The cloud has given us speed, scalability, and access to innovation at a pace that would have been hard to imagine only a few years ago. But the landscape is changing. Geopolitical dependencies are being reassessed, digital sovereignty is becoming increasingly important - particularly in Europe - and generative AI is creating new requirements around data, infrastructure, connectivity, and cost.

As a result, a question is becoming increasingly relevant in customer conversations and real-world cloud projects: Does every workload really need to run entirely in the public cloud or can we bring the benefits of Azure to environments where data and applications deliberately need to remain local?

Azure Local brings cloud capabilities closer to on-premises infrastructure, while Local AI creates new opportunities to run AI models closer to enterprise data and systems. This becomes particularly relevant when sovereignty requirements, sensitive data, limited connectivity, latency, or the economics of AI workloads come into play.

Instead of choosing between "cloud" and "on-premises," we are moving towards a spectrum of deployment models.

At Cluster Reply, we encounter these requirements in real customer environments. But we also believe that emerging technologies should not only be evaluated on architecture diagrams. We want to understand how they behave when they meet actual hardware, networks, operational constraints, and AI workloads.

That is why we built our own engineering lab to run Azure Local and Local AI on physical infrastructure, explore their limitations, and test real-world scenarios.

In this session, I will take you through that journey: What happens when an Azure Local architecture becomes a real system? What challenges emerge around hardware, networking, connectivity, and operations? How can local generative AI become part of this architecture? And which lessons from our engineering lab can be applied to real customer scenarios?

This is not about moving away from the cloud or returning to traditional on-premises infrastructure. It is about choice - combining cloud, local infrastructure, and AI in a way that balances innovation, sovereignty, economics, and operational requirements.

You will leave with practical experiences, concrete use cases, and lessons learned from our engineering lab as well as a balanced view of when Azure Local and Local AI can complement the public cloud, and when the cloud remains the better choice.