Why Kubernetes Matters in the Age of AI

Everyone’s buzzing about AI… but behind every breakthrough, every lightning-fast response, every cloud-scaled model, there’s an invisible superhero doing the real heavy lifting. It’s Kubernetes.

What Kubernetes Actually Is (and How It Works)

You see, in 2025, ChatGPT hit 800 million weekly active users. When OpenAI handles billions of requests per day, Kubernetes silently spins up thousands of GPU pods during peak demand… and tears them down the moment traffic drops. No human babysitting. 

The same thing happens in gaming. When millions of players simultaneously jump into Xbox Cloud Gaming, Kubernetes automatically scales services to ensure that no one experiences lag or downtime.  

And here’s the part nobody talks about: today’s most innovative AI startups run their production workloads on Kubernetes. 

Kubernetes’s superhero origin

But hold on, what exactly is Kubernetes? 

The word Kubernetes comes from the Ancient Greek for “pilot” or “helmsman” meaning the person at the helm who steers the ship. Engineers shorten it to K-8-S, because there are eight letters between the K and the S.

So in plain English, Kubernetes is an open-source container orchestration platform. But the story gets better. It began when three Google engineers: Craig McLuckie, Joe Beda, and Brendan Burns pitched a radical idea: an open-source system that could manage containers at massive scale. The foundation of modern cloud-native computing was born. 

Under the hood, Kubernetes is built around two main layers: The control plane and worker nodes.

The Control Plane is the brain

The control plane is the brain of Kubernetes:  the system that maintains the desired state of your entire cluster. Think of it as constantly asking: “Where are we right now… and where should we be?” 

It’s powered by four core components: the API Server, etcd, the Scheduler, and the Controller Manager. 

• API Server (kube-apiserver): Think of this as Kubernetes’ front door. It exposes the entire Kubernetes API, handles every REST request, and processes everything from creating a pod to scaling a deployment. 
• Then it comes to etcd: It is a distributed key-value store that remembers API server data, basically everything about your cluster nodes, pods, configs, secrets, the whole state. If the API Server is the front door, etcd is the memory.
• The job of Scheduler (kube-scheduler) is simple but critical: it assigns each pod to the right node. Like choosing the best seat for every passenger on a plane.
• Last but not least: Controller Manager: “Is reality still matching the desired state?” If something crashes, it automatically restores it. 

Worker Nodes are the muscles

Now let’s talk about the worker nodes. These are the machines actually running your apps. Each one includes three essential components:

• kubelet, the local agent that starts and monitors containers, It makes sure containers are started, running, healthy, and exactly as the control plane expects.
• kube-proxy, it configures network rules on every node, making Kubernetes Services work which handles internal networking, load-balancing, and can forward TCP, UDP, and SCTP traffic using simple stream routing or round-robin across backends.
• Container runtime: This is the engine that actually runs your containers, such as Docker Engine, containerd, CRI-O, Mirantis Container Runtime, you name it. Every node needs one so Kubernetes can pull images, create containers, and keep your pods alive. 

Together, these components turn a messy cluster of machines into one unified, self-managing platform where applications deploy automatically, recover instantly, and scale seamlessly without human intervention. 

What Kubernetes Really Has to Do With AI?

So… what does Kubernetes have to do with AI?

When people imagine what’s behind AI, they picture massive data centers packed with powerful GPUs. That’s AI infrastructure, it’s a combination of hardware and software stack built to train massive AI models. 

But here’s the part most people missed: brutal AI infrastructure helps you train powerful AI models… but running and managing these models at a global scale is a completely different challenge. This is where Kubernetes comes into play! Btw if you’re curious about how LLMs are actually trained, there’s a full video already on the channel, check it out if you’re interested.

Kubernetes automates the hardest parts of AI workloads: GPU scheduling at large scale, workload orchestration, load balancing inference traffic, autoscaling fleets of containers, and keeping distributed systems alive 24/7. 

Remember when NVIDIA acquired Run.ai? That wasn’t random. Run.ai built a GPU orchestration layer on top of Kubernetes, letting teams dynamically allocate GPUs, schedule jobs, share resources, and squeeze maximum performance out of their compute. 

Kubernetes also adds true multi-cloud portability. Teams can move workloads between different clouds, from AWS to Azure, GCP, or on-prem clusters without rewriting code. 

Reliability comes out of the box: auto-healing, rolling updates, self-reconciliation, the SRE dream. 

The brutal truth about Kubernetes

But let’s be honest: Kubernetes isn’t magic. It’s powerful… but yes, for many organizations, painfully complex. That’s why you keep hearing stories of companies walking away from Kubernetes. 

Why? Because what engineers call “sophisticated,” many teams experience complexity, both in Kubernetes itself and across the huge ecosystem of tools you’re suddenly expected to understand.  

Historically, Kubernetes excels at stateless microservices running at massive scale. But stateful workloads databases, vector stores or even Kafka streams are a different beast. And while Kubernetes keeps getting better at this, the learning curve is steep. Some companies hit scaling bottlenecks or operational issues, then retreat back to the simplicity of VMs. 

And here’s the real problem: Kubernetes can run anything… but only if you have the people who know how to run Kubernetes. Engineers with deep, real-world production Kubernetes experience? They’re rare. And that scarcity drives salaries through the roof, turning Kubernetes from “free open-source tooling” into a very expensive operational commitment.  

Since 2023, that challenge has hit even harder. Wall Street is hyping AI, but at the same time, companies are slashing budgets, restructuring teams, and laying off tech talent left and right. Are those the days when organizations paid premium salaries just to keep Kubernetes experts in-house?

Yeah… those days are gone. 

So, before you jump into Kubernetes, you’d better ask: Do we have the talent and the budget to run it the way it actually needs to be run?

The AI Startups Betting Big on Kubernetes 

Despite the controversy, many companies still run their production data and AI systems on Kubernetes today. 

Like NVIDIA running Kubernetes and even built an entire cloud-native stack as a crucial abstraction layer, which we detailed in this video, and Netflix, Apple, Adobe, and more. Even some AI startups are betting big on Kubernetes. 

Start with OpenAI. Large portions of OpenAI’s internal infrastructure, including experimentation pipelines, batch processing, run on massive Kubernetes clusters. Imagine 800 million weekly active users on ChatGPT. When traffic surges, Kubernetes auto-scales fleets of GPU-backed nodes behind the scenes. 

Anthropic, the company behind Claude, runs their inference workloads on Google Kubernetes Engine (GKE) because it relies on containerized, Kubernetes-orchestrated jobs for repeatability, portability, and high availability across regions.  

Beyond these giants, Kubernetes is powering AI startups in every domain.  

In healthcare, Babylon Health built its AI diagnostic engine entirely on Kubernetes, then used Kubeflow to automate its ML pipeline from ingestion to deployment.

AlphaSense migrated fully to Kubernetes, cutting deployments from hours to minutes and increasing release frequency by 30×.

Norna AI used a managed Kubernetes platform to avoid hiring a full DevOps team, boosting productivity by 10×.

And Cast AI pushes Kubernetes to its limit, optimizing GPU clusters, eliminating idle nodes, and cutting LLM cloud bills by up to 50%.

The Global Kubernetes Market hit $1.7B in 2023 and is projected to grow to $11.8B by 2032 — a 24% CAGR.

Safe to say, Kubernetes isn’t going anywhere. It’s growing with AI, and the ecosystem around it is exploding. 

Brendan Burns, co-founder of Kubernetes, wrote in my book Certified Kubernetes Administrator Exam Guide: “The expectations of business, customers, and users of today’s applications have never been greater. Kubernetes and cloud-native are the skills that will enable you to build applications that meet the standards necessary to compete in the world of modern application development.”  

So, if you’re a tech professional or an entrepreneur, skilling up on Kubernetes and cloud-native tools is more than just useful: it’s a competitive advantage. 

Looking forward

If you enjoyed this breakdown, you may be interested in other videos in this Kubernetes in 30 days playlist. I’m also adding a new series called Cloud-Native AI from Zero to Production to this playlist in the coming weeks. Now, what you’re watching is episode 1. So, subscribe to my YouTube channel and my newsletter if you’re interested!  I’ll see you in the next one!