Building a cloud-native foundation for AI The relationship between model innovation and AI infrastructure has never been more clear; as AI becomes more capable, the platform underneath it matters even more. Agents are fundamentally changing how applications work, and with that, what organizations need from the cloud. Learn how to build a cloud-native AI foundation Cloud-native practices gave organizations the scale, flexibility and resilience to build a generation of distributed applications.
Agents build on that foundation, but they work differently, placing new demands on it. Unlike applications designed around more predictable workflows, agents behave in entirely new ways, gathering information and deep intelligence from across the business , using tools, making decisions, and coordinating work across long-running processes. To complete complex tasks, they pause for approval, respond to new information in real time and change course as conditions evolve.
All this changes what the platform needs to deliver. Customers need infrastructure that adapts as demand changes, applications that remain reliable as work becomes more dynamic, and the visibility and control to understand what is happening across the system. Meeting those needs doesn’t mean starting over, but it does require that the cloud-native foundation organizations already rely on, to evolve.
Compute, storage and networking need to work together . Application platforms need to make it easier to build and run AI workloads. Open technologies need to give customers flexibility without adding unnecessary complexity.
The platform decisions organizations make today are increasingly strategic, shaping how effectively they can put AI to work for their business. Evolving the cloud-native foundation is essential to helping organizations get more from AI today while giving them the flexibility to take advantage of new capabilities as they emerge. Optimize infrastructure for AI workloads The true measure of AI infrastructure is the useful work it enables.
AI workloads rarely place the same demands on infrastructure from beginning to end. Different parts of the work may need accelerated compute, CPUs, fast access to data or connectivity across applications and environments. The answer isn’t simply more capacity, but rather, to match the right resources to the right work at the right time .
That makes coordination across the infrastructure extremely important. Compute, storage and networking need to work together as demands change. When they do, teams can spend less time managing infrastructure and more time focused on the applications and outcomes that matter to their business.
Economics matter, too. Infrastructure needs to be tuned to the demands of the application and the business value it delivers, with the right balance of performance, reliability and cost across the complete workload. Basecamp Research is one example.
The company used Azure to assemble 9. 7 trillion nucleotides for EDEN, its therapeutic-design models, and reported laboratory validation across three therapeutic applications within about three weeks. In this example, AI didn’t simply require more infrastructure, it needed AI infrastructure that was coordinated and designed around the outcome – bringing together the compute, data and platform capabilities needed to move complex work forward.
As Glen Gowers, co-founder of Basecamp Research put it: “To really be successful here, we need those three ingredients to come together. We need someone scaling the data; that’s us. But someone who really knows how to scale models and scale compute is Microsoft.”
Leadership takeaway: Measure success by the useful work AI completes. Judge infrastructure investment by the performance, reliability and cost of delivering those outcomes, with compute, storage and networking operating as one system. Turn open-source innovation into production capability Open technologies have long helped drive cloud innovation, and AI is accelerating that pace.
New models, frameworks and cloud-native technologies give organizations more choice in how they build and adapt applications. That opportunity builds on an established cloud-native foundation. Gartner forecasts that by 2028, 95% of new AI agent deployment will use Kubernetes.
1 Reusing established components can shorten the path to production, allowing developers to focus on differentiated business capabilities rather than rebuilding the foundation. More choice can also bring more complexity. Taking advantage of open-source innovation often requires specialized expertise to integrate, manage and maintain technologies in production.
That expertise is hard to find and even harder to scale. We need to make these capabilities accessible to more businesses, without requiring every organization to build and maintain that expertise themselves. Azure helps reduce that burden.
Services like Azure Kubernetes Service give teams access to open-source innovation while helping manage the complexity and risk of running it in production. This makes it easier for more businesses to take advantage of open technologies while preserving the flexibility to adapt with changing needs. Leadership takeaway: Choose a platform that helps turn open-source freedom into business advantage.
Preserve the ability to adapt and optimize while reducing the expertise and operating effort required to sustain those choices in production. Align AI infrastructure costs with business value As more organizations put AI into production, understanding the economics becomes increasingly important. AI investment starts with an expectation of business value: faster decisions, more responsive services or greater capacity to get useful work done.
Then, it’s evaluating whether the value justifies the investment to deliver it. FinOps can help teams understand that return and make more informed decisions in this area.
Originally published at azure.microsoft.com
