Kategorie: Microsoft Azure

PostgreSQL on Azure supercharged for AI

We are almost a century removed from when a group of computer scientists at Dartmouth College coined the term “Artificial Intelligence.” In the 75-year span, AI has become common vernacular, making inroads from imagined to mainstream. Today, we see entire industries being disrupted and entire ecosystems revolutionized by AI. To keep up, the way developers work and the tools they use have had to evolve. Every developer now needs to be an AI developer, and every system—from compute and storage to the data layer—now needs to be AI ready.

The database reimagined

New AI applications require databases that are not only reliable, extensible, and secure, but also AI-ready. In parallel, the way developers build software is being reshaped by AI. 1Most developers—more than 80%—now use AI tools in their workflow. This has led to notable productivity gains and it’s changing expectations for developer experience.

PostgreSQL has emerged as a top choice among developers and is becoming the default starting point for many new applications and projects. Favored by developers for its reliability, extensibility, and rapid innovation, 2PostgreSQL is chosen by 78.6% of developers that are building AI and real-time applications.

PostgreSQL on Azure meets the moment

Selecting the right ecosystem is critical to support your AI and agentic aspirations, and we’ve made great strides in bolstering our PostgreSQL managed services to meet the needs of today’s developer. At Microsoft, we’ve embraced PostgreSQL not just as a product, but as a community. We’re proud to be one of the top contributors to the PostgreSQL open-source project, with more than 500 commits in the latest release. We are continuously innovating to make PostgreSQL the best database for building intelligent applications, and Azure the best place to run them.

Learn about Azure Database for PostgreSQL

The existing Azure Database for PostgreSQL continues to cater to lift-and-shift and new open-source workloads with improved performance and experience, while the new Azure HorizonDB, introduced at Ignite, targets the future by offering a PostgreSQL-compatible cloud service built for scale-out and ultra-low latency. Together, they position Azure to support developers building everything from small apps and agents to AI-powered, mission-critical systems, and anything in between.

A frictionless and intelligent developer experience

Building intelligent applications should feel intuitive, not intimidating. The Microsoft team has invested in making Azure Database for PostgreSQL a frictionless experience, especially for those building AI apps and agents. From provisioning to AI integration and scale, we’ve reimagined the developer experience to remove friction at every step.

Start in the IDE you love

The journey begins in Visual Studio Code—the leader in integrated development environments (IDEs) among developers—by a mile. With our PostgreSQL extension for Visual Studio Code, developers can now provision secure, fully managed PostgreSQL instances on Azure directly from the IDE. No portal hopping or manual setup. Just a few clicks, and your database is ready to go with built-in support for Entra ID authentication and Azure Monitor.

From there, GitHub Copilot becomes your intelligent assistant. It understands your PostgreSQL schema and helps you write, optimize, and debug SQL queries using natural language. Whether you’re joining tables, creating indexes, or exploring performance issues, Copilot is right there with you offering expert-level guidance to save time and improve performance.

Access in-database intelligence for smarter, faster apps

Once your database is live, you’re just a query away from infusing AI into your application. Azure Database for PostgreSQL now supports seamless integration with Microsoft Foundry, enabling developers to invoke pre-provisioned large language models (LLMs) in SQL. You can generate embeddings, classify text, or perform semantic search without leaving the database.

For applications that rely on relevance and speed, our DiskANN vector indexing delivers high-performance similarity search. Combined with semantic ranking, your queries return more accurate results, faster. This is ideal for powering intelligent agents, recommendations, and natural language interfaces.

Build intelligent agents with Microsoft Foundry

When you’re ready to build AI agents, Microsoft Foundry’s native PostgreSQL integration makes it easy. Using the new Model Context Protocol (MCP) server for PostgreSQL, developers can connect PostgreSQL directly to Foundry’s agent framework with a few clicks and permissions. This allows agents to reason over your data, invoke LLMs, and act on insights. And, of course, this is all backed by Azure’s enterprise-grade security and governance.

It’s a powerful combination: PostgreSQL’s structured data, Foundry’s orchestration, and Azure’s AI models working together to deliver intelligent, context-aware applications.

Leverage zero extract, transform, load (ETL) real-time analytics

Intelligent applications thrive on fresh insights. With Azure Database for PostgreSQL, you can mirror your operational data into Microsoft Fabric for real-time analytics without impacting performance. Alternatively, we’ve also enabled support for Parquet via the Azure Storage Extension, letting customers directly read from and write to Parquet files stored in Azure Storage from their Postgres databases, using SQL commands.

This means faster time to insight, fewer moving parts, and more time spent building.

Performance and scale that grows with you

All this intelligence is meaningless if the database isn’t secure and performant. As such, we’ve continued to innovate to unlock better performance and scale to meet the needs of even the most demanding, hypergrowth AI workloads. With PostgreSQL 18 now generally available on Azure, you get faster I/O, improved vacuuming, and smarter query planning. Our new V6 compute SKUs deliver higher throughput and lower latency, while Elastic Clusters enable horizontal scaling for multi-tenant and high-volume workloads.

Whether you’re building a startup MVP or scaling a global AI platform, Azure Database for PostgreSQL is ready to grow with you. Our customers have already been utilizing these new capabilities to build competitive advantage in industries from pharma to finance.

Real-world AI on Azure: How Nasdaq reinvented governance with PostgreSQL

When people think of Nasdaq, they picture trading floors and financial data moving at lightning speed. But behind the scenes, Nasdaq also manages board governance for thousands of organizations, including nearly half of the Fortune 500. At Ignite, Nasdaq shared how they modernized their Boardvantage platform using Azure Database for PostgreSQL and Microsoft Foundry.   Their goal: introduce AI to help directors navigate 500-page board packets and extract insights, without compromising security or compliance.The result? A governance platform that uses AI to summarize meeting minutes, flag anomalies, and surface relevant decisions while keeping each customer’s data isolated and protected.

Looking ahead: Azure HorizonDB and the future of intelligent apps

At Ignite, we also introduced Azure HorizonDB, a new, fully managed PostgreSQL-compatible service built for AI-native workloads. With scale-out compute, sub-millisecond latency, and built-in AI features, Azure HorizonDB represents the future of cloud databases. While the service is currently in private preview, it’s a glimpse of what’s coming.

Explore Azure HorizonDB

The future is open, intelligent, and built on Azure

At Microsoft, our mission is to offer customers databases equipped for next-generation development, whether they be SQL, NoSQL, relational, or open source. As PostgreSQL continues to stand out as a platform for innovation, it’s now primed for intelligent applications and agents due to Microsoft’s continued support and service enhancements. Whether you’re a startup building your first AI feature or an enterprise modernizing mission-critical systems, Azure gives you the tools to move faster, build smarter, and scale confidently.

The future of intelligent applications will be written in Postgres, and we’re thrilled to build it together with you on Azure.

Start today

Try the PostgreSQL extension for VS Code

Learn how to build AI agents with Azure Database for PostgreSQL

1Most developers—more than 80%—now use AI tools in their workflow.

2PostgreSQL is chosen by 78.6% of developers that are building AI and real-time applications.
The post PostgreSQL on Azure supercharged for AI appeared first on Microsoft Azure Blog.
Quelle: Azure

Maia 200: The AI accelerator built for inference

Today, we’re proud to introduce Maia 200, a breakthrough inference accelerator engineered to dramatically improve the economics of AI token generation. Maia 200 is an AI inference powerhouse: an accelerator built on TSMC’s 3nm process with native FP8/FP4 tensor cores, a redesigned memory system with 216GB HBM3e at 7 TB/s and 272MB of on-chip SRAM, plus data movement engines that keep massive models fed, fast and highly utilized. This makes Maia 200 the most performant, first-party silicon from any hyperscaler, with three times the FP4 performance of the third generation Amazon Trainium, and FP8 performance above Google’s seventh generation TPU. Maia 200 is also the most efficient inference system Microsoft has ever deployed, with 30% better performance per dollar than the latest generation hardware in our fleet today.

Maia 200 is part of our heterogenous AI infrastructure and will serve multiple models, including the latest GPT-5.2 models from OpenAI, bringing performance per dollar advantage to Microsoft Foundry and Microsoft 365 Copilot. The Microsoft Superintelligence team will use Maia 200 for synthetic data generation and reinforcement learning to improve next-generation in-house models. For synthetic data pipeline use cases, Maia 200’s unique design helps accelerate the rate at which high-quality, domain-specific data can be generated and filtered, feeding downstream training with fresher, more targeted signals.

Maia 200 is deployed in our US Central datacenter region near Des Moines, Iowa, with the US West 3 datacenter region near Phoenix, Arizona, coming next and future regions to follow. Maia 200 integrates seamlessly with Azure, and we are previewing the Maia SDK with a complete set of tools to build and optimize models for Maia 200. It includes a full set of capabilities, including PyTorch integration, a Triton compiler and optimized kernel library, and access to Maia’s low-level programming language. This gives developers fine-grained control when needed while enabling easy model porting across heterogeneous hardware accelerators.

YouTube Video

Engineered for AI inferenceFabricated on TSMC’s cutting-edge 3-nanometer process, each Maia 200 chip contains over 140 billion transistors and is tailored for large-scale AI workloads while also delivering efficient performance per dollar. On both fronts, Maia 200 is built to excel. It is designed for the latest models using low-precision compute, with each Maia 200 chip delivering over 10 petaFLOPS in 4-bit precision (FP4) and over 5 petaFLOPS of 8-bit (FP8) performance, all within a 750W SoC TDP envelope. In practical terms, Maia 200 can effortlessly run today’s largest models, with plenty of headroom for even bigger models in the future.

A close-up of the Maia 200 AI accelerator chip.

Crucially, FLOPS aren’t the only ingredient for faster AI. Feeding data is equally important. Maia 200 attacks this bottleneck with a redesigned memory subsystem. The Maia 200 memory subsystem is centered on narrow-precision datatypes, a specialized DMA engine, on-die SRAM and a specialized NoC fabric for high‑bandwidth data movement, increasing token throughput.

A table with the title “Industry-leading capability” shows peak specifications for Azure Maia 200, AWS Trainium 3 and Google TPU v7.

Optimized AI systemsAt the systems level, Maia 200 introduces a novel, two-tier scale-up network design built on standard Ethernet. A custom transport layer and tightly integrated NIC unlocks performance, strong reliability and significant cost advantages without relying on proprietary fabrics.

Each accelerator exposes:

2.8 TB/s of bidirectional, dedicated scaleup bandwidthPredictable, high-performance collective operations across clusters of up to 6,144 acceleratorsThis architecture delivers scalable performance for dense inference clusters while reducing power usage and overall TCO across Azure’s global fleet.

Within each tray, four Maia accelerators are fully connected with direct, non‑switched links, keeping high‑bandwidth communication local for optimal inference efficiency. The same communication protocols are used for intra-rack and inter-rack networking using the Maia AI transport protocol, enabling seamless scaling across nodes, racks and clusters of accelerators with minimal network hops. This unified fabric simplifies programming, improves workload flexibility and reduces stranded capacity while maintaining consistent performance and cost efficiency at cloud scale.

A top-down view of the Maia 200 server blade.

A cloud-native development approachA core principle of Microsoft’s silicon development programs is to validate as much of the end-to-end system as possible ahead of final silicon availability.

A sophisticated pre-silicon environment guided the Maia 200 architecture from its earliest stages, modeling the computation and communication patterns of LLMs with high fidelity. This early co-development environment enabled us to optimize silicon, networking and system software as a unified whole, long before first silicon.

We also designed Maia 200 for fast, seamless availability in the datacenter from the beginning, building out early validation of some of the most complex system elements, including the backend network and our second-generation, closed loop, liquid cooling Heat Exchanger Unit. Native integration with the Azure control plane delivers security, telemetry, diagnostics and management capabilities at both the chip and rack levels, maximizing reliability and uptime for production-critical AI workloads.

As a result of these investments, AI models were running on Maia 200 silicon within days of first packaged part arrival. Time from first silicon to first datacenter rack deployment was reduced to less than half that of comparable AI infrastructure programs. And this end-to-end approach, from chip to software to datacenter, translates directly into higher utilization, faster time to production and sustained improvements in performance per dollar and per watt at cloud scale.

A view of the Maia 200 rack and the HXU cooling unit.

Sign up for the Maia SDK previewThe era of large-scale AI is just beginning, and infrastructure will define what’s possible. Our Maia AI accelerator program is designed to be multi-generational. As we deploy Maia 200 across our global infrastructure, we are already designing for future generations and expect each generation will continually set new benchmarks for what’s possible and deliver ever better performance and efficiency for the most important AI workloads.

Today, we’re inviting developers, AI startups and academics to begin exploring early model and workload optimization with the new Maia 200 software development kit (SDK). The SDK includes a Triton Compiler, support for PyTorch, low-level programming in NPL and a Maia simulator and cost calculator to optimize for efficiencies earlier in the code lifecycle. Sign up for the preview here.

Get more photos, video and resources on our Maia 200 site and read more details.

Scott Guthrie is responsible for hyperscale cloud computing solutions and services including Azure, Microsoft’s cloud computing platform, generative AI solutions, data platforms and information and cybersecurity. These platforms and services help organizations worldwide solve urgent challenges and drive long-term transformation.
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Quelle: Azure

Microsoft named a Leader in IDC MarketScape for Unified AI Governance Platforms

As organizations rapidly embrace generative and agentic AI, ensuring robust, unified governance has never been more critical. That’s why Microsoft is honored to be named a Leader in the 2025-2026 IDC MarketScape for Worldwide Unified AI Governance Platforms (Vendor Assessment (#US53514825, December 2025). We believe this recognition highlights our commitment to making AI innovation safe, responsible, and enterprise-ready—so you can move fast without compromising trust or compliance.

Read the IDC MarketScape for Unified AI Governance Platforms reportA graphic showing Microsoft’s position in the Leaders section of the IDC report.Figure 1. IDC MarketScape vendor analysis model is designed to provide an overview of the competitive fitness of technology and suppliers in a given market. The research methodology utilizes a rigorous scoring methodology based on both qualitative and quantitative criteria that results in a single graphical illustration of each supplier’s position within a given market. The Capabilities score measures supplier product, go-to-market and business execution in the short term. The Strategy score measures alignment of supplier strategies with customer requirements in a three- to five-year timeframe. Supplier market share is represented by the size of the icons.The urgency for a unified AI governance strategy is being driven by stricter regulatory demands, the sheer complexity of managing AI systems across multiple AI platforms and multicloud and hybrid environments, and leadership concerns for risk related to negative brand impact. Centralized, end-to-end governance platforms help organizations reduce compliance bottlenecks, lower operational risks, and turn governance into a strategic driver for responsible AI innovation. In today’s landscape, unified AI governance is not just a compliance obligation—it is critical infrastructure for trust, transparency, and sustainable business transformation.

Our own approach to AI is anchored to Microsoft’s Responsible AI standard, backed by a dedicated Office of Responsible AI. Drawing from our internal experience in building, securing, and governing AI systems, we translate these learnings directly into our AI management tools and security platform. As a result, customers benefit from features such as transparency notes, fairness analysis, explainability tools, safety guardrails, regulatory compliance assessments, agent identity, data security, vulnerability identification, and protection against cyberthreats like prompt-injection attacks. These tools enable them to develop, secure, and govern AI that aligns with ethical principles and is built to help support compliance with regulatory requirements. By integrating these capabilities, we empower organizations to make ethical decisions and safeguard their business processes throughout the entire AI lifecycle.

Microsoft’s AI Governance capabilities aim to provide integrated and centralized control for observability, management, and security across IT, developer, and security teams, ensuring integrated governance within their existing tools. Microsoft Foundry acts as our main control point for model development, evaluation, deployment, and monitoring, featuring a curated model catalog, machine learning oeprations, robust evaluation, and embedded content safety guardrails. Microsoft Agent 365, which was not yet available at the time of the IDC publication, provides a centralized control plane for IT, helping teams confidently deploy, manage, and secure their agentic AI published through Microsoft 365 Copilot, Microsoft Copilot Studio, and Microsoft Foundry.

Deeply embedded security systems are integral to Microsoft’s AI governance solution. Integrations with Microsoft Purview provide real-time data security, compliance, and governance tools, while Microsoft Entra provides agent identity and controls to manage agent sprawl and prevent unauthorized access to confidential resources. Microsoft Defender offers AI-specific posture management, threat detection, and runtime protection. Microsoft Purview Compliance Manager automates adherence to more than 100 regulatory frameworks. Granular audit logging and automated documentation bolster regulatory and forensic capabilities, enabling organizations in regulated industries to innovate with AI while maintaining oversight, secure collaboration, and consistent policy enforcement.

Guidance for security and governance leaders and CISOsTo empower organizations in advancing their AI transformation initiatives, it is crucial to focus on the following priorities for establishing a secure, well-governed, and scalable AI framework. The guidance below provides Microsoft’s recommendations for fulfilling these best practices:

CISO guidance What it means How Microsoft deliversAdopt a unified, end‑to‑end governance platform Establish a comprehensive, integrated governance system covering traditional machine learning, generative AI, and agentic AI. Ensure unified oversight from development through deployment and monitoring. Microsoft enables observability and governance at every layer across IT, developer, and security teams to provide an integrated and cohesive governance platform that enables teams to play their part from within the tools they use. Microsoft Foundry acts as the developer control plane, connecting model development, evaluation, security controls, and continuous monitoring. Microsoft Agent 365 is the control plane for IT, enabling discovery, security, deployment, and observability for agentic AI in the enterprise. Microsoft Purview, Entra, and Defender integrate to deliver consistent full-stack governance across data, identity, threat protection, and compliance.Industry‑leading responsible AI infrastructure Implement responsible AI practices as a foundational part of engineering and operations, with transparency and fairness built in. Microsoft embeds its Responsible AI Standards into our engineering processes, supported by the Office of Responsible AI. Automatic generation of model cards and built-in fairness mechanisms set Microsoft apart as a strategic differentiator, pairing technical controls with mature governance processes. Microsoft’s Responsible AI Transparency Report provides visibility to how we develop and deploy AI models and systems responsibility and provides a model for customers to emulate our best practices.Advanced security and real‑time protection Provide robust, real-time defense against emerging AI security threats, especially for regulated industries. Microsoft’s platform features real-time jailbreak detection, encrypted agent-to-agent communication, tamper-evident audit logs for model and agent actions, and deep integration with Defender to provide AI-specific threat detection, security posture management, and automated incident response capabilities. These capabilities are especially critical for regulated sectors.Automated compliance at scale Automate compliance processes, enable policy enforcement throughout the AI lifecycle, and support audit readiness across hybrid and multicloud environments. Microsoft Purview streamlines compliance adherence for regulatory requirements and provides comprehensive support for hybrid and multicloud deployments—giving customers repeatable and auditable governance processes.We believe we are differentiated in the AI governance space by delivering a unified, end-to-end platform that embeds responsible AI principles and robust security at every layer—from agents and applications to underlying infrastructure. Through native integration of Microsoft Foundry, Microsoft Agent 365, Purview, Entra, and Defender, organizations benefit from centralized oversight and observability across the layers of the organization with consistent protection and operationalized compliance across the AI lifecycle. Our comprehensive approach removes disparate and disconnected tooling, enabling organizations to build trustworthy, transparent, and secure AI solutions that can start secure and stay secure. We believe this approach uniquely differentiates Microsoft as a leader in operationalizing responsible, secure, and auditable AI at scale.

Strengthen your security strategy with Microsoft AI governance solutionsAgentic and generative AI are reshaping business processes, creating a new frontier for security and governance. Organizations that act early and prioritize governance best practices—unified governance platforms, build-in responsible AI tooling, and integrated security—will be best positioned to innovate confidently and maintain trust.

Microsoft approaches AI governance with a commitment to embedding responsible practices and robust security at every layer of the AI ecosystem. Our AI governance and security solutions empower customers with built-in transparency, fairness, and compliance tools throughout engineering and operations. We believe this approach allows organizations to benefit from centralized oversight, enforce policies consistently across the entire AI lifecycle, and achieve audit readiness—even in the rapidly changing landscape of generative and agentic AI.

Explore moreRead the IDC MarketScape excerpt.Learn more about AI Security, Governance and Compliance.Read our latest Security for AI blog to learn more about our latest capabilitiesTo learn more about Microsoft Security solutions, visit our website. Bookmark the Security blog to keep up with our expert coverage on security matters. Also, follow us on LinkedIn (Microsoft Security) and X (@MSFTSecurity) for the latest news and updates on cybersecurity.
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Quelle: Azure

Bridging the gap between AI and medicine: Claude in Microsoft Foundry advances capabilities for healthcare and life sciences customers

Healthcare and life sciences organizations are navigating an era of unprecedented complexity. Administrative burden continues to rise, clinical workflows remain fragmented, and scientific discovery is advancing faster than traditional systems can support. At the same time, trust, safety, and regulatory compliance remain non-negotiable.

From clearing prior authorization backlogs to accelerating clinical research and regulatory submissions, organizations need AI that does more than generate text. They need AI that understands medical and scientific complexity, reasons across multi-step workflows, and can be deployed responsibly at enterprise scale.

Today, we’re excited to announce Claude for Healthcare and Life Sciences, now available in Microsoft Foundry bringing advanced reasoning, agentic workflows, and life sciences–tuned capabilities to some of the industry’s most demanding real-world use cases. Built on Azure’s secure, enterprise-grade foundation, Foundry ensures these capabilities scale responsibly while integrating with familiar Azure services for data, compliance, and workflow automation.

See what Claude Sonnet on Microsoft Foundry can do for your business

From general intelligence to domain expertise

Claude for Healthcare A complementary set of tools and resources that enable healthcare providers, payers, and organizations to use Claude for medical and operational workflows, while meeting the highest standards for trust, privacy, and compliance.

Claude for Life Sciences New components that accelerate every stage of the research and development (R&D) lifecycle connecting Claude to more scientific platforms and enabling it to generate more consistent, high-quality experimental and clinical protocols.

Together, these capabilities build on major recent advances in Claude’s general intelligence bringing domain-aware AI into the workflows that matter most.

Built for regulated, real-world workflows

Claude for Healthcare and Life Sciences enables organizations to deploy vertical-specific AI agents tailored to healthcare and life sciences use cases. These agents combine:

Advanced model capabilities optimized for healthcare and scientific reasoning.

Enterprise-grade deployment paths aligned to industry requirements.

Domain-specific connectors, model context protocols (MCPs), and skills to complete specialized tasks.

All within the trusted, unified Microsoft Foundry platform.

Transforming healthcare from insight to action

Healthcare teams are often constrained by administrative burden, fragmented systems, and time-intensive workflows. Claude on Microsoft Foundry helps address these challenges by supporting use cases such as:

Prior authorization: Streamlining documentation review and decision support.

Insurance claims appeal processing: Accelerating appeals with structured reasoning and evidence synthesis.

Care coordination and patient message triage: Helping clinicians prioritize and respond more effectively.

Why it matters

Trusted: Deployed on HIPAA-ready infrastructure through Claude for Enterprise.

Powerful: Frontier-level reasoning across clinical, operational, and coding-related tasks.

Tailored: Purpose-built for healthcare workflows with healthcare-specific MCPs.

Committed: Designed for long-term evolution alongside healthcare organizations.

Accelerating life sciences from discovery to translation

In life sciences, speed and scientific rigor are critical, whether in early discovery or regulatory submission. Claude for Life Sciences supports end-to-end workflows across research, development, and operations.

Key life sciences use cases

Preclinical R&D acceleration

Bioinformatics analysis

Protocol and experimental design

Literature synthesis and hypothesis generation

Clinical trial operations and data management

Regulatory affairs and submission preparation

Life sciences–tuned capabilities

Advanced research and protocol design agents

Code interpreter workflows for bioinformatics

Models trained to support:

Experimental protocol design

Next-hypothesis generation

Plasmid and molecular design tasks

Why it matters

Trusted: Life sciences–specific capabilities built with biosafety guardrails.

Powerful: Frontier AI for bioinformatics, experimental design, and synthesis.

Tailored: Deep integrations with scientific databases and lab platforms.

Committed: Co-developed alongside pharma and research leaders.

Powered by the latest advances in Claude intelligence

These domain-specific capabilities build on major improvements in Claude’s underlying models. According to Anthropic, when assessed on detailed simulations of real-world medical and scientific tasks, Claude Opus 4.5 substantially outperforms earlier releases across benchmarks such as:

PubMed-based medical question answering

Clinical reasoning simulations

Agent-based medical task benchmarks

Combined with ongoing investments in safety, low hallucination rates, and responsible AI, these advances make Claude dramatically more useful for real-world healthcare and life sciences workflows including prior authorization, care coordination, and regulatory submissions.

One platform. Many models. Built for trust.

With Microsoft Foundry, customers can choose from a growing catalog of industry-leading models—including Claude—while benefiting from a unified platform for governance, observability, deployment, and compliance.

Claude for Healthcare and Life Sciences adds another powerful option for organizations that need:

Domain-aware reasoning

Enterprise-grade controls

Flexible deployment across regulated environments

Get started

Claude for Healthcare and Life Sciences is available today in Microsoft Foundry. To learn more, explore the model catalog or connect with your Microsoft account team to understand how Claude can support your healthcare or life sciences workloads.

Explore Claude Sonnet on Microsoft Foundry

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Quelle: Azure

Microsoft’s strategic AI datacenter planning enables seamless, large-scale NVIDIA Rubin deployments

CES 2026 showcases the arrival of the NVIDIA Rubin platform, along with Azure’s proven readiness for deployment. Microsoft’s long-range datacenter strategy was engineered for moments exactly like this, where NVIDIA’s next-generation systems slot directly into infrastructure that has anticipated their power, thermal, memory, and networking requirements years ahead of the industry. Our long-term collaboration with NVIDIA ensures Rubin fits directly into Azure’s forward platform design.

Learn more about Azure AI infrastructure

Building with purpose for the future

Azure’s AI datacenters are engineered for the future of accelerated computing. That enables seamless integration of NVIDIA Vera Rubin NVL72 racks across Azure’s largest next-gen AI superfactories from current Fairwater sites in Wisconsin and Atlanta to future locations.

The newest NVIDIA AI infrastructure requires significant upgrades in power, cooling, and performance optimization; however, Azure’s experience with our Fairwater sites and multiple upgrade cycles over the years demonstrates an ability to flexibly enhance and expand AI infrastructure in step with advancements in technology.

Azure’s proven experience delivering scale and performance

Microsoft has years of market-proven experience in designing and deploying scalable AI infrastructure that evolves with every major advancement of AI technology. In lockstep with each successive generation of NVIDIA’s accelerated compute infrastructure, Microsoft rapidly integrates NVIDIA’s innovations and delivers them at scale. Our early, large-scale deployments of NVIDIA Ampere and Hopper GPUs, connected via NVIDIA Quantum-2 InfiniBand networking, were instrumental in bringing models like GPT-3.5 to life, while other clusters set supercomputing performance records, demonstrating we can bring next-generation systems online faster and with higher real-world performance than the rest of the industry.

We unveiled the first and largest implementations of both NVIDIA GB200 NVL72 and NVIDIA GB300 NVL72 platforms, architected as racks into single supercomputers which train AI models dramatically faster, helping Azure remain a top choice for customers seeking advanced AI capabilities.

Azure’s systems approach

Azure is engineered for compute, networking, storage, software, and infrastructure all working together as one integrated platform. This is how Microsoft builds a durable advantage into Azure and delivers cost and performance breakthroughs that compound over time.

Maximizing GPU utilization requires optimization across every layer. In addition to Azure being able to adopt NVIDIA’s new accelerated compute platforms early, Azure advantages come from the surrounding platform as well: high-throughput Blob storage, proximity placement and region-scale design shaped by real production patterns, and orchestration layers like CycleCloud and AKS tuned for low-overhead scheduling at massive cluster scale.

Azure Boost and other offload engines clear IO, network, and storage bottlenecks so models scale smoothly. Faster storage feeds larger clusters, stronger networking sustains them, and optimized orchestration keeps end-to-end performance steady. First party innovations reinforce the loop: liquid cooling Heat Exchanger Units maintain tight thermals, Azure hardware security module (HSM) silicon offloads security work, and Azure Cobalt delivers exceptional performance and efficiency for general-purpose compute and AI-adjacent tasks. Together, these integrations ensure the entire system scales efficiently, so GPU investments deliver maximum value.

This systems approach is what makes Azure ready for the Rubin platform. We are delivering new systems and establishing an end-to-end platform already shaped by the requirements Rubin brings.

Operating the NVIDIA Rubin platform

NVIDIA Vera Rubin Superchips will deliver 50 PF NVFP4 inference performance per chip and 3.6 EF NVFP4 per rack, a five times jump over NVIDIA GB200 NVL72 rack systems.Azure has already incorporated the core architectural assumptions Rubin requires:

NVIDIA NVLink evolution: The sixth-generation NVIDIA NVLink fabric expected in Vera Rubin NVL72 systems reaches ~260 TB/s of scale-up bandwidth, and Azure’s rack architecture has already been redesigned to operate with those bandwidth and topology advantages.

High-performance scale-out networking: The Rubin AI infrastructure relies on ultra-fast NVIDIA ConnectX-9 1,600 Gb/s networking, delivered by Azure’s network infrastructure, which has been purpose-built to support large-scale AI workloads.

HBM4/HBM4e thermal and density planning: The Rubin memory stack demands tighter thermal windows and higher rack densities; Azure’s cooling, power envelopes, and rack geometries have already been upgraded to handle the same constraints.

SOCAMM2 driven memory expansion: Rubin Superchips use a new memory expansion architecture; Azure’s platform has already integrated and validated similar memory extension behaviors to keep models fed at scale.

Reticle sized GPU scaling and multi-die packaging: Rubin moves to massively larger GPU footprints and multi-die layouts. Azure’s supply chain, mechanical design, and orchestration layers have been pre-tuned for these physical and logical scaling characteristics.

Azure’s approach in designing for next generation accelerated compute platforms like Rubin has been proven over several years, including significant milestones:

Operated the world’s largest commercial InfiniBand deployments across multiple GPU generations.

Built reliability layers and congestion management techniques that unlock higher cluster utilization and larger job sizes than competitors, reflected in our ability to publish industry leading large-scale benchmarks. (E.g., multi-rack MLPerf runs competitors have never replicated.)

AI datacenters co-designed with Grace Blackwell and Vera Rubin from the ground up to maximize performance and performance per dollar at the cluster level.

Design principles that differentiate Azure

Pod exchange architecture: To enable fast servicing, Azure’s GPU server trays are designed to be quickly swappable without requiring extensive rewiring, improving uptime.

Cooling abstraction layer: Rubin’s multi-die, high bandwidth components require sophisticated thermal headroom that Fairwater already accommodates, avoiding expensive retrofit cycles.

Next gen power design: Vera Rubin NVL72 demand increasing watt density; Azure’s multi-year power redesign (liquid cooling loop revisions, CDU scaling, and high amp busways) ensures immediate deployability.

AI superfactory modularity: Microsoft, unlike other hyperscalers, builds regional supercomputers rather than singular megasites, enabling more predictable global rollout of new SKUs.

How co-design leads to user benefits

The NVIDIA Rubin platform marks a major step forward in accelerated computing, and Azure’s AI datacenters and superfactories are already engineered to take full advantage. Years of co-design with NVIDIA across interconnects, memory systems, thermals, packaging, and rack scale architecture means Rubin integrates directly into Azure’s platform without rework. Rubin’s core assumptions are already reflected in our networking, power, cooling, orchestration, and pod exchange design principles. This alignment gives customers immediate benefits with faster deployment, faster scaling, and faster impact as they build the next era of large-scale AI.
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Quelle: Azure

Azure updates for partners: December 2025

At Microsoft Ignite 2025, we explored what it means for organizations to move into the era of Frontier transformation. This shift is focused on embedding AI across every part of the business to improve decision-making, increase speed, and create new value. Organizations leading in AI make it foundational. They rethink processes and integrate new technologies from the start to improve efficiency.

For partners, this move toward Frontier represents a significant opportunity to lead customers into this new era. By building AI-powered solutions, connecting data for intelligent insights, and deploying Microsoft Azure’s cloud-ready platforms, partners can deliver value faster and scale confidently through the Microsoft ecosystem.

Microsoft Ignite came with a significant number of announcements, so I’ve gathered the Azure updates that matter most for partners. These are the capabilities that can strengthen your ability to deliver intelligent solutions, drive operational efficiency, and differentiate your product or service in the market. You can also explore how partners are turning momentum into action, access highlights, and grab practical guidance from my Microsoft Ignite session.

Azure Copilot: Now in private previewAzure Copilot introduces specialized agents to the Azure portal, PowerShell, and CLI. Powered by Azure Resource Manager (ARM)-driven scenarios and advanced AI models from Microsoft and partners, Azure Copilot streamlines migration, assessment, and modernization activities with data-driven insights, guided workflows, and improved governance across customer environments. For partners, this creates a unified way to deliver intelligent automation for cloud workloads, accelerate modernization projects, reduce operational overhead, and strengthen governance through integrated agentic workflows across Azure and GitHub Copilot.

For more information, check out these additional resources:

Blog: Ushering in the Era of Agentic Cloud Operations with Azure CopilotMicrosoft Ignite session: Agentic AI Tools for Partner-Led Migration and Modernization SuccessMicrosoft Ignite session: Partners: Accelerate Secure Migrations and Innovate in the Era of AI

Foundry Control Plane: Now in public previewMicrosoft Foundry Control Plane extends Agent 365 by bringing unified visibility, security, and control to AI agents operating across the Microsoft Cloud. It centralizes policy management, lifecycle governance, and observability, offering a consistent way to manage agent behavior and performance. By providing enterprise-grade governance and security capabilities that support safe, scalable, and efficient agent management for customers across varied environments, Control Plane empowers confident deployment and operation of AI-powered solutions.

For more information, review these additional resources:

Microsoft Learn: What is the Microsoft Foundry Control Plane?Microsoft Ignite session: Build Partner Advantage: Drive Key AI Use-Cases with Azure Tech Stack

Foundry IQ: Now in public previewFoundry IQ provides a unified endpoint for agent knowledge, automating source routing and retrieval workflows through Azure AI Search. It equips agents to work with enterprise content securely and with greater contextual grounding by connecting a unified knowledge base to multiple data sources. For partners, this creates a streamlined way to build retrieval augmented generation (RAG) solutions, link agents to customer-specific knowledge sources, and deliver consistent, context-rich capabilities that empower organizations to unlock more value from their data.

Read our blog to learn more: Foundry IQ: Unlocking ubiquitous knowledge for agents

Fabric IQ: Now in public previewMicrosoft Fabric IQ offers a live, unified view of enterprise data and AI agents, organizing information by business concepts and using OneLake to support real-time analytics across hybrid and multicloud environments. For partners, Fabric IQ creates a foundation for building intelligent, context-aware solutions that align to business processes, accelerate analytics performance, and strengthen governance to improve reliability and efficiency across customer deployments.

For more information, check out these additional resources:

Blog: From Data Platform to Intelligence Platform: Introducing Microsoft Fabric IQMicrosoft Ignite session: Microsoft Fabric IQ: Turning unified data into unified intelligenceMicrosoft Ignite session: How Microsoft’s data platform is creating value for partners

Microsoft Agent Factory: Now availableMicrosoft Agent Factory is a new program designed for organizations that want to move from experimentation to execution faster. At the heart of this program is the Microsoft Agent Pre-Purchase Plan (P3), which streamlines procurement and reduces complexity. With P3, partners can offer their customers access to 32 Microsoft services through one flexible pool of funds, eliminating the need to manage multiple contracts or choose between platforms. This single metered plan not only reduces upfront licensing and provisioning but also supports greater predictability for organizations investing in AI innovation. Eligible organizations can also tap into hands-on support from top AI Forward Deployed Engineers (FDEs) and access tailored, role-based training to boost AI fluency across teams. Together, they unlock new opportunities for growth and innovation while encouraging customers to confidently embrace the future of AI.

Read our blog to learn more: Accelerate innovation with Microsoft Agent Factory

Microsoft Foundry: Anthropic Claude models are now availableMicrosoft Foundry now offers Anthropic Claude models that support advanced reasoning for research, coding, and agentic workflows, all within the Microsoft unified governance and observability framework. For partners, this expands choice across model capabilities to develop multistep agents using the right model per task while maintaining governance and deployment consistency across Azure, Foundry, and Microsoft 365 Copilot environments.

Read our blog to learn more: Introducing Anthropic’s Claude models in Microsoft Foundry: Bringing Frontier intelligence to Azure

Resale enabled offers: Now available through Microsoft MarketplaceResale enabled offers are now available in nearly all Marketplace-supported regions, allowing software companies to work with channel partners to manage listings and expand reach. For partners, this creates new channel-led sales opportunities by making it easier to promote and manage listings on behalf of publishers and reach more customers globally without adding operational complexity.

For more information, check out these resources:

Marketplace: Cloud solutions, AI apps, and agentsBlog: The Microsoft Marketplace opportunity for channel ecosystemMicrosoft Ignite session: Executing on the channel-led marketplace opportunity for partnersMicrosoft Ignite session: Marketplace success for partners—from SMB to enterpriseMicrosoft Ignite session: Partner: Benefits for Accelerating Software Company Success

Azure HorizonDB for PostgreSQL: Now in private previewAzure HorizonDB is a new PostgreSQL cloud database for mission-critical applications and modern AI workloads, offering auto-scaling storage, rapid compute scale out, advanced vector indexing, and integration with the Microsoft AI and analytics ecosystem. For partners, HorizonDB supports the development of intelligent and resilient applications, modernization of legacy systems, and creation of high-performance data platforms designed for security, scale, and future AI workloads.

Check out these additional resources:

Blog: Announcing Azure HorizonDBPreview sign-up: Apply for the preview hereMicrosoft Ignite session: Azure HorizonDB: Deep Dive into a New Enterprise-Scale PostgreSQL

Microsoft Agent 365: The control plane for AI agentsAgent 365 extends the Microsoft user management infrastructure to AI agents, empowering organizations to govern agents across Microsoft 365, Azure, and Foundry. Available in the Microsoft 365 admin center with the Frontier program, it combines capabilities from Microsoft 365 Defender, Entra, Purview, and Microsoft 365 for unified security, productivity, and management. For partners, this creates a consistent approach to deploying, securing, and managing fleets of AI agents across customer environments with streamlined governance and operational clarity.

Read our blog to learn more: Microsoft Agent 365: The control plane for AI agents

Looking forwardMicrosoft Ignite is about more than product updates; it’s a time to celebrate what we can achieve together as partners. Continue your journey and explore the Cloud & AI Platforms partner sessions at Microsoft Ignite and read the Azure at Microsoft Ignite 2025: All the intelligent cloud news explained blog post for more product updates.

Stay connected with us. Follow Microsoft Partner on LinkedIn, join the conversation in our Partner News Community, and explore the Microsoft partner site to keep your momentum going.

For details on recent announcements, please read the “What’s new in Azure for Partners” newsletter on the Microsoft Community Hub and follow the tag “Azure News” to stay updated.

November update: What’s new in Azure for Partners | Microsoft Community HubOctober update: What’s new in Azure for Partners | Microsoft Community Hub
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Quelle: Azure