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TL;DR A Microsoft and VMware hybrid platform should not be designed as a loose collection of products or as an attempt to make one vendor’s control plane replace the other. The practical pattern is to use VMware Cloud Foundation as the private cloud execution and lifecycle domain, then use Microsoft services such as Azure Arc, […]
TL;DR Moving from VMware Cloud Foundation to Microsoft Azure is not a product replacement exercise. It is an architectural translation problem. A VMware design expresses workload intent through objects such as workload domains, vSphere clusters, resource pools, datastores, NSX segments, gateways, distributed firewall rules, identity groups, and operations policies. Azure expresses similar outcomes through a
The Hybrid Cloud Translation Bureau: Mapping VMware Cloud Foundation to Microsoft Azure Read More »
TL;DR The image presents a compelling vision: business capabilities operate as connected domains, an AI command center turns enterprise signals into decisions, and a secure network fabric keeps applications, data, people, and devices moving across private cloud, public cloud, and edge environments. The practical architecture is more disciplined than the picture suggests. NSX should remain
TL;DR A modern enterprise is not a collection of products. It is a system of interdependent capabilities that must share identity, policy, telemetry, automation, ownership, and governance. The practical goal is not one vendor, one console, or one giant platform team. It is coordinated autonomy, where Azure, VMware Cloud Foundation, SaaS, networking, data, security, AI,
The Enterprise Clockwork: An Integrated Hybrid Cloud Operating Model Read More »
Introduction The VMware replacement debate often begins with the wrong question. Teams ask which hypervisor has live migration, high availability, snapshots, distributed switching, templates, role-based access control, or an API. Those comparisons are useful, but they address only the lowest visible layer of a much larger operating model. A mature VMware estate is rarely just
Introduction Azure Local networking becomes confusing when the same words appear in several different product contexts. Logical network, virtual network, network security group, load balancer, gateway, and Network Controller all sound familiar to anyone who has worked with Azure or VMware NSX. The names create an understandable expectation that the underlying capabilities and operating models
AI placement is becoming a real architecture decision. For the first wave of generative AI adoption, many organizations could experiment with hosted models, isolated copilots, and proof-of-concept retrieval systems without making hard infrastructure choices. That window is closing. AI is moving from isolated experiments into business workflows, operational systems, and agentic patterns that can retrieve
AI placement decisions become more useful when they move from opinion to architecture criteria. The first article in this series focused on the core inputs: data gravity, latency, sovereignty, and cost. Those inputs explain why some AI workloads belong in managed cloud services, some belong close to local infrastructure, and some need a private AI
Azure AI, Azure Local, and vCF Private AI: A Practical Placement Comparison Read More »
TL;DR Multicloud is no longer just a workload placement problem. The harder problem is control-plane sprawl. Azure, AWS, Google Cloud, and VMware Cloud Foundation each bring their own identity model, policy engine, hierarchy, observability stack, network control plane, automation surface, and operational lifecycle. Those native control planes are useful. They become dangerous when each platform
Microsoft has quietly become the main supplier of OpenAI models in China, selling the technology to the country’s largest internet companies even as OpenAI and Anthropic keep their own models out of the market on intellectual-property and misuse grounds. The arrangement, detailed this week by Bloomberg, hands Microsoft a position no other American AI vendor holds:
Microsoft sells OpenAI models in China. OpenAI and Anthropic won’t. Read More »
