Kategorie: Microsoft Azure

AI for nuclear energy: Powering an intelligent, resilient future

The world is racing to meet a historic surge in power demand with an infrastructure pipeline built for the analog age. Driven by the exponential expansion of digital technologies and the reindustrialization of supply chains, the mandate for always-on, carbon-free power is urgent and absolute. Nuclear energy is the essential backbone for this future, but the industry remains trapped in a delivery bottleneck. Before a shovel even hits the dirt, critical projects are slowed by highly customized engineering, fragmented data, and mountains of manual regulatory review.

Drive innovation to power a secure and sustainable futureThat is where AI comes in. To break the infrastructure bottleneck and shift the industry from ambition to delivery, Microsoft is announcing an AI for nuclear collaboration with NVIDIA, to provide end-to-end tools that streamline permitting, accelerate design, and optimize operations across the industry.

This set of technologies brings disciplined engineering to the entire lifecycle of a nuclear plant—spanning site permitting, design, construction, and continuous operations. By enabling these capabilities within a connected, AI-powered foundation, we are empowering energy developers to make highly complex work repeatable, traceable, secure, and predictable—slashing development timelines and eliminating rework without sacrificing safety.

PausedThe digital foundation for nuclear at scaleThe only thing that may be more complex than building a nuclear plant is designing and permitting one. Permitting alone can take years, cost hundreds of millions of dollars, and involve an immense amount of data processing and reporting. It’s not a lack of need, knowledge, or even willingness that’s holding development back, but rather the inability to progress efficiently and consistently through rigorous permitting and development processes.

Engineers can spend thousands of hours drafting, cross-referencing, formatting, searching, reviewing, and reworking materials. They have to identify and fix inconsistencies across tens of thousands of pages. It is little wonder that plants have been notorious for construction delays and cost overruns.

To break this infrastructure bottleneck, we need to move away from highly customized engineering towards repeatable, reference-based delivery—while maintaining regulatory standards and engineering accountability.

With AI, we can identify tiny documentation inconsistencies and resolve them quickly. By unifying data and simulation across the lifecycle, we ensure complex work remains:

Traceable: Every engineering decision is digitally linked to the evidence and regulations that back it up.Audit-Ready: The system keeps a perfect “paper trail,” ensuring that regulators can verify safety instantly.Secure: High-level intelligence is applied within a governed, protected environment.Predictable: High-fidelity simulations map time and cost, catching delays before they happen in the real world.This isn’t just about speed; it’s about trust. Engineers and regulators are freed to focus on what matters most: building a safe, secure, high-capacity, carbon-free power source that’s on-time and on-budget.

Here is how AI and Digital Twins can carry a project from the initial phases to efficient operations:

Design and engineering: Digital Twins and high-fidelity simulations enable faster iteration. Engineers can reuse proven patterns and instantly see how a tiny design change impacts the entire model, creating a validated plan before breaking ground.Licensing and permitting: Generative AI handles the heavy lifting of document drafting and gap analysis. It unifies all project information, ensuring comprehensive applications aligned with historical permits. This allows expert regulators to focus their time on safety judgments rather than reconciling thousands of pages of text.Construction and delivery: While traditional 3D models only map physical space, 4D (time scheduling) and 5D (cost tracking) simulations can virtually construct the plant before shovels hit the dirt. AI and Digital Twins allow developers to track physical progress against the digital plan in real-time, catching potential delays and preventing the schedule collisions that lead to expensive rework.Operations and maintenance: AI-powered sensors and operational digital twins detect anomalies early, ensuring higher uptime and predictive maintenance that keeps the grid stable with human operators firmly in control.By unifying data, traceability, and simulation across phases, AI accelerates design validation with high-fidelity 3D models and Digital Twins, improves licensing consistency through AI-assisted document workflows, and connects design assumptions to operational performance—giving operators, regulators, and stakeholders clearer, continuous visibility.

Accelerating delivery: How Aalo Atomics, Idaho National Labs, and Southern Nuclear are deploying AI for nuclearThe proof is in the progress. Our collaboration is already changing the pace of nuclear delivery.

Aalo AtomicsAalo Atomics has reduced the time-intensive permitting process by 92% using the Microsoft Generative AI for Permitting solution, saving an estimated $80 million a year. For Aalo, the value of the Microsoft and NVIDIA collaboration isn’t just speed—it’s confidence.

“Two things matter most: enterprise-scale complexity and mission-critical reliability. We’re deploying something complex at a scale only a company like Microsoft really understands. There’s no room for anything less than proven reliability.”

—Yasir Arafat, Chief Technology Officer, Aalo Atomics

PausedSouthern NuclearSouthern Nuclear has developed and deployed agents using Microsoft Copilot across its fleet, including engineering and licensing, to improve consistency, reuse knowledge faster, and support better decision-making in key workstreams.

Idaho National LaboratoryWhen it comes to the public sector and specifically United States Federal, Idaho National Laboratory (INL) has become an early adopter of AI for nuclear technology. By using the AI capabilities to automate the assembly of complex engineering and safety analysis reports, INL is streamlining the review process and creating standard methodologies for regulators to adopt these tools safely, further speeding deployment.

Expanding the ecosystem: How Everstar and Atomic Canyon are operationalizing AI for nuclear on Microsoft AzureMicrosoft is actively expanding this secure ecosystem. Everstar—an NVIDIA Inception startup—brings domain-specific AI for nuclear to Azure to modernize how the industry manages project workflows and governed data pipelines.

“The nuclear industry has been bottlenecked by documentation burden and regulatory complexity for decades. This partnership means our customers get the secure, scalable cloud deployments they demand. It’s a significant step toward making nuclear power fast, safe, and unstoppable.”

—Kevin Kong, Chief Executive Officer, Everstar

We are also excited to highlight Atomic Canyon, whose Neutron platform is now available in the Microsoft Marketplace, allowing nuclear developers to deploy these capabilities with consistency and control through trusted procurement pathways.

Progress at the pace this moment requiresAI is enabling the energy industry to deliver more power, faster, and safely. This Microsoft and NVIDIA collaboration provides the path to do exactly that for advanced developers, owners, and operators. By turning fragmented, high-variance workflows into governed, auditable systems, we can compress timelines without compromising rigor. By unifying data, simulation, and evidence across design, permitting, construction, and operations, we are accelerating the deployment of firm, carbon-free power while strengthening regulatory confidence and operational resilience.

The AI for nuclear operations collaboration brings together NVIDIA Omniverse, NVIDIA Earth 2, NVIDIA CUDA-X, NVIDIA AI Enterprise, PhysicsNeMo, Isaac Sim, and Metropolis with Microsoft Generative AI for Permitting Solution Accelerator and Microsoft Planetary Computer to create a comprehensive, AI-powered digital ecosystem for nuclear energy on Azure.

Microsoft, NVIDIA, and Aalo Atomics will be presenting this AI-lead industry perspective at CERAWeek 2026 in a session entitled “A Digital Age for Nuclear: Aalo Atomics, NVIDIA, and Microsoft.”

Discover moreReady to move from ambition to delivery? See how the Microsoft and NVIDIA nuclear for AI collaboration can drive change within your organization.

Contact us to learn more.
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Quelle: Azure

What’s new with Microsoft in open-source and Kubernetes at KubeCon + CloudNativeCon Europe 2026

There’s a pattern in how complex technology matures. Early on, teams make their own choices: different tools, different abstractions, different ways of reasoning about failure. It looks like flexibility but at scale it reveals itself as fragmentation.

The fix is never just more capability; it’s shared operational philosophy. Kubernetes proved this. It didn’t just answer “how do we run containers?” It answered “how do we change running systems safely?” The community built those patterns, hardened them, and made them the baseline.

AI infrastructure is still in the chaotic phase. The shift from “working versus broken” to “good answers versus bad answers” is a fundamentally different operational problem, and it won’t get solved with more tooling. It gets solved the way cloud-native did: open source creating the shared interfaces and community pressure that replace individual judgment with documented, reproducible practice.

That’s what we’re building toward. Since my last update at KubeCon + CloudNativeCon North America 2025, our teams have continued investing across open-source AI infrastructure, multi-cluster operations, networking, observability, storage, and cluster lifecycle. At KubeCon + CloudNativeCon Europe 2026 in Amsterdam, we’re sharing several announcements that reflect that same goal: bring the operational maturity of Kubernetes to the workloads and demands of today.

Learn more about Azure Kubernetes ServiceBuilding the open source foundation for AI on KubernetesThe convergence of AI and Kubernetes infrastructure means that gaps in AI infrastructure and gaps in Kubernetes infrastructure are increasingly the same gaps. A significant part of our upstream work this cycle has been building the primitives that make GPU-backed workloads first-class citizens in the cloud-native ecosystem.

On the scheduling side, Microsoft has been collaborating with industry partners to advance open standards for hardware resource management. Key milestones include:

Dynamic Resource Allocation (DRA) has graduated to general availability, with the DRA example driver and DRA Admin Access also shipping as part of that work.Workload Aware Scheduling for Kubernetes 1.36 adds DRA support in the Workload API and drives integration into KubeRay, making it more straightforward for developers to request and manage high-performance infrastructure for training and inference.DRANet now includes upstream compatibility for Azure RDMA Network Interface Cards (NICs), extending DRA-based network resource management to high-performance hardware where GPU-to-NIC topology alignment directly affects training performance.Beyond scheduling, we’ve continued investing in the tooling needed to deploy, operate, and secure AI workloads on Kubernetes:

AI Runway is a new open-source project that introduces a common Kubernetes API for inference workloads, giving platform teams a centralized way to manage model deployments and adopt new serving technologies as the ecosystem evolves. It ships with a web interface for users who shouldn’t need to know Kubernetes to deploy a model, along with built-in HuggingFace model discovery, GPU memory fit indicators, real-time cost estimates, and support for runtimes including NVIDIA Dynamo, KubeRay, llm-d, and KAITO.HolmesGPT has joined the Cloud Native Computing Foundation (CNCF) as a Sandbox project, bringing agentic troubleshooting capabilities into the shared cloud-native tooling ecosystem.Dalec, a newly onboarded CNCF project, defines declarative specifications for building system packages and producing minimal container images, with support for SBOM generation and provenance attestations at build time. Reducing attack surface and common vulnerabilities and exposures at the build stage matters for any organization trying to run AI workloads responsibly at scale.Cilium also received a broad set of Microsoft contributions this cycle, including native mTLS ztunnel support for sidecarless encrypted workload communication, Hubble metrics cardinality controls for managing observability costs at scale, flow log aggregation to reduce storage volume, and two merged Cluster Mesh Cilium Feature Proposals (CFPs) advancing cross-cluster networking.What’s new in Azure Kubernetes ServiceIn addition to our upstream contributions, I’m happy to share new capabilities in Azure Kubernetes Service (AKS) across networking and security, observability, multi-cluster operations, storage, and cluster lifecycle management.

From IP-based controls to identity-aware networkingAs Kubernetes deployments grow more distributed, IP-based networking becomes harder to reason about: visibility degrades, security policies grow difficult to audit, and encrypting workload communication has historically required either a full-service mesh or a significant amount of custom work. Our networking updates this cycle close that gap by moving security and traffic intelligence to the application layer, where it’s both more meaningful and easier to operate.

Azure Kubernetes Application Network gives teams mutual TLS, application-aware authorization, and detailed traffic telemetry across ingress and in-cluster communication, with built-in multi-region connectivity. The result is identity-aware security and real traffic insight without the overhead of running a full-service mesh. For teams managing the deprecation of ingress-nginx, Application Routing with Meshless Istio provides a standards-based path forward: Kubernetes Gateway API support without sidecars, continued support for existing ingress-nginx configurations, and contributions to ingress2gateway for teams moving incrementally.

At the data plane level, WireGuard encryption with the Cilium data plane secures node-to-node traffic efficiently and without application changes. Cilium mTLS in Advanced Container Networking Services extends that to pod-to-pod communication using X.509 certificates and SPIRE for identity management: authenticated, encrypted workload traffic without sidecars. Rounding this out, Pod CIDR expansion removes a long-standing operational constraint by allowing clusters to grow their pod IP ranges in place rather than requiring a rebuild, and administrators can now disable HTTP proxy variables for nodes and pods without touching control plane configuration.

Visibility that matches the complexity of modern clustersOperating Kubernetes at scale is only manageable with clear, consistent visibility into infrastructure, networking, and workloads. Two persistent gaps we’ve been closing are GPU telemetry and network traffic observability, both of which become more critical as AI workloads move into production.

Teams running GPU workloads have often had a significant monitoring blind spot: GPU utilization simply wasn’t visible alongside standard Kubernetes metrics without manual exporter configuration. AKS now surfaces GPU performance and utilization directly into managed Prometheus and Grafana, putting GPU telemetry into the same stack teams are already using for capacity planning and alerting. On the network side, per-flow L3/L4 and supported L7 visibility across HTTP, gRPC, and Kafka traffic is now available, including IPs, ports, workloads, flow direction, and policy decisions, with a new Azure Monitor experience that brings built-in dashboards and one-click onboarding. For teams dealing with the inverse problem (metric volume rather than metric gaps) operators can now dynamically control which container-level metrics are collected using Kubernetes custom resources, keeping dashboards focused on actionable signals. Agentic container networking adds a web-based interface that translates natural-language queries into read-only diagnostics using live telemetry, shortening the path from “something’s wrong” to “here’s what to do about it.”

Simpler operations across clusters and workloadsFor organizations running workloads across multiple clusters, cross-cluster networking has historically meant custom plumbing, inconsistent service discovery, and limited visibility across cluster boundaries. Azure Kubernetes Fleet Manager now addresses this with cross-cluster networking through a managed Cilium cluster mesh, providing unified connectivity across AKS clusters, a global service registry for cross-cluster service discovery, and intelligent routing with configuration managed centrally rather than repeated per cluster.

On the storage side, clusters can now consume storage from a shared Elastic SAN pool rather than provisioning and managing individual disks per workload. This simplifies capacity planning for stateful workloads with variable demands and reduces provisioning overhead at scale.

For teams that need a more accessible entry point to Kubernetes itself, AKS desktop is now generally available. It brings a full AKS experience to your desktop, making it straightforward for developers to run, test, and iterate on Kubernetes workloads locally with the same configuration they’ll use in production.

Safer upgrades and faster recoveryThe cost of a bad upgrade compounds quickly in production, and recovery from one has historically been time-consuming and stressful. Several updates this cycle focus specifically on making cluster changes safer, more observable, and more reversible.

Blue-green agent pool upgrades create a parallel pool with the new configuration rather than applying changes in place, so teams can validate behavior before shifting traffic and maintain a clear rollback path if something looks wrong. Agent pool rollback complements this by allowing teams to revert a node pool to its previous Kubernetes version and node image when problems surface after an upgrade (without a full rebuild). Together, these give operators meaningful control over the upgrade lifecycle rather than a choice between “upgrade and hope” or “stay behind.” For faster provisioning during scale-out events, prepared image specification lets teams define custom node images with preloaded containers, operating system settings, and initialization scripts, reducing startup time and improving consistency for environments that need rapid, repeatable provisioning.

Connect with the Microsoft Azure team in AmsterdamThe Azure team are excited to be at KubeCon + CloudNativeCon Europe 2026. A few highlights of where to connect with the Azure team on the ground:

Rules of the Road for Shared GPUs: AI Inference Scheduling at Wayve—Customer keynote, Tuesday, March 24, 2026, 9:37 AM CET.Scaling Platform Ops with AI Agents: Troubleshooting to Remediation—Tuesday, March 24, 2026, 10:13 AM CET with Jorge Palma, Principal PDM Manager, Microsoft.Building cross-cloud AI inference on Kubernetes with OSS—Wednesday, March 25, 2026, 1:15 PM CET with Jorge Palma, Principal PDM Manager, Microsoft and Anson Qian, Principal Software Engineer, Microsoft.Visit our booth #200 for live demos and conversations with the Azure and AKS team.Or browse the full schedule of sessions by Microsoft speakers throughout the week.Happy KubeCon + CloudNativeCon!

Get started with Azure Kubernetes Service
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Quelle: Azure

Modernizing regulated industries with cloud and agentic AI

Organizations today face mounting pressure to grow revenue, strengthen security, and innovate—often all at the same time. To meet these demands, many are accelerating cloud migration as a way to unlock greater business outcomes. According to the IDC White Paper,1 sponsored by Microsoft, the top driver for moving to the cloud is operational efficiency, with 46% of organizations prioritizing reductions in IT operating costs. Beyond cost savings, cloud infrastructure is also enabling organizations to prepare for increased use of AI (37%), launch new performance intensive applications (30%), improve resilience (26%), and meet governance, risk, and compliance requirements (24%).

Yet despite broad cloud adoption, migration and modernization remain complex. Legacy architectures, fragmented environments, and persistent skills gaps continue to slow progress, pushing organizations to find ways to migrate faster while minimizing operational risk.

The IDC study highlights agentic AI as a critical unlock. These intelligent systems automate assessments, orchestrate migration and modernization efforts, and optimize operations across hybrid environments—helping organizations shift from periodic, manual initiatives to continuous, adaptive modernization. This momentum is driving unprecedented growth, with IDC forecasting the public cloud services market will reach USD1.9 trillion by 2029.

Discover how AI accelerates modernizationWhile migration frameworks may be horizontal, their real-world impact is industry-specific. Healthcare, financial services, and manufacturing each face unique constraints shaped by regulation, operational risk, and mission-critical systems.

In this blog, we explore the key migration and modernization challenges across these three industries—healthcare, manufacturing, and financial services—through real customer stories that highlight the tangible impact cloud adoption is delivering today.

Healthcare: Modernizing securely while powering next-generation clinical experiencesHealthcare faces the toughest modernization headwinds: strict regulations (HIPAA/HITECH, HITRUST), fragmented clinical data across electronic health records (EHRs) and imaging systems, aging on-premises infrastructure resulting in high Capex, and heightened exposure to ransomware.1 Clinical environments also demand extremely low latency and high reliability.

The IDC study notes that these constraints slow modernization—but accelerate the need for it, as organizations push to scale telehealth, imaging workloads, genomics pipelines, and AI-powered clinical workflows.1

What healthcare organizations need, according to the IDC study:

Secure, compliant integration across EHRs, picture archiving and communication systems (PACS), genomics systems, and Internet of Things (IoT) medical devices.1Elastic compute for high-throughput imaging and genomics.Stronger disaster recovery and recovery time performance.1Ambient documentation and AI-supported diagnostics.Secure clinician collaboration and modern patient digital front doors.Customer spotlight: Franciscan HealthFacing aging infrastructure and disaster recovery risks, Franciscan adopted a pragmatic workload placement strategy—moving its Epic EHR to Microsoft Azure.

The results included:

$45 million in savings over five years after migrating Epic to Azure.90% faster disaster recovery compared to the prior environment.Around a 30-minute failover, reduced from hours.$10–$12 million per day in potential downtime risk avoided.Learn more about Franciscan Health’s journey to migrate its Epic EHR to Azure here.

Healthcare’s modernization mandate is clear: reduce operational risk, meet regulatory demands, and harness cloud AI to improve patient outcomes.

Turn modernization into AI impact: Explore the AI for Better Health e‑bookFinancial services: Enabling real-time intelligence and automated complianceFinancial institutions operate in one of the most regulated environments, including the payment card industry data security standard (PCI DSS), the Sarbanes-Oxley Act (SOX), the Gramm-Leach-Bliley Act (GLBA), Basel capital frameworks, and know your customer (KYC) and anti-money laundering (AML) requirements, and rely heavily on legacy mainframes that are difficult to modernize. Today, regulatory pressure is intensifying further as new frameworks such as the EU’s Digital Operational Resilience Act (DORA) and the EU AI Act raise the bar for operational resilience, third-party risk management, model transparency, and ongoing compliance monitoring. Under DORA, financial services firms must demonstrate continuous information and communication technology (ICT) risk management, advanced incident reporting, and resilience testing across critical systems and cloud service providers. Meanwhile, the EU AI Act introduces governance requirements for high-risk AI systems, including explainability, data lineage, human oversight, and auditability—with direct implications for fraud models, credit scoring, and customer decisioning platforms.

IDC interviews highlight accelerating demand for real-time risk analytics, fraud detection, digital onboarding, and infrastructure elasticity to support peak activity—capabilities that are increasingly mandated, not optional.1

Key challenges the IDC study identifies:

Strict data residency, model risk governance, explainability, and eDiscovery requirements.1Heightened expectations for operational resilience, cyber defense, and third-party risk oversight.Legacy systems and common business-oriented language (COBOL)-based batch processes resistant to change.Rapidly evolving regulatory mandates requiring continuous compliance rather than point-in-time audits.Cloud—especially especially platform as a service (PaaS) and managed services—helps financial institutions shift from static, batch-driven compliance to continuous controls and real-time observability. By reducing batch windows from hours to minutes, modern cloud platforms enable real-time insights, automated evidence collection, resilient architectures, and policy-driven compliance workflows aligned with DORA and AI governance requirements.1 Learn more about how Microsoft can help financial institutions navigate these requirements in this e-book.

Reimagine financial services with AI, data, and cloud innovationCustomer spotlight: CrediclubTo accelerate product innovation and meet expectations from Mexico’s national banking and securities commission (CNBV), Mexican fintech Crediclub modernized its databases to a serverless platform as a service (PaaS) architecture and adopted microservices.1

The impact:

Uptime improved from around 80% to 99.5%.90% reduction in network latency through Multiprotocol Label Switching (MPLS) and dark fiber.Rapid deployment of new financial products via Kubernetes and DevSecOps.For financial institutions, modernization is no longer just about efficiency—it is foundational to resilience, trustworthy AI, and regulatory compliance at scale.

Manufacturing: Unifying IT and OT for predictive, data-driven industrial operationsManufacturers operate in one of the most complex operating environments—defined by legacy and proprietary operational technology (OT) protocols, historically air-gapped manufacturing execution systems (MES) and supervisory control and data acquisition (SCADA) systems, and globally distributed supply chains. Stringent low-latency requirements for safety-critical systems, intermittent connectivity at the edge, and the need to protect intellectual property further compound the challenge. The ability to modernize and unify these environments—without compromising safety, reliability, or performance—represents a critical inflection point for industrial transformation.

Unique modernization challenges according to the IDC study:

Ultra-low latency requirements for safety-critical operations.Massive telemetry ingestion and time-series analytics at scale.Operational complexity across global, distributed supply chains.Secure protection of intellectual property across edge and cloud environments.Opportunities unlocked by cloud:

Predictive maintenance with IoT ingestion.1Reduced unplanned downtime and improved overall equipment effectiveness (OEE).Digital twins for plants, lines, and products.Computer vision for real-time quality and safety.High-performance computing (HPC) simulations for engineering and design.Standardized, global data models.Get the e-book: Modernizing Manufacturing for Transformative GrowthCustomer spotlight: ASTEC IndustriesASTEC unified fragmented systems across its rock to road value chain—from aggregate processing through asphalt production and paving—by adopting Azure, modernizing to timeseries databases, and building a universal connectivity platform using Azure IoT Hub, Azure Events Hub, and Power BI.1

The results:

Realtime operational visibility across fleets.Predictive maintenance for reducing downtime.New digital services supported by connected equipment.Manufacturing’s modernization imperative: unify OT and IT, scale real-time intelligence, and enable global efficiency.

Microsoft’s approach: Continuous, intelligent, collaborative modernizationMicrosoft’s strategy is grounded in a simple principle: modernization should be continuous, intelligent, and collaborative. The IDC study emphasizes that successful enterprises adopt a balanced, multipath migration strategy, blending rehost, replatform, refactor, and software as a service (SaaS) substitution based on workload criticality.1

Microsoft enables this approach through a comprehensive set of tools and offerings, including Azure Copilot and GitHub Copilot. Agentic automation enables:

Discovery and dependency mapping.Security assessment and 6R recommendations.Application refactoring, code remediation, and modernization.Azure Migrate provides unified discovery, assessment, migration execution, and modernization services. Azure Accelerate complements this with a coordinated framework that includes:

Guided deployments through Cloud Accelerate Factory.1Funding and Azure credits for planning, pilot, and rollout.Expert partners and tailored skilling programs.The IDC study concludes that organizations using Microsoft Azure for migration and modernization achieve lower operational costs, improved resiliency, faster modernization timelines, and stronger security postures—especially in regulated industries.1

Looking ahead: Agentic modernization as the foundation for AI-ready enterprisesAcross all industries, IDC’s findings are consistent: agentic AI is emerging as the new force multiplier for modernization, enabling organizations to keep pace with rising complexity, regulatory demands, and competitive pressure.

Healthcare, financial services, and manufacturing each face unique constraints—but cloud modernization remains the foundation for innovation, operational excellence, and enterprise AI.

Microsoft’s approach gives organizations the unified automation, intelligence, and tooling they need to modernize securely and at scale.

Explore how Azure Copilot accelerates cloud migrationDiscover GitHub Copilot for app modernizationSee how Azure Accelerate supports transformationDiscover how AI accelerates modernization1 IDC White Paper, Cloud Migration and Modernization Strategies for Healthcare, Financial Services, and Manufacturing, February 2026.
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Quelle: Azure

Microsoft at NVIDIA GTC: New solutions for Microsoft Foundry, Azure AI infrastructure and Physical AI

Microsoft combines accelerated computing with cloud scale engineering to bring advanced AI capabilities to our customers. For years, we’ve worked with NVIDIA to integrate hardware, software and infrastructure to power many of today’s most important AI breakthroughs.

What’s new at NVIDIA GTCExpanded Microsoft Foundry capabilities to build, deploy and operate production-ready AI agents on NVIDIA accelerators and open NVIDIA Nemotron modelsNew Azure AI infrastructure optimized for inference-heavy, reasoning-based workloads, including the first hyperscale cloud to power on next-generation NVIDIA Vera Rubin NVL72 systemsDeeper integration across Microsoft Foundry, Microsoft Fabric and NVIDIA Omniverse libraries and open frameworks to support Physical AI systems from simulation to real‑world operationsFrom Frontier models to production-ready agentsAt the foundation of this system is Microsoft Foundry: serving as the operating system for building, deploying and operating AI at enterprise scale. Foundry builds on Azure to bring together models, tools, data and observability into a single system designed for production agents. Today we’re expanding those capabilities across Foundry Agent Service and NVIDIA Nemotron models.

The next-generation Foundry Agent Service and Observability in Foundry Control Plane are now generally available, enabling organizations to build and operate AI agents at production scale. Foundry Agent Service allows teams to quickly develop agents that reason, plan and act across tools, data and workflows. Once created, Foundry Control Plane provides the developer end-to-end visibility into agent behavior, unlocking both developer productivity as well as enterprise trust. Companies such as Corvus Energy are already using Foundry to replace manual inspection workflows with agent-driven operational intelligence across their global fleet.

We are further simplifying the path from prototype to production with the availability of Voice Live API integration with Foundry Agent Service, in public preview, which enables developers to build voice-first, multimodal, real-time agentic experiences. This pairs with the general availability of a refreshed Microsoft Foundry portal and expanded integrations for Palo Alto Networks’ Prisma AIRS and Zenity, delivering deeper builder experiences and runtime security across the entire agent lifecycle.

NVIDIA Nemotron models are also now available through Microsoft Foundry, joining the widest selection of models on any cloud, including the latest reasoning, frontier and open models. This bolsters our recent partnership announcement bringing Fireworks AI to Microsoft Foundry, enabling customers to fine-tune open-weight models like NVIDIA Nemotron into low-latency assets that can be distributed to the edge.

Scaling AI infrastructure for the world’s most demanding workloadsInference AI workloads are reshaping cost, performance and system design requirements. To operationalize agentic AI at scale, customers need purpose-built infrastructure for inference‑heavy, reasoning‑based workloads that can be deployed and operated consistently across global and regulated environments.

Microsoft’s AI infrastructure approach is engineered to seamlessly bring next-generation NVIDIA systems into Azure datacenters that are designed for power, cooling networking and rapid generational upgrades. This allows our customers to move with speed and agility and stay at the leading edge from generation to generation.

In less than a year, we’ve deployed hundreds of thousands of liquid-cooled Grace Blackwell GPUs across our global datacenter footprint, and now we are excited to be the first hyperscale cloud to power on NVIDIA’s newest Vera Rubin NVL72 in our labs. Over the next few months, Vera Rubin NVL72 will be rolled out into our modern, liquid-cooled Azure datacenters.

Microsoft’s infrastructure innovation with NVIDIA also extends to sovereign and regulated environments to give customers control of both where AI runs and how it evolves over time. Recently, we announced Foundry Local support for modern infrastructure and large AI models, and today we now have initial support for NVIDIA Vera Rubin platform on Azure Local, extending accelerated AI capabilities to customer-controlled environments. This approach allows organizations to plan for next-generation AI workloads, including reasoning-based and agentic systems, while maintaining Azure-consistent operations, governance and security through our unified software layer with Azure Arc and Foundry Local.

YouTube Video

Bringing AI into the physical worldAs AI moves beyond digital experiences, Microsoft and NVIDIA are collaborating to support the next wave of Physical AI. At GTC, this work centers on NVIDIA Physical AI Data Factory Blueprint, with Microsoft Foundry as the platform for hosting and operating Physical AI systems on Azure at cloud scale.

By integrating this blueprint with Azure services as part of a Physical AI Toolchain, Microsoft enables developers to build, train and operate physical AI and robotics workflows that connect physical assets, simulation and cloud training environments into repeatable, enterprise-grade pipelines. To support, we are introducing a public Azure Physical AI Toolchain GitHub repository integrated with the Nvidia Physical AI Data Factory and with core Azure services.

To further the impact of AI in real‑world, physical environments, today Microsoft and NVIDIA are deepening the integration between Microsoft Fabric and NVIDIA Omniverse libraries, connecting live operational data with physically accurate digital twins and simulation. This allows organizations to see what’s happening across their physical systems, understand it in real time and use AI to decide what to do next. In practice, customers in manufacturing and operations and beyond are using this approach to move beyond dashboards and alerts to coordinated, AI‑driven action across machines, facilities and workflows.

From innovation to impactMicrosoft is delivering reliable, production‑scale AI by bringing together its global AI infrastructure, platforms and real‑world systems with the latest innovation from NVIDIA. For customers, this means the ability to operate intelligence continuously, running inference-heavy, reasoning-based and physical AI workloads with the performance, security and governance required for real businesses and regulated industries.

Whether powering always-on agents, scaling next-generation AI infrastructure or deploying intelligent systems in factories, energy facilities and sovereign environments, Microsoft and Nvidia are helping customers move faster from insight to action.

Yina Arenas leads product strategy and execution for Microsoft Foundry, overseeing the end–to–end AI product portfolio, infrastructure, developer experiences and foundation model integration across OpenAI, Anthropic, Mistral, DeepSeek and others. She delivers an enterprise ready, production grade AI platform trusted by global customers for secure, reliable and scalable AI.
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Quelle: Azure

FabCon and SQLCon 2026: Unifying databases and Fabric on a single data platform

In this article

Introducing the Database Hub in Microsoft FabricGetting your data estate ready for AI with FabricUnifying your data estate with Microsoft OneLakeProcessing and harmonizing data with Fabric analyticsCreating semantic meaning with Fabric IQEmpowering agents to act with Fabric data and operations agentsBuilding mission-critical applications with developer experiences in FabricMigrating your existing Azure service to FabricSee more Fabric innovation

Welcome to the third annual FabCon and our first ever SQLCon here in Atlanta, Georgia. With nearly 300 workshops and sessions, this joint event will highlight how they are bringing the power of Microsoft SQL and Microsoft Fabric together to create a single, unified platform. But FabCon 2026 and SQLCon 2026 are about more than product innovation. It’s about providing space for our 8,000 attendees to come together and share real experiences, learn from each other, and solve challenges side-by-side. Only together can we move beyond the hype and into meaningful results.

Learn more about FabCon and SQLCon 2026

The excitement surrounding this event reflects the same momentum we’re seeing across our data portfolio. Just two and a half years after Microsoft Fabric reached general availability, it’s already serving more than 31,000 customers and remains the fastest-growing data platform in Microsoft’s history. Fortune 500 companies like The Coca-Cola Company are already using Fabric at scale across their organizations.

Microsoft Fabric is helping us evolve our data foundation into a more unified, AI-ready platform. Combined with Power BI and capabilities like Fabric IQ, it enables the enterprise to turn data into intelligence and act on it faster.
Shekhar Gowda, Vice President of Global Marketing Technologies at The Coca-Cola Company

Our databases are accelerating just as quickly, with SQL Server 2025 growing more than twice as fast as the previous version.

Today, we’re thrilled to share how we are bringing the power of databases and Fabric together to form a truly converged data platform—one that unifies transactional, operational, and analytical data under a single, consistent architecture. I’ll also highlight how we’ve enhanced Fabric to help you transform data into the semantic knowledge AI needs to understand your business, powered by Fabric IQ and Power BI’s industry-leading semantic model technology.

Introducing the Database Hub in Microsoft Fabric

Databases sit at the heart of the enterprise data estate—a system of record powering applications, transactions, and mission‑critical insights. Yet as organizations scale across cloud, on‑premises, and edge environments, database estates have become increasingly fragmented and isolated. As AI places even greater demands on data estates, unifying databases under a single access point and control plane has become essential.

To address this challenge, Fabric is expanding its role as the central access point for enterprise data with the Database Hub in Fabric, now available in early access. Database Hub in Fabric provides a unified database management experience that brings together databases across edge, cloud, and Fabric into a single, coherent view. Teams now have one place to explore, observe, govern, and optimize their entire database estate—including Azure SQL, Azure Cosmos DB, Azure Database for PostgreSQL, SQL Server (enabled by Azure Arc), Azure Database for MySQL, and Fabric Databases—without changing how each service is deployed.

Built for scale, the Database Hub in Fabric introduces an agent‑assisted, human-in-the loop approach to database management. With built-in observability, delegated governance, and Microsoft Copilot-powered insights, teams can deploy intelligent agents to continuously reason over estate‑wide signals and surface what changed, explain why it matters, and guide teams toward what to do next. The result is a simpler, more confident way to manage databases at scale. Over time, this model enables database estates to become more proactive, resilient, and intelligent, laying the foundation for greater autonomy, while keeping humans firmly in control of goals, boundaries, and trust.

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Learn more about Database Hub in Fabric and what’s new across Databases

Beyond unified database management, we’re also introducing savings plan for databases, a new way to save by up to 35% compared to pay-as-you go pricing on select services.*

Bringing databases together under a single management layer is a critical step as you prepare your estates for AI at scale. But it’s not the end of the journey. The challenge shifts from where data lives to how data is understood, connected, and activated across the enterprise.

Getting your data estate ready for AI with Fabric

As organizations move from traditional applications to AI‑powered, multi‑agent systems, the advantage is shifting away from the specific model you deploy. It now lies in the intelligence and context that allow agents to understand how your business is run, the state of your business, and your institutional knowledge to help take meaningful action.

This is the challenge Microsoft IQ is designed to address. Unlike point solutions on the market today, Microsoft IQ provides an intelligence layer that delivers shared, enterprise-grade business context to every agent. That context is built from three complementary sources: productivity signals from Work IQ, institutional knowledge from Foundry IQ, and live business data from Fabric IQ.

However, like the database layer, while the IQ context layer is a critical part of a successful, and healthy AI foundation, it is not the full story. Building a complete AI-ready data foundation requires investing in four core steps:

Unifying your data estate to eliminate silos and reduce architectural complexity.

Processing and harmonizing data so it becomes AI-ready, clean, connected, and structured for both operational and analytical use.

Curating semantic meaning to give agents contextual understanding, enabling them to interpret data the way your teams already do. This is where Microsoft IQ comes into play.

Empowering AI agents to act, applying that context to automate workflows, accelerate decisions, and transform operations end‑to‑end.

Unifying your data estate with Microsoft OneLake

Every AI initiative starts with the same fundamental challenge: understanding where your data lives and how to bring it together. Microsoft OneLake was built to solve that problem by unifying data across clouds, on-premises environments, and third-party platforms into a single logical data lake without unnecessary extracting, transforming, and loading (ETL), fragmentation, or duplicated copies.

Are my agents hunting for data?

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Connecting to more sources than ever before

Today, we’re expanding Mirroring in Fabric to support even more systems our customers rely on. Mirroring for SharePoint lists and Dremio are now in preview with Azure Monitor coming soon, while mirroring for Oracle and SAP Datasphere are generally available—all of which are available as part of the core mirroring capabilities. We are also introducing extended capabilities in mirroring designed to help you operationalize mirrored sources at scale, including Change Data Feed (CDF) and the ability to create views on top of mirrored data, starting with Snowflake. Extended capabilities for mirroring will be offered as a paid option.

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Shortcut transformations are also now generally available, allowing data to be shaped automatically as it connects to or moves within OneLake. You can convert formats such as Excel to Delta tables, now in preview, and apply AI-powered transformations.

Additionally, we are continuing to invest in open interoperability, ensuring OneLake works seamlessly with the platforms organizations already use. We are excited to announce the ability to natively read from OneLake through Azure Databricks Unity Catalog is now in public preview. We also recently announced the general availability of our interoperability with Snowflake.

I’m also excited to share that Auger, a rapidly growing supply chain platform designed to bring intelligence and automation to global operations, has built its platform on Fabric, with all data stored natively in OneLake. This architecture enables Auger customers to seamlessly access their operations data through OneLake shortcuts within their own Fabric environments and use the full power of the platform including Power BI, Fabric data agents, and more. Learn more in my blog, co-authored with Auger Chief Executive Officer Dave Clark.

Protect your data with OneLake security, now generally available

Security and governance remain foundational to OneLake. I’m thrilled to announce OneLake security will be generally available in the coming weeks, enabling data owners to define roles, enforce row- and column-level controls, and manage permissions through a single unified model that follows the data.

To learn more about these announcements, read the OneLake blog and the Fabric Data Factory blog.

Processing and harmonizing data with Fabric analytics

AI agents are only as reliable as the data you feed them. Before data can train or ground an agent, it must be integrated, cleaned, and structured, so the agent operates from consistent, trusted information. With industry-leading engines in Fabric like Spark, T-SQL, KQL, and Analysis Services, we can equip data teams to do exactly that.

Now, we are expanding these capabilities with the introduction of Runtime 2.0 in preview, purpose-built for large-scale data computation. It incorporates Apache Spark 4.x, Delta Lake 4.x, Scala 2.13, and Azure Linux Mariner 3.0 to power advanced enterprise workloads. Materialized lake views are also now generally available, simplifying medallion architecture implementation in Spark SQL and PySpark and enabling always up-to-date pipelines with no manual orchestration. In addition, a new agentic Copilot experience in notebooks delivers deeper context awareness, reasoning over your workspace, and generating code with greater speed and precision.

For real-time scenarios, we’re launching Maps in Fabric into general availability. Maps add geospatial context to your agents and operations by turning large volumes of location-based data into interactive, real-time visual insights.

For a comprehensive overview of these announcements and much more, read the Fabric Analytics blog and the Fabric Real-Time Intelligence blog.

Creating semantic meaning with Fabric IQ

Preparing raw data for AI is essential. The next step is transforming that data into meaningful, unified business context. That is where Fabric IQ comes in.

Fabric IQ unifies analytical data and operational data, including telemetry, time series, graph, and geospatial data, within a shared semantic framework of business entities, relationships, properties, rules, and actions. Instead of thinking in terms of tables and schemas, your teams and agents can operate on this framework, or ontology, aligned to how the business actually runs.

Fabric IQ ontologies will soon become accessible through an MCP server in preview, enabling agents to discover, understand, and act on this semantic layer. Ontologies can also serve as context sources for maps and soon in operations agents in Fabric, extending shared business context directly into operational decision-making and execution.

We are also excited to announce planning in Fabric IQ, a new enterprise planning capability that enables organizations to create plans, budgets, forecasts, and scenario models directly on top of Fabric’s semantic models. By complementing Fabric IQ’s ontologies with integrated planning, you get a complete, contextual view of your historical, real-time, and forward planning data. This allows users and agents to quickly answer what has happened, what is happening, and what should happen all from a single source. See this in action:

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Finally, we recently announced a strategic partnership with NVIDIA to power the next generation of Physical AI by integrating Real-Time Intelligence and Fabric IQ with NVIDIA Omniverse libraries. The combined platform unifies real‑time operational data, business semantics, and physical simulation to enable organizations to optimize their physical operations in scenarios like intelligent digital twins, predictive maintenance, autonomous logistics, and energy optimization.

To learn more about all of our partner announcements, read the Fabric ISV blog and the planning in Fabric IQ blog.

Enhancing the underlying Fabric IQ technology

Powering much of Fabric IQ’s rich experience is a combination of Power BI’s industry-leading, rich semantic model technology and graph in Fabric, our highly scalable graph database. Already delivering insights to more than 35 million active users, semantic models provide the ideal foundation for training agents through Fabric IQ. Now, with the general availability of Direct Lake on OneLake, your tables can be read directly from OneLake with native security enforcement, richer cross-item modeling, and import-class performance without data movement or refresh.

I’m also excited to share that graph in Fabric will be generally available in the coming weeks, enabling teams to visualize and query complex relationships across customers, partners, and supply chains.

To learn more, check out the Fabric IQ blog and the Power BI blog.

Empowering agents to act with Fabric data and operations agents

Frontier organizations are moving beyond general-purpose assistants and instead, adopting multi-agent systems composed of specialized agents. These agents are each grounded on specific data and reusable across different systems, allowing you to deliver more accurate, accelerated, and scalable outcomes.

To support your multi-agent systems, Fabric comes with built-in agent creation capabilities with Fabric data agents and operations agents. I’m excited to share that Fabric data agents are now generally available. Fabric data agents can be thought of as virtual analysts, aligned to specific domain data to support deeper analysis and deliver insights. Operations agents complement them by monitoring real-time data, detecting patterns, and taking proactive action.

Check out a quick demo of operations agents in Fabric:

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These agents can be used across Fabric or as foundational knowledge sources in leading AI tools like Microsoft Foundry, Copilot Studio or even Microsoft 365 Copilot. To learn more about our AI announcements, check out the Fabric analytics blog covering data agents and the Fabric IQ blog covering operations agents.

Building mission-critical applications with developer experiences in Fabric

Developers building the next generation of AI applications need a comprehensive, cost-effective data platform that’s already integrated with your existing tools and workflows. Today, we are expanding Fabric’s developer tooling to meet that demand.

First, Fabric Model Context Protocol (MCP) is advancing with two major milestones. Fabric local MCP is now generally available, providing an open-source local server that connects AI coding assistants such as GitHub Copilot directly to Fabric. Alongside this, we’re introducing the public preview of Fabric remote MCP, a secure, cloud‑hosted execution engine that enables AI agents and automation tools to perform authenticated actions in Fabric.

We’re also enhancing our Git integration with selective branching, allowing developers to branch out for a specific feature and pull only the items they need. You also get improved change comparisons to more easily review recent updates, and new folder relationships which show how feature workspaces connect to source workspaces.

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We’re also launching two open-source projects to help teams move faster with Fabric: Agent Skills for Fabric and Fabric Jumpstart. Agent Skills for Fabric is an open-source set of purpose-built plugins that let you use natural language in the GitHub Copilot terminal to harness the full power of Microsoft Fabric. Additionally, Fabric Jumpstart is designed to help you get off the ground with detailed guidance, reference architectures, and single‑click deployments for sample datasets, notebooks, pipelines, and reports.

Finally, we are announcing that the Fabric Extensibility Toolkit (FET), an evolution of the Workload Development Kit (WDK), is now generally available. Along with this release, we are enabling support for full CI/CD, variable library, and a new management experience in the Admin portal.

Read the Fabric Platform blog

Migrating your existing Azure service to Fabric

As Fabric continues to grow in functionality, we are also simplifying the migration from other Azure services. In addition to our existing Synapse tooling, we are bringing new migration assistants for Azure Data Factory, Azure Synapse Analytics, and Azure SQL in public preview.

The new Fabric migration assistant for Azure Data Factory and Synapse Analytics helps move your existing pipelines and artifacts like Spark pools and notebooks into Fabric with minimal disruption. It’s designed to support incremental modernization, allowing teams to evaluate, convert, and optimize pipelines as they transition to Fabric. The migration assistant for SQL databases helps move SQL Server into Fabric by importing schemas through DACPACs, identifying and resolving compatibility issues with AI assistance, and guiding teams through assessment and data copy workflows for a smoother cutover.

See more Fabric innovation

Explore the AI shift with The Shift podcast

In addition to the announcements above, we are also rolling out a broad set of Fabric innovations across the platform. For a deeper look at the updates and what’s new this month, visit the Fabric March 2026 Feature summary blog, the Power BI March 2026 feature summary blog, and the latest posts on the Fabric Updates channel.

Explore additional resources for Microsoft Fabric

Sign up for the Fabric free trial.View the updated Fabric Roadmap.Try the Microsoft Fabric SKU Estimator.Visit the Fabric website.Join the Fabric community.Read other in-depth, technical blogs on the Microsoft Fabric Updates Blog.

Read additional blogs by industry-leading partners

Sonata Software: Building an AI-ready data platform with data agents, ontology, and governance in Microsoft FabricQuadrant Technologies LLC: Real-Time Operational Intelligence in Microsoft Fabric: Deep Dive into RTI Capabilities, Anomaly Detection and Activator AlertingInspark: Why switch from Azure Synapse to Microsoft Fabric?Esri: Unlock the power of location intelligence with ArcGIS for Microsoft FabricDream IT Consulting Services: 8 Real-World Use Cases of Data Agents in Microsoft FabricUB Technology Innovations Inc.: From Data Platform to Decision Platform: How Microsoft Fabric and Copilot are Redefining Enterprise AnalyticsSimpson Associates: Fabric Data Warehouse: Bringing Structure to Modern Data StrategiesSynapx Ltd.: Migrating Power BI to Microsoft Fabric Lakehouse with Medallion Architecture: A Strategic Imperative for Modern Construction EnterprisesCloud Services: Real-Time Intelligence in Action: How Microsoft Fabric Helped Delfi Transform Its NewsroomCloud Services: Microsoft Fabric Data Agents: A New RealityiLink Digital: Detect to Act in Seconds: How Real-Time Intelligence Is Rewriting the Rules of Emissions ManagementValorem Reply: How Nonprofits Are Rethinking Data with Microsoft Fabric

*Customers may see savings estimated to be between 0% and 35%. The 35% savings estimate is based on one Azure SQL Database serverless running for 12 months at a pay-as-you-go rate vs. a reduced rate for a 1-year savings plan. Based on Azure pricing as of March 2026. Prices are subject to change. Actual savings may vary based on location, database service, and/or usage. 
The post FabCon and SQLCon 2026: Unifying databases and Fabric on a single data platform appeared first on Microsoft Azure Blog.
Quelle: Azure

Advancing agentic AI with Microsoft databases across a unified data estate

This week, we are excited to kick off SQLCon 2026 alongside FabCon in Atlanta. Bringing these SQL and Fabric communities together creates a unique opportunity to learn, connect, and share what’s next across the Microsoft databases portfolio.

This year is especially meaningful, as it marks the return of a Microsoft‑led SQL community event, while also showcasing how SQL continues to evolve as a critical part of Fabric. It is not just about new technology, but about reconnecting with each other and building the future of SQL together.

It’s inspiring to see the Microsoft SQL community continue to grow and engage, with user groups worldwide keeping conversations active across the SQL portfolio and a lot of customers using Microsoft SQL to innovate every day. With a comprehensive portfolio built on strategic common foundations and available across edge, PaaS, and SaaS, Microsoft databases form a unified platform for modern enterprise needs, whether you are migrating and modernizing, building cloud-native AI applications, or unifying your data.

Learn more about what’s happening at SQLConMigrate and modernize with Azure SQLMany of our customers are not modernizing in one big leap. You are evolving from SQL Server to hybrid and then to cloud services, and you want that journey to feel familiar, predictable, and low risk. That is exactly what Azure SQL is designed to deliver. Built on a consistent Microsoft SQL foundation from on premises to the cloud, Azure SQL brings AI capabilities directly into your database experience, along with enterprise‑grade security, high availability, and the flexibility to scale as your needs grow. Azure SQL is fully SQL compatible, delivers strong performance and low latency, and supports hybrid scenarios through Azure Arc.

AI agents are becoming an important accelerator for database migration and modernization at Microsoft, helping our customers reduce manual effort and move faster with more guided experiences across the journey. The general availability of GitHub Copilot in SSMS 22 is a great example of that investment in action: you can use the same GitHub Copilot experience you already use in Visual Studio and VS Code, now inside SQL Server Management Studio (SSMS), with chat and code assistance that helps you write, edit, and refactor T‑SQL more quickly and confidently. Whether you are a developer or database administrator (DBA), new to SQL or highly experienced, GitHub Copilot can support common workflows like improving queries and assisting with troubleshooting and administration tasks right where you work, and we are continuing to expand what it can do.

Today we are announcing savings plan for databases, a flexible, spend-based pricing option that helps you save up to 35%1 vs. pay-as-you-go prices on a one-year commitment. Savings plan for databases is designed for modern, evolving database environments: Customers commit to a fixed hourly spend for one year and receive lower prices across eligible Azure database services. Savings are automatically applied to the highest-value usage each hour, helping reduce costs while supporting migration, modernization, and architectural change.

Build cloud-native AI apps at scaleOnce you move to the cloud, the questions shift. How do you build faster, scale smarter, and unlock more value from your data without re‑architecting everything you have already built? That is where Azure SQL Database Hyperscale comes in.

With Azure SQL Database Hyperscale, customers gain better price-performance, elastic scale and resilience for any workload, without the cost or disruption of rewriting T‑SQL or reworking operational models. Its unique architecture, built on shared storage and multiple replicas, allows you to scale reads independently from writes. With built‑in HTAP isolation, applications can handle massive transactional and analytical workloads without complex redesign. New capabilities now in public preview extend that foundation even further, including the SQL MCP Server for securely connecting SQL data to AI agents and Copilots, as well as larger 160 and 192 vCore options for high‑throughput workloads.

We’re delivering faster, more capable vector indexes to power AI applications. Recent enhancements improve vector search performance and efficiency with no code changes required. With full insert, update, and delete support, vector indexes stay current in real time, enabling dynamic applications. Features like quantization, iterative filtering, and tighter query optimizer integration provide faster, more predictable results, helping teams build responsive AI experiences directly on their SQL data.

Temenos built its next‑generation banking platform, Temenos Core, on Azure using Azure SQL Database Hyperscale to achieve global scale, high availability, and resilient performance. The platform processes billions of transactions daily and more than 17,500 transactions per second at peak. By building on Hyperscale, Temenos reduced onboarding time, accelerated innovation, and shifted banks from worrying about downtime to competing on availability and digital innovation.

Unify your data estate with SQL database in FabricWe continue to raise the bar on enterprise readiness for SQL database in Fabric by bringing enterprise-grade security and compliance capabilities directly into the platform. Today at SQLCon, we announced the general availability of features including SQL Auditing, Customer‑Managed Keys, and Dynamic Data Masking, and the preview of workspace‑level Private Link. We brought these enhancements to help customers meet strict governance and regulatory requirements without adding operational complexity. The result is confidence that your SQL workloads in Fabric are secure, compliant, and ready for production.

SQL database in Fabric is becoming even more powerful for AI‑driven applications. The same vector indexing enhancements available in Azure SQL Database Hyperscale are now built into SQL database in Fabric as well. Because both are powered by the same Microsoft SQL engine, customers benefit from consistent performance, capabilities, and innovation across the SQL portfolio—making it easier to build intelligent applications wherever their data lives.

Finally, moving to SQL database in Fabric is simpler than ever. The Migration Assistant now supports SQL database in Fabric as a target destination. It provides a Copilot-assisted experience that helps SQL developers assess readiness, migrate schema, identify compatibility issues, and copy data with less manual effort. By preserving familiar SQL skills and workflows, customers can modernize at their own pace while accelerating time to value on Fabric’s unified analytics and AI platform.

Learn more about SQL database in FabricThere is one more Fabric innovation that matters deeply for how we deliver Microsoft databases as a unified platform. As applications grow more sophisticated, most organizations now rely on a mix of SQL and NoSQL databases across cloud, on‑premises, and edge environments. Provisioning, monitoring, and maintaining health across a growing database fleet often requires multiple tools and portals, making it harder to see what’s happening and manage at scale.

To address this, we are introducing the Database Hub in Microsoft Fabric, now available in early access. The Database Hub provides a unified database management experience that brings together databases across edge, cloud, and Fabric into one coherent view. From a single place, database teams can explore, observe, govern, and optimize their entire estate, including Azure SQL, Azure Cosmos DB, Azure Database for PostgreSQL, SQL Server enabled by Azure Arc, and Azure Database for MySQL without changing how each service is deployed or operated.

Built for scale, the Database Hub introduces an agent-assisted, human-in-the-loop approach to database management. Intelligent agents continuously reason over estate-wide signals to surface what changed, explain why it matters, and guide teams toward what to do next, while built-in observability, delegated governance, and Copilot-powered insights help teams move from insight to action with greater confidence. With the Database Hub, teams spend less time navigating tools and more time enabling what comes next: unlocking deeper integration across applications, analytics, and AI from a single control plane for the Microsoft databases portfolio.

Database Hub is available today in early access. Sign up today and see how the Database Hub can bring clarity and control to your database estate.

Moving forward with the SQL communitySQLCon is about bringing the SQL community together. It is about rebuilding connections and shared learning. It also reflects our long-term commitment to SQL. With a comprehensive portfolio built on strategic common foundations and available across edge, PaaS, and SaaS, Microsoft databases provide a unified platform for modern enterprise needs, whether you are migrating and modernizing, building cloud-native AI applications, or unifying your data. We are investing in SQL for the future, alongside the community that continues to shape it.

Finally, SQLCon is coming to Europe! Join the global data and SQL community from 28 Sep – 01st October, 2026 in Barcelona, Spain for hands-on learning, expert insights, and real-world stories. Register to be a part of it. I can’t wait to see you there.

Additional SQL resourcesGet early access to Database Hub in FabricMicrosoft Azure Summit: Migrate and Modernize with Agentic AIWhat is new about SQL database in FabricLearn more about savings plan for databases1Customers may see savings estimated to be between 0% and 35%. The 35% savings estimate is based on one Azure SQL Database serverless running for 12 months at a pay-as-you-go rate vs. a reduced rate for a 1-year savings plan. Based on Azure pricing as of March 2026. Prices are subject to change. Actual savings may vary based on location, database service, and/or usage. 
The post Advancing agentic AI with Microsoft databases across a unified data estate appeared first on Microsoft Azure Blog.
Quelle: Azure

Introducing GPT-5.4 in Microsoft Foundry

Built for Reliable AI Production: Stronger reasoning, dependable execution, and agentic workflows at scaleToday, we’re announcing OpenAI’s GPT‑5.4 to be generally available soon in Microsoft Foundry: a model designed to help organizations move from planning work to reliably completing it in production environments. As AI agents are applied to longer, more complex workflows; consistency and follow‑through become as important as raw intelligence. GPT‑5.4 combines stronger reasoning with built in computer use capabilities to support automation scenarios, and dependable execution across tools, files, and multi‑step workflows at scale.

GPT-5.4: Enhanced Reliability in Production AIGPT-5.4 is designed for organizations operating AI in real production environments, where consistency, instruction adherence, and sustained context are critical to success. The model brings together advances in reasoning, coding, and agentic workflows to help AI systems not only plan tasks but complete them with fewer interruptions and reduced manual oversight.

Compared with earlier generations, GPT-5.4 emphasizes stability across longer interactions, enabling teams to deploy agentic AI with greater confidence in day-to-day production use.

GPT-5.4 introduces advancements that aim for production grade AI:

More consistent reasoning over time, helping maintain intent across multi‑turn and multi‑step interactionsEnhanced instruction alignment to reduce prompt tuning and oversightLatency improved performance for responsive, real-time workflowsIntegrated computer use capabilities for structured orchestration of tools, file access, data extraction, guarded code execution, and agent handoffsMore dependable tool invocation reducing prompt tuning and human oversightHigher‑quality generated artifacts, including documents, spreadsheets, and presentations with more consistent structureTogether, these improvements support AI systems that behave more predictably as tasks grow in length and complexity.

From capability to real-world outcomesGPT‑5.4 delivers practical value across a wide range of production scenarios where follow‑through and reliability are essential:

Agent‑driven workflows, such as customer support, research assistance, and business process automationEnterprise knowledge work, including document drafting, data analysis, and presentation‑ready outputsDeveloper workflows, spanning code generation, refactoring, debugging support, and UI scaffoldingExtended reasoning tasks, where logical consistency must be preserved across longer interactionsTeams benefit from reduced task drift, fewer mid‑workflow failures, and more predictable outcomes when deploying GPT‑5.4 in production.

GPT-5.4 Pro: Deeper analysis for complex decision workflowsGPT‑5.4 Pro, a premium variant designed for scenarios where analytical depth and completeness are prioritized over latency.

Additional capabilities include:

Multi‑path reasoning evaluation, allowing alternative approaches to be explored before selecting a final responseGreater analytical depth, supporting problems with trade‑offs or multiple valid solutionsImproved stability across long reasoning chains, especially in sustained analytical tasksEnhanced decision support, where rigor and thoroughness outweigh speed considerationsOrganizations typically select GPT‑5.4 Pro when deeper analysis is required such as scientific research and complex problems, while GPT‑5.4 remains the right choice for workloads that prioritize reliable execution and agentic follow‑through.

Microsoft Foundry: Enterprise‑Grade Control from Day OneGPT‑5.4 and GPT‑5.4 Pro are available through Microsoft Foundry, which provides the operational controls organizations need to deploy AI responsibly in production environments. Foundry supports policy enforcement, monitoring, version management, and auditability, helping teams manage AI systems throughout their lifecycle.

By deploying GPT‑5.4 through Microsoft Foundry, organizations can integrate advanced agentic capabilities into existing environments while aligning with security, compliance, and operational requirements from day one.

Customer Spotlight

Get Started with GPT-5.4 in Microsoft FoundryGPT‑5.4 sets a new bar for production‑ready AI by combining stronger reasoning with dependable execution. Through enterprise‑grade deployment in Microsoft Foundry, organizations can move beyond experimentation and confidently build AI systems that complete complex work at scale. Computer use capabilities will be introduced shortly after launch.

GPT‑5.4 in Microsoft Foundry is priced at $2.50 per million input tokens, $0.25 per million cached input tokens, and $15.00 per million output tokens. It is available at launch in Standard Global and Standard Data Zone (US), with additional deployment options coming soon. GPT‑5.4 Pro is priced at $30.00 per million input tokens, and $180.00 per million output tokens, and is available at launch in Standard Global.

Build agents for real-world workloads. Start building with GPT‑5.4 in Microsoft Foundry today.
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Quelle: Azure