tessera Integrated HSM creates a path toward greater transparency, interoperability, and innovation across the HSM ecosystem. The native support for quantum-resilient cryptography will help ease the industry’s transition to post-quantum cryptography.

Figure 1: Tessera Integrated HSM

Demonstrating an open, interoperable HSM ecosystem

Today at the Open Compute Project Global Summit, Microsoft and Marvell® demonstrated the Tessera Integrated HSM transfer protocols establishing HSM partition cloning and resiliency rings across a Tessera Integrated HSM and a Marvell® LiquidSecurity® 2 HSM. The demonstration showed how two heterogeneous HSMs can operate in a resilient and unified security domain. Non-extractable keys within the security domain always remain protected within the HSMs and are available only to the authorized HSM partitions participating in the security domain and remain under the sole control of the partition owner.

The Tessera Integrated HSM protocols use post-quantum cryptography to protect communication between HSMs. This enables authorized workloads with a Tessera Integrated HSM to use both classical cryptography and post-quantum cryptographic services while their keys remain secured within the HSM boundary and can only be authorized for use by the key owner.

Figure 2: Partition cloning example

The Project Tessera specifications, protocols, software libraries, drivers, firmware, and hardware are available through the Tessera Integrated HSM GitHub. Going forward, this repository will add more open-source silicon, new hardware designs, and enhancements developed with the community.

Expanding Project Tessera through confidential computing

The early collaborators working on Project Tessera within the OCP community are exploring how the Tessera Integrated HSM key protection architecture can extend beyond systems with dedicated integrated HSM hardware. As part of this work, the project is making a minimal, unikernel-based Tessera firmware stack open source, designed to run within hardware-isolated confidential computing environments.

This implementation uses the same firmware stack as the hardware integrated HSM, extending a common security architecture across HSMs and confidential computing environments. In this model, cryptographic keys are protected within a confidential computing container, which provides cryptographic services to securely bound workloads, including worker containers and virtual machines. The initial implementation leverages AMD SEV-SNP and integrates with open-source virtualization technologies such as OpenVMM, with the Open Compute community working to extend support to other confidential computing architectures.

The complementary deployment models in Project Tessera extend strong key protection across systems with different hardware capabilities and assurance requirements. For platforms without dedicated integrated HSM hardware, confidential computing provides broadly deployable, hardware-isolated key protection with a minimal trusted computing base.

Project Tessera provides the highest level of hardware assurance, protecting keys within a dedicated, tamper-resistant security boundary designed to meet demanding certification requirements, including FIPS 140-3 Level 3 and Common Criteria.

Figure 3: Protection profiles for assurance levels

The deployment models share the same Project Tessera firmware and programming model, giving organizations a consistent foundation for cryptographic security across their infrastructure while providing a natural path to integrated HSM and higher levels of assurance in newer systems.

Together, these models make strong key protection accessible across a broad range of systems with different security assurance needs, all built on a common, open architecture. As the Open Compute Project community continues to advance Tessera Integrated HSM, we see an exciting opportunity for broader industry collaboration to strengthen key protection, expand secure key management, and accelerate innovation across the ecosystem.

Explore the Tessera Integrated HSM GitHub

For more information, visit the Microsoft booth at the Open Compute Project Global Summit for live demonstrations, explore Tessera Integrated HSM implementation on GitHub, and read the Microsoft Infrastructure Community blog for additional hardware security announcements: OCP2026 Hardware Security Updates.

Check out Microsoft’s presence at OCP Summit this year: aka.ms/OCP2026

Learn more about Azure Infrastructure: aka.ms/silicontosystems

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Infrastructure for the frontier era: Scaling intelligence through open industry collaboration

At Microsoft, our vision is to build a Frontier Firm: one where people and technology work together, combining human expertise, context, and judgment with technology that can extend intelligence throughout everyday work. The potential is significant: helping people create, solve complex problems, and make progress on some of society’s most consequential challenges.

But realizing that potential requires frontier infrastructure. The next phase of Super Intelligence needs a unified infrastructure engine that delivers more intelligence with greater efficiency, making AI more available, reliable, and sustainable across science, healthcare, education, and the economy. This is the work of an entire ecosystem, and precisely the vision we have advanced for years through our industry collaboration within the Open Compute Project Foundation.

Designing silicon in the loop with real workloads

As we enter the next phase of Super Intelligence, inference is becoming the dominant workload and that’s where efficiency, latency, and cost really matter. Azure Maia 300, our next generation AI accelerator from our Maia family, advances the inference efficiency frontier. Maia 300 is a complete system designed for the new types of AI workloads emerging today: long-context reasoning, latency-sensitive conversational experiences, and sustained, multi-step agentic tasks. By improving efficiency across the system, we expect Maia 300 to deliver 40% more tokens per watt compared to leading GPU-based systems. And in a full 96-accelerator configuration, it delivers up to seven times the rack-scale token throughput of Maia 200.

Learn more about Maia 300

Maia reflects our system co-design approach. It is designed alongside Microsoft infrastructure and production workloads, bringing models, application frameworks, kernels, communication libraries, systems software, and custom silicon into one optimization loop. Maia 200 is deploying across the United States, Europe and Australia and delivering up to 1.7x more tokens per watt for built-in AI capabilities in Microsoft Teams, Copilot and Cowork experiences compared with leading GPU infrastructure.

Azure Maia 200 systems in a datacenter

AI is not only about accelerators. Agentic workloads create entirely new demands on CPUs. Every agent interaction generates orchestration, data retrieval, memory management, and service calls that must occur quickly and efficiently. That is why we designed Cobalt 200 as an agent-native CPU. Cobalt 200 delivers more than 50% performance improvement over Cobalt 100 and reduces agent call latency by 33%, while providing 23% higher throughput on agentic workloads.

Azure Cobalt 200 systems in a datacenter

Maia and Cobalt are designed in the loop with our largest enterprise workloads. That gives Microsoft a unique advantage in tuning performance, reliability, and cost to exactly what customers need in production.

We are applying the same principle to storage and security. The Azure Boost Data Processing Unit moves compression, networking transactions, and security functions away from the CPU onto purpose-built silicon, enabling us to deliver four times more performance while using significantly less power.

And Azure Integrated HSM makes security part of the hardware foundation, with our in-house security chip in all new datacenter servers. It brings cryptographic operations closer to the workload while keeping keys protected inside the hardware security boundary—not exposed in application memory.

Building an open, resilient AI fabric

Compute creates intelligence and networks make it usable at scale.

Working with industry partners, Microsoft developed Multipath Reliable Connection (MRC), rethinking Ethernet for AI. Its multi-plane architecture scales beyond 100,000 GPUs with two network tiers, reducing power, complexity, failure points, and cost. By intelligently distributing traffic across multiple paths and through packet spraying, MRC limits congestion and maintains reliable performance for frontier-scale training.

We are also advancing open scale-up connectivity through Optical Compute Interconnect Multi-Source Agreement (OCI MSA). OCI, the first optical specification for multi-rack scale-up systems, enables high-bandwidth, power-efficient, multi-vendor infrastructure. Moving forward, we will continue to work with the industry to expand participation in OCI MSA.

In parallel, The Ethernet for Scale-Up Networking (ESUN) workstream brings together more than 175 organizations to define requirements for Ethernet-based scale-up networking. The group has released specification version 1.0 and is developing version 1.1 for later this year. Read more about our networking innovations with the open community.

Rethinking power from grid to silicon

AI infrastructure is also forcing a fundamental redesign of power delivery. Through OCP and the Open Systems for AI initiative, Microsoft is helping advance the industry’s transition toward 800 volt direct current (VDC) infrastructure.

Mt. Diablo, Microsoft’s disaggregated high-voltage datacenter (DC) sidecar architecture, separates power conversion from the compute rack so each can scale independently. It includes a modular design supporting approximately 100 kilowatts (kW) to beyond one megawatt (MW), with redundant feeds, telemetry and optional energy storage. Microsoft, together with OCP partners, contributed Mt. Diablo specifications v0.7 and v1.0 to OCP.

Microsoft and its partners are advancing solid-state transformer-based architectures to make power conversion more efficient, responsive, and scalable for next-generation 800 VDC datacenters. Through OCP’s Clean Backup Power initiative, we are developing open requirements for reliable backup systems that reduce reliance on diesel. In parallel, Microsoft collaborated with partners to release a common grid-interface specification to align utilities, operators, and equipment vendors as hyperscale power architectures evolve. Read more about Microsoft’s approach to 800VDC transition and scaling power infrastructure sustainably.

Cooling as a first-class design consideration

Microsoft approaches cooling as a datacenter-wide challenge, driving innovation from DC facility design to rack-level liquid cooling technologies.

Our third-generation Azure Heat Exchanger Unit, Mt. Atlas, nearly doubles cooling capacity compared to the previous generation while maintaining the same operational footprint. We are also contributing public reference architecture through OCP to help accelerate industry adoption.

At the datacenter level, Microsoft is advancing a zonal cooling strategy in its next-generation AI datacenters to handle the growing mix of liquid-cooled accelerators and traditional air-cooled systems. By tailoring cooling to the specific needs of each workload, and introducing multiple independent water loops, each supplying coolant at different temperatures, we aim to increase efficiency and performance while supporting Microsoft’s energy, carbon, and water reduction goals.

Open hardware security innovation

Microsoft believes strong cloud security is built through openness, transparency, and industry collaboration. This principle is reflected in our founding role in the Confidential Computing Consortium, our work to establish Caliptra as an open-source silicon root of trust, and our contributions to the OCP S.A.F.E. security conformance framework. As AI reshapes the cybersecurity landscape and advances in quantum computing introduce new cryptographic risks, protecting the keys and credentials at the foundation of digital trust has never been more important.

To address these challenges, and building on our commitment to security transparency and industry collaboration, Microsoft, Marvell, and Oracle are introducing Tessera Integrated HSM, an open-source initiative for integrated hardware security modules. Tessera Integrated HSM carries forward the workload direct attached HSM architecture introduced by Azure Integrated HSM, open-sourcing its architecture, specifications, protocols, drivers, software, firmware, and silicon to the broader community. Learn more about Tessera Integrated HSM.

Fleet resiliency at scale

As AI infrastructure scales, fleet reliability is essential to protecting customer workloads, capacity, and service quality. Through OCP, Microsoft has published common RAS, debugging, telemetry, and diagnostics specifications, while contributing work on hardware fault management, CPU and GPU management, and fleet memory fault management. We are using years of fleet data and using AI to validate, predict, and drive fleet actions aimed at preventing faults, pinpointing issues, and recovering more autonomously. Learn more about how Microsoft is advancing fleet reliability through industry collaboration.

Advancing sustainability across the infrastructure stack

Scaling AI responsibly means considering the full datacenter lifecycle to help reach our 2030 commitments to be carbon negative, water positive, and zero waste. As a company, we are committed to incorporating sustainability into how we design, build, and operate our datacenters extending to the communities where we build them. Through OCP, Microsoft is collaborating with partners on low-embodied-carbon steel and concrete—with pathways to reduce concrete emissions by more than 50% per cubic yard.

We are advancing waste-heat reuse, and helping develop Cloud Services Product Category Rules for consistent, auditable lifecycle-carbon reporting. Inside our datacenters, closed-loop liquid cooling, intelligent power management, and renewable-energy agreements help reduce resource use, while Circular Centers reuse or recycle more than 90% of servers and components. Beyond the datacenter, we are investing in local skills, partnerships, and infrastructure so the communities hosting this growth can grow with us. Read Microsoft’s 2026 Environmental Sustainability report.

Building the frontier together

Our vision is to help every organization become a Frontier Firm, uniting human expertise with AI in a continuous learning loop that turns knowledge and ingenuity into scalable intelligence. Realizing that vision requires full-stack co-design: silicon, compute, memory, networking, power, cooling, models, and applications working as one secure, reliable, efficient, and sustainable platform while giving customers choice without passing complexity on to them.

Such a future cannot be realized by a single organization. Through OCP, we are advancing open interfaces, sharing lessons from operating infrastructure at global scale, and collaborating across the industry so innovation moves faster, and the ecosystem stops solving the same problems repeatedly. Together, we can transform frontier innovation into infrastructure that makes intelligence accessible and affordable for every organization, in every industry, and across every region of the world.

At OCP Global Summit 2026, we invite the community to build that future with us.

Build for what’s next

Learn more about Microsoft’s systems approach to infrastructure.

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Quelle: Azure

AI transformation across the infrastructure lifecycle: From supply chain to fleet operations

AI infrastructure is a system, and every part of that system is connected. Decisions made in silicon and systems design influence how infrastructure is sourced, deployed, and operated across the fleet. And what we learn once that hardware is running can inform what we build next.

This feedback matters because the system never stands still. Demand shifts, component constraints emerge, new capacity comes online, and hardware requirements evolve across the fleet. Keeping infrastructure reliable means continually learning and adapting as requirements change.

At Microsoft, Azure Hardware Systems and Infrastructure works across that lifecycle, from systems architecture and design through supply chain, deployment, and fleet operations across Azure’s more than 80 regions and 500 datacenter campuses. This end-to-end view gives us an opportunity to connect insights across the hardware lifecycle, so what we learn in one part of the system can improve decisions across the others.

Learn more about Azure infrastructure

AI can accelerate that learning. Across our own AI transformation, we are applying agentic and AI tools to help teams connect information, understand what is changing, and act sooner while keeping human judgment at the center. The opportunity is bigger than making individual tasks faster. It’s to build a system that learns from how infrastructure is designed, sourced, and operated, and applies those learnings to what comes next. This approach is part of our broader AI transformation journey.

Start with the work, not the AI

This process has reinforced a critical lesson as we’ve scaled how we apply AI as a force multiplier across our cloud infrastructure: AI transformation starts with the work, not the technology. Speed matters, but the greater opportunity is to redesign how decisions are made: what information is available when a decision needs to happen, how quickly teams can understand what changed, and where human judgment matters most.

Our cloud supply chain is a good example of this principle in practice. Every month, demand-planning teams forecast Azure’s infrastructure needs years into the future, accounting for changing customer demand, regional needs, installed capacity, and decommissioning activity. When a plan changes, determining why could require reconciling information across multiple systems, turning a single investigation into a lengthy process. It was tempting to look at that work and ask where we could add an agent.

For any company, putting AI on top of a fragmented process can simply make the fragmentation move faster. Before applying AI, our teams mapped and simplified the work, established a shared data foundation with quality, governance, and access controls, and identified decisions where people needed to remain accountable. We call this approach “Lean before AI.”

Starting with end-to-end processes and taking an AI-driven approach provides new ways of working and moves teams to parallel execution rather than sequential handoffs, resulting in integrated, collaborative workflows.

From days of research to decisions in minutes

With this foundation in place, we approached demand planning differently. A multi-agent workflow can examine signals such as installed-base shifts, regional demand, and decommissioning changes, then help planners understand what changed, where, and what drove the movement. Work that previously took five to seven business days can now be completed in hours, and sometimes in less than 20 minutes. Across more than five monthly planning cycles, our full demand-planning team saw approximately 50% less manual effort and cycle time fell by up to 75% in selected workflows.

The same pattern is taking shape across planning, product data, sourcing, fulfillment, logistics, and operational workflows. Specialized agents are helping teams spend less time finding and reconciling information and more time applying expertise.

In fulfillment, understanding why rack delivery is blocked from meeting customer demand could require teams to pull information manually from multiple sources. An intelligent assistant now brings together information about blockers and compatible or incompatible supplies, saving investigation time by as much as 55%. In logistics, an AI-powered logistics agent brings together data across air, land, and sea options to help teams evaluate speed, cost, as well as carbon tradeoffs and forecast emissions.

Building the learning loop

These individual applications matter, but the larger opportunity is to connect them. Our cloud supply chain team is moving toward end-to-end multi-agent workflows across bill-of-materials generation, capacity delivery, spare-parts management, capacity docking, and sales and operations execution. This work reflects a broader shift in our business: moving beyond isolated experiments toward a faster, more resilient, and intelligent operating system that places human judgment at the center.

The important outcome is not simply speed. Planners can begin with connected evidence instead of spending days assembling it, giving them more time to examine the explanation, add business context, and determine what it means for the decision ahead.

That is the learning loop we want. AI helps people reach the evidence faster. People bring context and judgment, act on what they learn, and create new information that can improve the next decision.

Learning across the fleet

The hardware lifecycle does not end when a server reaches a datacenter. Once infrastructure is deployed, the challenge becomes keeping it operating reliably for customers. Across millions of nodes in our fleet, continuous monitoring generates signals that help our teams investigate issues and determine root causes to decide how to act.

Across Azure, we’re moving cloud reliability upstream—transforming fleet management from reactive firefighting into a closed-loop system that prevents defects, predicts failures, and automatically restores hardware back into service. As we move toward a self-healing fleet, we are applying the same principles: connecting data across the lifecycle, continuous evaluation, redesigning workflows for human-agent orchestration, and keeping engineers in control of production decisions. Ultimately, this is also when the next learning cycle begins: systems collect and analyze information about the fleet, and failure patterns become evidence for how suppliers design and build the next generation of hardware.

Azure failure prediction and detection uses AI to analyze fleet telemetry so teams can identify emerging hardware failure patterns sooner and take action before potential issues impact customers. Engineers retain oversight of production decisions. This has already resulted in a 92% reduction in disk-related virtual machine (VM) interruptions and reduced repair time on out-of-service nodes by 53%. For rack managers, prediction provides up to three days of advance warning, enabling proactive recovery that cuts out-of-service repairs by 40%.

We also proactively and periodically screen our fleet to identify hardware vulnerable to silent data corruption before customer workloads are deployed, helping strengthen platform reliability.

We are also developing workflows that preserve context as hardware moves through investigation and recovery. For faulted resources, these workflows track assignment, action, outcome, and next step, with policy and approval controls around fleet actions. History and outcomes can then inform future decisions.

This is where the broader systems story comes together. Demand-planning decisions influence sourcing. Logistics affects when capacity reaches a datacenter. Once hardware is running, fleet telemetry and operational outcomes create another source of learning. Information about component performance can help teams proactively address potential issues before customers experience them. Those insights can also feed forward into the next generation of silicon, system, and rack design, while giving suppliers information to improve future components.

AI can make that loop faster, but the value comes from helping the system work better as a whole.

What we learned when things did not work

Some of our most useful lessons came from approaches that fell short.

We learned that applying AI to one part of a fragmented process can accelerate that task while creating more work somewhere else. An agent might produce its output faster, but if a downstream team must interpret, reformat or reconcile it manually, the workflow as a whole has not improved.

That changed how we measured success. Instead of evaluating only the task an agent performs, teams must examine the full workflow: the work removed, the new work created, the quality of the decision, and the outcome.

Reliable, accessible, and well-governed data is a prerequisite, not an afterthought. AI cannot compensate for conflicting definitions, unclear permissions, or information isolated across systems.

And we learned not to become attached to a particular architecture or agent. Models, frameworks, and business needs continue to change. A solution that is useful today may need to be redesigned six months from now or retired if the original business need no longer applies.

The resulting rhythm is practical: begin with a consequential decision, simplify the work around it, connect the right governed data, build alongside the people who know the work, evaluate the complete outcome, and keep changing as the business and technology evolve.

What we measure next

The next phase of enterprise AI will require us to measure more than adoption: how many people use an agent, how many agents are deployed, or how much time they save. Those measures matter, but they don’t tell us whether the work itself has improved. Can a planner understand in minutes a change that once took days to explain? Can a fulfillment manager resolve a capacity blocker without manually reconciling information across systems? Can an engineer identify a potential hardware problem before it becomes a customer problem? And can teams apply their expertise, so the next decision is better than the last?

That is the direction we are pursuing: a way of working that keeps learning as the technology and the business change. We are not trying to build the largest collection of agents or automate decisions simply because we can. We are working to produce more useful output from the infrastructure, information, and expertise already in the system.

The yield imperative

If AI is going to change what the world can build, we need to keep evolving how we build the infrastructure behind it. By connecting insights across silicon, systems, supply chain, and fleet operations, each stage can help improve the next. The result is reliable infrastructure ready when customers need it, and a system that learns how to deliver it better with every cycle.

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Microsoft named a Leader in the 2026 Gartner® Magic Quadrant™ for Global Industrial AIoT Platforms

We’re proud to share that Microsoft has been named a Leader in the 2026 Gartner® Magic Quadrant™ for Global Industrial AIoT Platforms. We believe this recognition reflects our vision for the future of industrial operations: moving toward intelligent systems that continuously learn, adapt, and improve business outcomes.

Read the full 2026 Gartner® Magic Quadrant™ for Global Industrial AIoT Platforms report

As industrial organizations modernize, they need a foundation that connects operations across cloud and edge environments. Azure’s adaptive cloud approach, powered by Microsoft Azure IoT, Microsoft Azure Arc, Azure Local, Microsoft Fabric, and Microsoft Foundry, enables organizations to connect operational assets, contextualize data, apply AI-powered reasoning, and close the loop between insight and action.

The shift towards learning operations

The first wave of industrial digital transformation was driven by IoT, connecting operational assets and making industrial data accessible, at scale. That visibility remains the foundation, but on its own it is not enough. Industrial organizations are contending with skilled labor shortages, supply chain volatility, increasing customization demands, and growing operational complexity. Success now depends on turning operational knowledge into scalable intelligence that improves throughput, quality, resilience, safety, and sustainability.

This is where Industrial AI and Physical AI create new value. Industrial AI applies intelligence to operational data, identifying patterns, predicting outcomes, diagnosing conditions, and recommending actions. Physical AI extends these capabilities into the real world, enabling autonomous or human-assisted action that learns from results and improves performance over time. Together with IoT, they establish a business learning loop:

IoT helps organizations understand operations. The people learn.

Industrial AI helps organizations reason over operational data, identifying patterns, predicting outcomes, and recommending actions. The systems learn.

Physical AI helps organizations act, whether through autonomous operation or human-in-the-loop workflows. By measuring outcomes and feeding results back into future decisions, the process learns.

The outcome is more than automation. It is a transition from connected operations to learning operations, where every cycle of understanding, reasoning, action, measurement, and learning improves the next outcome. As the loop compounds over time, productivity increases, resilience improves, expertise scales, and the business itself becomes smarter.

Grounding the loop in organizational intelligence

A learning loop must be grounded in the operational context unique to each organization. By applying AI through secure, governed platforms, organizations can capture and scale the knowledge embedded in their people, processes, assets, and operations, while maintaining control of the expertise and intellectual property that differentiate their business. As that knowledge becomes institutional intelligence, it can be applied across adaptive workflows that continuously improve from real-world outcomes, helping organizations turn operational data into a durable source of competitive advantage.

To accelerate this journey, the recently announced Microsoft Frontier Company initiative brings together industry expertise and enterprise-grade AI engineering to help customers design, deploy, and scale AI systems grounded in their own organizational intelligence, so they can translate operational expertise into measurable business impact more rapidly.

Recognized as a leader in industrial AIoT

Gartner defines the global industrial AIoT platforms market as a series of seamlessly integrated technologies that deliver industrial data aggregation and curation, device management of industrial assets, ease of integration with IT systems, and the ability to unlock the value of industrial applications and AI.

We continue to help industrial organizations transform by focusing on the areas our customers prioritize:

Unified platform for industrial needs

Microsoft’s industrial AIoT platform extends Azure from cloud to edge, providing a consistent foundation for managing and securing industrial environments while connecting data and AI across operations. Key capabilities include Microsoft Azure IoT Hub, Azure IoT Operations, Microsoft Fabric, and Microsoft Foundry. Together, these capabilities help organizations make data-driven decisions, boost operational efficiency, and scale AI across varied deployment environments.

Comprehensive management and security

Microsoft offers a unified management and security plane where millions of distributed devices and assets, from factory Programmable Logic Controllers (PLCs) to autonomous vehicles, can be managed as first-class Azure resources. Devices and assets connected through Azure IoT Hub and Azure IoT Operations are onboarded as Azure Resource Manager resources, enabling deep integration with Azure Device Registry and Azure’s control-plane AI capabilities for governance, insights, and automation. Microsoft is advancing a Zero Trust security roadmap across the device lifecycle, from trusted identity, certificate management, secure updates, and firmware risk monitoring to policy-driven deployments and integrated SecOps. Encryption, confidential computing, sovereign operations, and auditable patching help customers strengthen resilience and meet evolving regulatory requirements.

Governed industrial data foundation

Microsoft provides a unified data plane delivered through Azure IoT Hub, Azure IoT Operations, Azure Device Registry, and Microsoft Fabric. The platform ingests OT, IoT, and enterprise data; transforms raw telemetry into structured, semantically aligned signals; supports Unified Namespace and ontology patterns; and makes governed, reusable, AI-ready data available across many different analytics and agent systems. Support for open formats and industry standards enables interoperable digital threads, digital twins, and AI-powered OT/IT workflows, while low-code and no-code options let subject matter experts drive the creation of data pipelines and analytics.

Agentic industrial operations

Microsoft is advancing industrial intelligence through agentic operations. Agentic AI can operate in both cloud and edge environments. In the cloud, agents use ontologies such as Fabric IQ to understand operational context, monitor conditions, and collaborate with teams through tools like Microsoft Teams. At the edge, agents run within Arc-enabled adaptive cloud environments, using local semantic models to reason over site-specific data and assets, enabling intelligent operations even during periods of intermittent connectivity. For industrial teams, this can support practical decisions: detecting equipment anomalies, recommending maintenance windows, and prioritizing responses to quality issues or production constraints.

Shaping the future of digital operations

Ultimately, the future isn’t about connecting more things. It’s about creating intelligent systems that can perceive, learn, reason, and act, where every machine, every process, and every action makes the next outcome better than the last.

By combining connected assets, governed industrial data, AI-powered insights, and intelligent action, organizations can create systems that continuously adapt and improve from real-world outcomes. We believe this is the future of digital operations: intelligent systems that learn and deliver lasting business value.

Microsoft’s robust partner ecosystem can help bring local expertise and tailored solutions to every industry, unlocking new opportunities and delivering even greater impact. Whether co-innovating on industry-specific solutions or scaling AI adoption globally, our partners are essential to helping customers build confidently on Azure.

Learn more

Discover how Microsoft’s industrial AIoT offerings can enhance your operations:

Explore the Azure IoT portfolio, Microsoft Fabric, Microsoft Foundry, and Copilot Studio.

Check out our Partner Resource Guide.

Recognized as a Leader

in 2026 Gartner® Magic Quadrant™ for Global Industrial AIoT Platforms

Read the report

Gartner, Magic Quadrant for Global Industrial AIoT Platforms, 15 September 2026, Scot Kim Et Al.

Gartner does not endorse any company, vendor, product or service depicted in its publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner publications consist of the opinions of Gartner’s business and technology insights organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this publication, including any warranties of merchantability or fitness for a particular purpose.

Gartner and Magic Quadrant are a trademark of Gartner, Inc., and/or its affiliates.

This graphic was published by Gartner, Inc. as part of a larger research document and should be evaluated in the context of the entire document. The Gartner document is available upon request from Microsoft.
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Quelle: Azure

Responsible infrastructure at hyperscale: Managing the full lifecycle of Azure hardware

Every new generation of cloud infrastructure creates two jobs. We must deploy more capable systems quickly, and we must manage the hardware they replace responsibly.

Even though these jobs happen behind the scenes, the results matter to Microsoft Azure customers. Newer systems deliver more compute capacity from the space and power available in a datacenter. At the same time, the reuse, refurbishment, and recycling of older equipment can extend the life of valuable components and return materials to the supply chain, ensuring cost efficiency and reliability.

Managing this process starts early in the lifecycle, with the design of our silicon, servers, networks, and datacenters. It continues through years of operation. When equipment leaves active service, Microsoft Circular Centers help determine its next useful life.

Discover more about Microsoft Circular Centers

With new Circular Centers in Newport, Wales, and Sydney, Australia, this work now spans North America, Europe, and Asia Pacific. These facilities help us recover components, prepare space for new infrastructure, and get more value from the hardware and materials already in our system.

Getting more from every rack and every watt

Cloud infrastructure must keep pace with the work customers want to run. That means increasing compute capacity while making better use of the space and power available in each datacenter.

Compared with early general-purpose systems, today’s servers provide significantly more processor cores, more memory capacity, and network bandwidth. As shown in the chart below, Azure infrastructure has become both denser and more efficient over time. Since 2014, cores per rack have increased approximately 13-fold, while the power required to complete the same task has decreased by roughly 90%. For example, a workload such as code compilation that consumed approximately 100 watts on early systems can now be completed using less than 10 watts.

This increase in compute density means we’re able to provide more server capacity to support customer workloads using the same datacenter footprint. By delivering more useful work from each rack, newer infrastructure helps improve resource efficiency while supporting continued growth and scale to meet cloud and AI demand.

Source: Microsoft internal analysis using combined average data across general compute infra partners and Cobalt using industry benchmark performance such as SPEC CPU report and industry-standard utilization markdown.

Azure Cobalt is one example of this systems approach. Our custom-designed cloud processor allows Microsoft to optimize silicon, servers, networking, storage, and software together. Cobalt 200 delivers up to 50% higher performance than Cobalt 100 and incorporates the latest Microsoft security, networking, and storage technologies.

This constant cycle of innovation is one of the defining characteristics of cloud infrastructure. As new generations of cloud and AI infrastructure are deployed, older hardware is gradually taken out of service. What happens to the equipment being replaced?

Giving hardware its next useful life

Last year, Microsoft achieved a 92% reuse and recycling rate for decommissioned servers and components. However, at the scale we operate, retiring a server is a little more complex than simply sorting components into the correct recycling bin. Every server must be securely decommissioned, and data-bearing components wiped. Then we must decide how to recover the most value from it. Can the complete system serve another purpose? Can processors, memory, and other components keep working elsewhere? Which materials can return to the supply chain?

Microsoft Circular Centers manage these decisions as part of our datacenter operations. At these facilities, our staff give decommissioned hardware a series of possible next lives. Complete systems can support Microsoft labs and training environments, help schools and universities teach technical skills, or be sold to qualified buyers for continued use. Components can become spare parts for other systems. Materials are recycled when reuse is no longer possible.

Our Circular Centers operate as a connected global network. With recent launches in Wales and Australia, eight centers are now in operation across North America, Europe, and Asia Pacific. The network continues to grow, with a new center planned for San Antonio, Texas.

We are also investing in technologies that improve the efficiency and scale of circular operations. Through automation initiatives with AI-powered capabilities, such as robotic disassembly and autonomous material handling, we are helping accelerate the recovery of components and materials.

Making the most of every resource

As demand for cloud and AI continues to grow, every part of the infrastructure lifecycle matters. We must get more compute from every rack and every watt, while getting more value from every server and component. Circular Centers help us do both. They make responsible recovery part of how we grow Azure, renew the foundational infrastructure, and build capacity for what customers need next.

Learn More

Explore Azure migration and modernization.

Learn more about Microsoft’s silicon to systems approach to Azure Infrastructure.

Learn how Microsoft Circular Centers are scaling cloud supply chain sustainability.

Read Microsoft’s 2026 Environmental Sustainability Report.

Microsoft Circular Centers

Helping deliver towards our commitment on Zero Waste

Take a virtual tour

The post Responsible infrastructure at hyperscale: Managing the full lifecycle of Azure hardware appeared first on Microsoft Azure Blog.
Quelle: Azure

Ship agents faster with expanded model choice, voice agents, and continuous optimization

The best model for your business will keep changing. Adopting it should move your business forward, not send your team back to rebuild the architecture around it. As models advance, organizations need the freedom to choose the right model for each workload, and an agent foundation that can evolve with those choices.

Microsoft Foundry provides that model—and harness—agnostic foundation for building and running agents. Teams can adopt better models as they emerge while preserving their investments in the enterprise systems, knowledge, tools, and controls that make agents useful to their business.

Adopting a newer model is only the starting point. Foundry helps teams continuously improve their agents, using production traces to evaluate and refine instructions, skills, tools, and model choice against quality, latency, and cost. That’s the hill-climbing approach: observe, evaluate, optimize, validate, and repeat—with people in control

Learn more about Microsoft Foundry

Today, we’re advancing that foundation to help teams build and continuously improve agents that deliver business value:

Choose from a broader range of models as frontier AI advances.

Build voice experiences and support long-running work on the same agent foundation.

Equip agents with knowledge and tools that help them discover and apply the right capabilities.

Turn production insights into continuous improvement across agent quality, latency, and cost.

These advances help teams spend less time assembling infrastructure and more time building.

One customer putting these capabilities together is Fashable, which uses Foundry’s models, agents, knowledge, and tools to help fashion brands turn trends and ideas into clothing designs, virtual try-on experiences, and campaign imagery. By connecting these workflows with the existing brands’ systems, Fashable reports cutting the time to develop new fashion products from months to weeks, with one customer reducing its budget for physical samples by 60%.

Choose and evaluate frontier AI models in Microsoft Foundry

Frontier AI is advancing rapidly across providers. New capabilities are arriving in days, not months, and this week captures that momentum: the full GPT-6 family from OpenAI, including GPT-6 Sol and GPT-6 Luna, and Claude Opus 5.5 from Anthropic are now available in Microsoft Foundry. What makes this moment important is not any single model. It is the pace and breadth of innovation happening across the frontier at the same time.

For developers and enterprises, model choice is no longer a decision you make at the start of a project. It is becoming a continuous advantage. The best model for a coding agent today may not be the best model for research, customer service, or high-volume inference, and the right answer will keep changing as new models arrive. Microsoft Foundry is built for that reality.

Foundry brings together models from leading AI providers in an open platform where teams can evaluate them against their own data and workloads, compare tradeoffs across quality, latency, and cost, and put the right models into production without rebuilding their AI strategy around a single provider. The arrival of the GPT-6 family and Claude Opus 5.5 expands that choice, giving teams more models to evaluate for their specific workloads alongside the broader portfolio already available in Foundry.

Build and operate voice agents with Foundry Agent Service

Voice is rapidly becoming a major way people interact with AI agents. Yet many voice experiences are still built by layering speech onto agents designed for text, requiring separate speech, orchestration, monitoring, and deployment components. With voice agents in Foundry Agent Service, voice becomes a native part of the agent foundation—built, deployed, observed, evaluated, and optimized through the same platform, API, and SDK as the rest of your Foundry agents.

Voice agents, now in public preview, are a new agent type supporting both prompt and hosted agents. Choose from industry-leading voice models— GPT Realtime, Azure Realtime, MAI and more—to balance latency, reasoning depth, and cost for each scenario, or bring your own. Models, tools, and deployment paths are designed for spoken interaction from the start: expressive voices, natural turn-taking, and graceful recovery when someone interrupts, across 80+ languages and 140+ locales. Teams that want a distinct identity can fine-tune custom speech for domain vocabulary, create a custom voice, or add a photo or video avatar for unique brand identity. Developers can reuse the tools, enterprise knowledge, and governance configured for their other Foundry agents, then deploy to web channels, Microsoft Teams, Teams Phone, and Twilio-based inbound and outbound telephony experiences.

The developer workflow extends across the tools teams already use:

AZD AI provides a repeatable, source-controlled workflow for configuring and deploying voice agents.

Foundry Toolkit in Visual Studio Code (coming soon) enables developers to create, run, speak with, and debug voice agents without leaving their development environment.

Voice-agent observability brings tracing, monitoring, transcripts, and voice-native evaluation to spoken interactions.

At Fashable, our mission is to make advanced AI accessible to brands and manufacturers of all sizes, regardless of their technical maturity. As we introduce new agentic capabilities, voice becomes an important part of that journey, enabling interactions that feel more natural, conversational, and accessible. People don’t think in prompts, they think in conversations, and using voice agents in Foundry with MAI-Voice helps bridge that gap.
—Orlando Ribas Fernandes, CEO, Fashable

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Build resilient hosted agents for long-running tasks

Real enterprise work does not always finish within a single request. A research task may span many sources, a report may require several tools and processing stages, and an agent may need to wait for external systems or human approval. Long-running resilience in Foundry Agent Service, now in public preview, lets the work of hosted agents continue after a request disconnects and recover when its hosting process stops unexpectedly.

Microsoft Agent Framework complements these capabilities with framework-level workflow state and checkpointing. When a resilient background workflow is interrupted, Agent Framework can resume from its latest durable checkpoint while Foundry preserves the hosted response.

Microsoft Agent Framework also adds Agent Channel for messaging integrations, AG-UI support for interactive applications, CodeAct and Hyperlight containers for isolated code execution, and episodic procedural memory to reuse successful task patterns. Read more about the latest updates in Microsoft Agent Framework.

To build and deploy these long-running agents, developers can use the Foundry Toolkit for Visual Studio Code, which supports hosted agents and Microsoft Agent Framework. The new Foundry dev pack simplifies onboarding by bringing the Foundry developer toolchain together in one installer.

Read more about the latest updates in Microsoft Agent Framework

Give agents only what they need, when they need it

Enterprise agents don’t need every document, tool, and procedure in every interaction. Loading everything upfront adds latency and cost, and can make behavior harder to predict. Agents should be able to find and use the resources they need as the work unfolds.

Toolboxes give agents on-demand access to tools and reusable procedures, with opt-in discovery and tool-level guardrails. Teams can make capabilities available without loading them into every call. Toolboxes are already generally available for hosted agents and are now expanding to prompt agents in public preview.

Tool search in Toolboxes, now generally available, lets agents find the right tool without loading the full catalog of tool definitions upfront. In an internal evaluation using a public benchmark of more than 44,000 tools and 7,000 queries, tool search reduced input-token consumption by more than 60% for a 50-tool toolbox and by over 97% for a 1,000-tool toolbox. These results are compared with a prompt-cached baseline that loads the full tool catalog upfront.

Agent-to-Agent (A2A), now generally available, lets agents call other agents, including other Foundry agents, through an open, standardized protocol. Teams can connect agents without building custom point-to-point integrations, while centralized credentials and policy enforcement help maintain security and governance as those systems grow.

Routines in Foundry Agent Service, now generally available, let teams run automated agents on a schedule, after a delay, or in response to an event. Teams no longer need to build their own schedulers, queues, and identity plumbing just to start an agent at the right time.

Together, these capabilities help teams reduce unnecessary context and custom infrastructure, with more control over what agents can access and when they run.

Turn production evidence into continuous improvement

Getting an agent into production is just the beginning. As real users put it to work, teams need to understand what’s driving quality, latency, and cost, and which changes will improve performance without introducing regressions. That has often meant piecing together traces, dashboards, and ad hoc tests. Foundry brings those steps into a continuous improvement loop, helping teams turn production insights into measurable improvements: observe, understand, evaluate, optimize, validate, and repeat.

Insights in Foundry, now in public preview, helps teams understand what is happening in production. It analyzes production traces to surface recurring issues and ones teams didn’t know to look for. For each issue, it gathers the supporting evidence, points to the likely cause, and recommends what to do next, so teams know where to focus before a small problem becomes a widespread one.

Rubric evaluator, generally available later this month, helps teams define what good looks like once they know what to fix. It turns an agent’s prompts, traces, and requirements into clear success criteria that can be measured consistently.

Synthetic and traces to dataset generation, generally available later this month, builds the evaluation data to match those criteria. Traces to dataset generation draws on real production traffic, so tests reflect the scenarios customers actually encounter, including the ones Foundry insights flagged.

Agent optimizer in Foundry Agent Service, generally available later this month, puts those evaluations, datasets, and constraints to work. It tests changes across instructions, tools, skills, and model choice, and shows which changes actually improve results before anything ships.

Together, these capabilities close the loop from production back to production. The improved agent returns to real traffic, where Foundry insights keep watching for what comes next, and each pass builds on the last so agents keep getting better the longer they run.

Agent optimizer in Foundry Agent Service turns evaluation insights into actionable datasets and optimization, reducing the manual effort of finding and fixing performance issues. That systematic loop aligns directly with NTT DATA’s AgentOps with Harness approach—a core element of our Smart AI Agent® concept—together moving agents from proof of concept to trusted production at scale.
—Takashi Okamoto, AI Technology Strategist, Global AI Office, NTT DATA Group Corporation

Govern AI agents with identity, lifecycle, and network controls

As agents take on more work, organizations need to manage them as enterprise assets, with identities, owners, policies, lifecycle controls, and evidence of what they did.

Foundry agents now honor governance operations originating in Microsoft Entra and Agent 365. Actions such as blocking, disabling, deleting, restoring, or assigning a new owner are enforced by the Foundry runtime rather than recorded only in a directory.

Network egress controls govern the outbound destinations a hosted agent can access. Teams can define allow and deny rules, modify outbound headers, or redirect requests before traffic leaves the Foundry-managed sandbox. An audit mode enables teams to evaluate policies before enforcement, with each decision emitted to Azure Application insights.

Coming soon in October, Microsoft Foundry is expanding its integration with Azure API Management with preview support for the new AI Gateway tier, helping organizations bring centralized governance into the Foundry developer experience. With this integration, organizations can adopt a hub-and-spoke approach where platform teams centrally govern model access through AI Gateway and make approved models available to developers through Admin Connected Models. Developers can then use these governed models across familiar Foundry experiences for model discovery, experimentation, agent development, and evaluation. Developers will also be able to connect to models across other resources or outside of Foundry directly from the playground. Existing Foundry integration with traditional Azure API Management tiers will remain supported.

Governing agents as enterprise assets also means proving they meet the organization’s safety and policy requirements. The new open-source run-assert-eval Skill brings that verification into a single workflow.

It runs three of Microsoft’s open-source tools in sequence:

Clarity surfaces risks developers may have missed.

ASSERT turns requirements into measurable evaluations.

Agent Control Specification (ACS) applies targeted runtime controls where an agent falls short.

The Skill then re-runs the evaluation to confirm the agent improved without restricting legitimate behavior, giving teams evidence that each policy is working as intended.

Get started with Microsoft Foundry

The latest capabilities in Microsoft Foundry give developers a flexible path from a simple prompt to a production agent, without requiring teams to assemble disconnected development tools, runtimes, gateways, schedulers, and governance controls.

Start building: Head to ai.azure.com to build your next agent in Microsoft Foundry. Choose a model, define the agent’s instructions, and connect the knowledge and tools it needs.

Learn with the community: Join Model Mondays and Foundry Fridays for demos and practical guidance. Next up: Agent Optimizer and insights in Foundry Session

Join us at Microsoft Ignite: Visit Microsoft Ignite to explore sessions on AI and agents and opportunities to connect with experts. 

Start building and optimizing in Microsoft Foundry today

Begin here

The post Ship agents faster with expanded model choice, voice agents, and continuous optimization appeared first on Microsoft Azure Blog.
Quelle: Azure

Enhancing Microsoft Azure Virtual Machine lifecycle

Azure continuously advances through new infrastructure generations, platform capabilities, services, and innovations that lead the industry in performance, security, reliability, and efficiency. Our Virtual Machine (VM) Lifecycle policy guides how we manage these transitions, giving Azure customers transparency, predictability, and guidance to plan for and execute technology changes with confidence. Our goal is to help every organization plan and navigate these modernization steps in line with Azure’s latest innovations and ongoing platform investments.

Learn more about virtual machines in Azure

To give you access to the most advanced cloud and AI infrastructure, deployed on the most sophisticated silicon and hardware, the Azure Virtual Machine lifecycle is built around three customer outcomes:

Transparency to easily understand the lifecycle status of Azure technologies, Microsoft’s level of ongoing investment, and the recommended path forward as new innovations become available.

Predictability to plan against defined lifecycle stages, understand what to expect at each stage, and advance notice as technologies progress through their lifecycle.

Guidance to help you modernize and adopt newer technologies rapidly and with confidence, along with AI-led tooling to accelerate workload transitions and recommendations.

This lifecycle provides a shared taxonomy, common stages, and consistent commitments for how we communicate technology evolution.

Azure General Purpose, Memory Optimized, Compute Optimized, and Storage Optimized VMs are the first VM families covered by this policy.

The lifecycle stages, spanning Current, Extended, End of Life, and Retired, are defined below, with dedicated Microsoft investment levels, regional and quota availability, applicable purchasing options, and pricing. The policy establishes clear lifecycle stages, expected customer experiences, and Microsoft’s commitments at each stage so customers can confidently plan, deploy, and modernize workloads over time.

Azure Virtual Machine Lifecycle PolicyStageDescriptionCurrentCustomer recommendation: Customers can confidently deploy new workloads, expand existing deployments, and optimize their Azure investments using VMs in the Current stage of the lifecycle. These VMs are specifically recommended for new deployments. Microsoft investment: During this stage, products receive standard maintenance and support.Availability: Current VM series are generally available to customers and have broad availability across Azure regions globally.Purchasing options and pricing: Pay-as-you-go pricing and Azure Savings Plans. Azure Reserved VM Instances may also be offered. ExtendedCustomer recommendation: Azure VMs in the Extended stage remain supported for existing workloads; however, customers should begin evaluating and planning a transition to VMs in the Current stage.Microsoft investment: During this stage, products continue to receive standard maintenance and support.Availability: VMs in the Extended stage remain generally available across multiple regions. However, VMs in this stage may be subject to quota restrictions. Purchasing options and pricing: Pay-as-you-go pricing and Azure Savings Plans. Azure Reserved VM Instances may also be offered. Changes to purchasing options and pricing are possible.End of LifeCustomer recommendation: VMs enter the End of Life stage when their retirement is formally announced. These VMs are based on older platform generations and do not benefit from the latest advancements in performance, security, reliability, and efficiency. Customers should actively plan and transition the workloads to VMs in the Current or Extended stages.Microsoft investment: During this stage, products continue to receive standard maintenance and support. In addition, Azure will provide notifications, migration tooling, and guidance to help customers move to newer versions.Availability: VMs in the End of Life stage remain available to existing customers in currently supported regions and within their existing quota allocations, subject to available capacity. New deployments and quota requests are subject to restriction. Microsoft will deploy new workload placement capabilities for VMs in the End of Life stage. While customers may experience minor workload performance variations in rare scenarios, existing Azure Virtual Machines SLAs will be met. These capabilities allow Microsoft to improve capacity availability for VMs in the End of Life stage as well as help support continued investments in modern hardware, new capabilities, and global scale.Purchasing options and pricing: Pay-as-you-go pricing and Azure Savings Plans remain available, while Reserved Instances may no longer be offered. When VMs enter the End of Life stage, customers should expect changes to pricing and availability.RetiredCustomer recommendation: Retired VMs are no longer available and can no longer be provisioned. Customer modernization should be complete, with workloads now deployed to VMs in the Current or Extended stages.Microsoft investment: VMs in a Retired series are no longer covered by a service level agreement (SLA) and are not eligible for Microsoft support.Availability: New VM creation is no longer available, and existing VMs will be deallocated.Purchasing options and pricing: VMs in a retired series are no longer available for purchase.

Note: Lifecycle details described in this policy represent general guidance and may be subject to product-specific, regional, or operational exceptions.

Platform experiences and tools to help simplify transitions

Microsoft is committed to helping you modernize your workload portfolio with the greatest possible speed, confidence, and simplicity. Azure Advisor and Azure Service Health give you clear insight into your subscriptions and resources—helping you discover upcoming VM retirements, identify affected resources, and track your modernization progress with recommended actions built in. Beyond this, Microsoft is investing in intelligent, AI-augmented tools designed to apply lifecycle guidance across your entire workload portfolio, further reducing friction as you modernize.

Microsoft is committed to helping you navigate these infrastructure transitions and take advantage of the next wave of innovation. As Azure continues to advance, our commitment is to make change predictable, transparent, and supported through clear lifecycle guidance, early visibility, and practical help to modernize with confidence. This policy is an important step toward making modernization easier to plan, simpler to execute, and more valuable for every Azure customer.

For more information about the Azure Virtual Machines lifecycle, visit the VM lifecycle overview page.

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The post Enhancing Microsoft Azure Virtual Machine lifecycle appeared first on Microsoft Azure Blog.
Quelle: Azure