Why Cloud Native Infrastructure Is Non-Negotiable for GenAI
Significant trends are shaping the dynamic field of generative AI in early 2025, including the rise of agentic AI. This powerful technology improves GenAI capabilities in reasoning, planning, and tool use to enable LLMs to act on what they suggest. In the meantime, large investments are being made into nuclear energy to meet the power-hungry needs of data centers. GenAI is becoming more of a factor in robotics but is also a growing cybersecurity threat.
Yet amid these headline-grabbing trends, one issue deserves more attention: the need for cloud native architecture to support apps for GenAI. Without these foundations, project ROI could fall well short of expectations or worse, fail.
The challenges are already becoming evident. Gartner forecasts that by the end of 2025, at least 30% of genAI projects will fail at the proof-of-concept stage.
To address the problems, building infrastructure that allows for containerization is essential. However, this requires a fresh approach tailored to GenAI’s unique needs. Another challenge is how to attract, retrain, and reskill teams.
Containerization Is The Gold Standard
Containerization might seem like another buzzword, but its concept is surprisingly simple. It’s about packaging an application’s code along with all its dependencies. This allows an app to run seamlessly, whether in the cloud, a private data center, or on edge devices.
Modern applications rely on complex dependencies, from modules that manage authentication to those connecting with ERP and CRM systems. Regular updates and patches are necessary. If not, even a minor issue can unexpectedly break an application.
This is where containerization comes in. Containerization transforms software development by allowing resource sharing — CPU, memory, storage — while providing isolation for heightened security.
Containerization is indispensable for GenAI applications, often tangled in intricate dependencies. By breaking down applications into microservices — self-contained, testable components — developers unlock the ability to iterate rapidly, scale effectively, and deploy with agility.
This trend isn’t going unnoticed. Recent research highlights the surge in container adoption partly due to GenAI’s demands. The report reveals that nearly 90% of organizations report that at least some of their applications are now containerized, and this number is expected to grow with the rapid adoption of new application workloads like GenAI. However, 64% of organizations find cloud native and container application development challenging.
Modern Infrastructure for the GenAI World
Traditional software development follows a highly structured process. The key steps include requirement analysis, system design, coding, debugging, code review, and deployment. It’s a time-tested process that’s been refined over decades. However, this playbook starts to fall apart when applied to GenAI applications. This is mainly due to the unique aspects of AI development, such as cleaning data, fine-tuning, and retrieval-augmented generation (RAG).
The infrastructure demands aren’t any easier. Training and deploying these models often call for GPU-heavy environments, which can drive up the costs. And then there’s the orchestration of multiple AI models. Of course, Kubernetes has long been the darling of developers, but new frameworks like LangChain, AutoGen, and CrewAI have emerged during the past couple of years.
Flexibility is yet another curveball. GenAI applications need to meld seamlessly into cloud native ecosystems and containerized environments. According to the ECI survey, over 80% of respondents ranked modernizing their infrastructure as a top priority.
Addressing the Skills Gap
GenAI is creating a significant skills gap. The ECI study, which included C-level decision-makers in IT and DevOps, revealed that only 21% of respondents believe their organizations have the skills necessary to support GenAI applications and infrastructure. Additional headcount isn’t readily available and comes at a premium.
GenAI experts are typically the end users and customers of these AI projects and can’t or don’t want to manage how GenAI is delivered. Focus on cultivating in-house expertise in platform engineering and data preparation, and leveraging simple but powerful enterprise-grade tools. The good news is that there are many training resources, from online courses to intensive boot camps.
Conclusion
GenAI is poised to reshape many industries, but its success relies on some key factors: containerization, modernized infrastructure, and a workforce skilled in GenAI’s demands. These aren’t just checkboxes on a to-do list. They’re the backbone of scalability, efficiency, and adaptability. The enterprise companies that get this right will turn today’s challenges into tomorrow’s opportunities and pave the way for sustainable, future-proof growth.
