Artificial intelligence (AI) is no longer an experimental side project. It's reshaping industries, the world we live in and becoming central to product innovation and competitive advantage. But beneath every impressive AI model lies a complex infrastructure stack that powers it.
As companies scale their AI initiatives, many are embracing cloud native platforms to do it. Why? Because containers, Kubernetes, and elastic cloud infrastructure are ideally suited to the demands of modern AI workloads. The use of cloud-native infrastructure to deploy AI workloads, sometimes referred to as cloud native artificial intelligence (CNAI), offers the scalability, portability, and speed that AI teams need to build and deploy smarter solutions, faster.
But there’s a catch: managing this infrastructure is anything but simple. And unless your core mission is infrastructure engineering, it may not be where your team should spend its time.
Cloud Native AI refers to the combination of cloud computing principles and tools—especially containers and Kubernetes—with the needs of AI workloads, such as model training, inference, and data processing.
Think of it as building AI applications on an infrastructure foundation designed to scale effortlessly, deploy consistently, and support rapid iteration. The Cloud Native Artificial Intelligence Whitepaper by the CNCF AI Working Group provides examples where this model is already in action at companies like Hugging Face and OpenAI:
These examples aren’t just impressive—they signal a shift. AI is becoming more operational, and cloud native is how it's getting done.
“Cloud Native Artificial Intelligence is an evolving extension of Cloud Native. Kubernetes is an orchestration platform that can be used to deploy and manage containers, which are lightweight, portable, self-contained software units. AI models can be packaged into containers and then deployed to K8s clusters. Containerization is especially crucial for AI models because different models typically require different and often conflicting dependencies. Isolating these dependencies within containers allows for far greater flexibility in model deployments. CN tooling allows for the efficient and scalable deployment of AI models, with ongoing efforts to tailor these for AI workloads specifically.”
Source: Cloud Native Artificial Intelligence Whitepaper
At the heart of every high-performing AI workload is a carefully constructed platform—one that starts with hardware (NVIDIA, Intel, ARM, etc), and has cloud infrastructure layered on top (see topology below). Fundamental to the infrastructure is its ability to be elastic, scalable, and optimized for the unique demands of AI. The orchestration layer brings it all together and that’s where Kubernetes stands out. When properly built, configured, and managed, Kubernetes offers key capabilities needed to meet these demands: it schedules workloads across clusters, ensures resources are allocated efficiently, and provides the consistency needed to move seamlessly from development to production.
And while Kubernetes is ideal for these workloads, Kubernetes is complex, so teams must decide if they want to focus on the platform and infrastructure or spend time on the ML lifecycle and workloads.
Source: Cloud Native Artificial Intelligence Whitepaper
AI workloads have specific demands:
Containers solve the first problem by allowing each model to run in a self-contained environment, avoiding dependency challenges and making deployments reproducible.
Kubernetes sits atop containers at the orchestration layer—scaling, deploying, and managing them reliably across environments. And the cloud provides the elastic, cost-effective backbone: compute, storage, and networking on demand.
Together, they form the ideal foundation for AI. But building that foundation is no small feat.
“I don’t know of anybody who's really writing new AI-based apps and running those models in VMs (virtual machines). That's all happening in Kubernetes. So in that sense, AI is built on cloud native.”
Source: Forbes, AI And Cloud Native Alchemize The Future Of Enterprise IT, Dan Ciruli, senior director of product management at Nutanix.
While the benefits of cloud native AI are clear—scalability, portability, and speed—the cost of building and operating that infrastructure is often underestimated.
Kubernetes is powerful, but it’s not turnkey. Running it for AI workloads means staying on top of a long list of responsibilities: security patches, Kubernetes version upgrades, CVE remediation, autoscaling tuning, node right-sizing, policy enforcement, and cost monitoring. And that’s before even getting to container registry management or CI/CD integration.
AI teams often enter the Kubernetes world for its flexibility and performance benefits. But instead of fine-tuning models or improving inference times, they end up buried in YAML and Helm charts. They’re stuck troubleshooting autoscaling issues when GPU resources spike or trying to enforce consistency across dev and production clusters.
AI infrastructure isn’t just about standing up a cluster, it’s about keeping it production-ready as AI workloads scale. That means ensuring the environment is optimized for high throughput, secured against misconfigurations, and cost-effective enough to support both experimentation and production workloads.
Running your own Kubernetes platform might give you full control—but it also means full responsibility.
So the real question is: do you want to spend your time maintaining infrastructure, or accelerating your AI breakthroughs? Because every hour your engineers spend on platform toil is an hour they’re not pushing your AI applications forward.
This is where Managed Kubernetes-as-a-Service comes in. These services take on the infrastructure complexity—so your team doesn’t have to.
Instead of managing upgrades, scaling clusters manually, or firefighting CVEs, you get:
The most advanced AI organizations—like OpenAI—invest (at great expense) in dedicated infrastructure teams to scale Kubernetes. However, most companies don’t have the luxury of huge funding rounds. Managed Kubernetes services help you punch above your weight.
Cloud native AI is the future of machine learning infrastructure. It offers all the speed, scale, and flexibility you need to run AI workloads efficiently. But unless you’re in the business of managing Kubernetes clusters, you shouldn’t have to build that future alone.
Let a Managed Kubernetes-as-a-Service handle the platform, so your team can focus on what really matters: building smarter, faster, more powerful AI applications.