Kubernetes is the foundation of cloud-native infrastructure
Kubernetes is the control system for modern cloud computing. It orchestrates everything, automating how companies deploy and manage software across data centers and clouds. That’s why nearly 84% of organizations are either running Kubernetes in production or actively evaluating it (CNCF 2023 survey).
What makes Kubernetes powerful is its flexibility. It’s not locked into one function—it manages CI/CD pipelines, machine learning models, global-scale databases, and much more. Companies use it to run everything from financial transactions to AI-driven analytics. And because it’s open-source, it keeps evolving. Thousands of developers contribute to it, making it better, faster, and more secure.
Kubernetes is not simple though. It’s incredibly capable, but also incredibly complex. It gives you full control, but with great power comes great responsibility. That’s why so many companies are shifting to managed Kubernetes services like Amazon EKS, Azure AKS, and Google GKE. These platforms handle the complexity for you, so you don’t have to hire an army of engineers just to keep things running.
“If your company is serious about scalability, automation, and resilience, Kubernetes is the future. But the question is, can it become easier to use?”
Usability is getting better, but still complex
Kubernetes today is powerful, but not yet user-friendly enough for the masses. It’s gotten much easier over the years, thanks to managed services and automation tools. Companies can now deploy applications without dealing with all the gritty details of configuring servers, networking, and storage manually.
But complexity hasn’t disappeared—it’s just been shifted. Instead of struggling with infrastructure, businesses now struggle with how to use Kubernetes effectively. The ecosystem is massive, with hundreds of tools, each solving a different problem.
For example, Kubernetes Operators have made running complex workloads—like databases—much easier by automating repetitive tasks. But setting them up still requires deep expertise. While security has improved, it’s still not “plug-and-play”. Misconfigurations happen all the time, leading to performance issues and security risks.
The data backs this up. 84% of companies are now moving away from self-managed Kubernetes and into managed services (State of Kubernetes 2023 survey). The message is clear: Kubernetes is becoming essential, but the industry is still working on making it accessible to everyone.
Complexity and the hidden cost of innovation
Kubernetes is a beast. It’s designed to handle global-scale applications, which means it comes with an overwhelming number of options and settings. That’s fine if you’re running thousands of servers, but for most businesses, it’s too much overhead.
75% of Kubernetes practitioners report ongoing issues with running their clusters (Spectro Cloud 2023 report). That number is up from 66% in 2022, meaning things are getting harder, not easier. Why? Because Kubernetes isn’t just about deployment anymore. It’s about multi-cloud, security, monitoring, automation, scaling, compliance, and everything in between.
One of the biggest challenges is multi-cluster management. Many enterprises run 10 or more Kubernetes clusters, each requiring coordination and optimization. Without the right tools, managing them is a nightmare. Even experienced teams struggle with configuring resource limits, network policies, and storage settings correctly.
Then there’s security. Kubernetes requires strict access controls, continuous vulnerability scanning, and compliance management. If misconfigured, it can expose your company to cyber threats. Even seasoned DevOps teams make mistakes because security in Kubernetes is not intuitive.
And then there’s the skills gap. Kubernetes is not as widely understood as traditional virtualization technologies like VMware. Many IT teams resist adopting it simply because they lack the expertise. That’s why companies are either hiring Kubernetes specialists or outsourcing to cloud providers.
“Kubernetes is the future, but right now, it’s complicated and resource-intensive. If companies want the benefits without the headaches, they need better abstraction, automation, and smarter tooling.”
Kubernetes is expanding beyond containers
At first, Kubernetes was just about managing containers—the building blocks of modern software. But now? It’s becoming the operating system for the cloud.
One of the biggest shifts is support for virtual machines (VMs). Enterprises have relied on VMware and other virtualization platforms for decades, and Kubernetes is now absorbing that world. With KubeVirt, companies can run legacy applications inside Kubernetes clusters, modernizing without needing a full rebuild.
Why does this matter? Because most enterprises still run on legacy workloads. Banks, automotive companies, and industrial firms depend on applications built 10, 20, even 30 years ago. These workloads can’t just be containerized overnight—they require careful migration. Kubernetes is now positioned to support both old and new applications, making it the go-to infrastructure for large-scale enterprises.
Another major shift? Kubernetes is evolving into a multi-cloud control plane. That means businesses can run applications across multiple cloud providers—AWS, Google Cloud, Microsoft Azure—without being locked into one. This flexibility is critical as enterprises try to reduce dependency on a single vendor.
With great power, however, comes complexity. Running legacy workloads in Kubernetes means dealing with data migration, disaster recovery, and new security challenges. Enterprises need strong Kubernetes expertise to make this transition smooth.
As Murli Thirumale, General Manager of Portworx, points out, this is a huge step forward. Kubernetes is no longer just for cloud-native startups—it’s now a serious contender for enterprise IT modernization.
AI is driving Kubernetes to the next level
AI is a game-changer for Kubernetes. The sheer scale of AI workloads—massive data sets, high-performance computing, and dynamic resource allocation—makes Kubernetes the perfect match. But the problem is that Kubernetes wasn’t designed for AI. It’s being forced to evolve to meet the needs of AI engineers, data scientists, and machine learning teams.
AI workloads demand fast, scalable, and automated infrastructure. Training an AI model can require hundreds of GPUs, and once trained, these models must be deployed efficiently for real-time inference. Kubernetes can handle this, but not out of the box. Teams have to build custom AI pipelines, deploy additional services, and manually optimize workloads.
This is where Kubeflow comes in. Kubeflow is an open-source project built to simplify machine learning model training and deployment on Kubernetes. It abstracts away the infrastructure headaches, letting AI teams focus on building models instead of configuring clusters. Companies like CERN, Red Hat, and Apple are already using Kubeflow to streamline AI workloads.
And then there’s AI inference—where models process real-world data in production. Kubernetes is well suited for this, but scalability remains a challenge. Some workloads need to be distributed across multiple cloud regions, while others run on edge devices with limited compute power. This is where KServe steps in, providing a framework-agnostic way to deploy AI models at scale.
As Chase Christiansen, Staff Solutions Engineer at TileDB, explains, the challenge is deploying AI on Kubernetes while making it reliable, scalable, and cost-efficient. The tools are improving, but there’s still work to be done. AI and Kubernetes will keep converging, but for now, the ecosystem is still too fragmented.
Kubernetes is expanding to the edge, but challenges remain
Kubernetes is moving beyond being used in data centers to “the edge”. That means running software in remote locations, factories, retail stores, and even military operations. Why? Because some workloads can’t afford the latency of sending data to the cloud and back.
Take the U.S. Department of Defense. They’re deploying Kubernetes in air-gapped environments—battleships, F-16 jets, and secure military bases—where there’s zero internet connectivity. Retail chains are using Kubernetes at the store level to process customer data in real time. IoT manufacturers are embedding lightweight Kubernetes distributions into smart devices and sensors.
But the challenge is that edge computing is a completely different beast. Instead of running a few large clusters, companies now have thousands of small Kubernetes clusters. Managing them is a logistical nightmare.
This is where lightweight Kubernetes distributions like K3s and Bottlerocket come in. These stripped-down versions of Kubernetes are designed for low-power, resource-constrained environments. Platforms like AWS Fargate and MicroShift are providing serverless compute options for edge workloads.
But managing thousands of edge clusters still requires centralized orchestration. As Raghu Vatte, Field CTO at ZEDEDA, puts it, “You don’t want to rebuild an application just because you’re deploying it on the edge.” Standardization across cloud and edge is key.
“The edge is the next frontier for Kubernetes, but the tooling is still catching up. Companies need better automation, security, and network resilience to make large-scale edge deployments practical.”
Security and compliance are weak links in the chain
Security in Kubernetes is vital, but still a work in progress. The power of Kubernetes also makes it a target for hackers. One misconfiguration can expose entire infrastructures to attack, making security a top concern for enterprises.
Here’s the reality: most Kubernetes security failures aren’t from the software itself—they’re from human error. Companies often fail to:
- Implement Role-Based Access Control (RBAC) properly.
- Set up network policies to restrict internal traffic.
- Regularly scan for container vulnerabilities.
And that’s before we get into compliance. For industries like finance, healthcare, and government, Kubernetes must meet strict security requirements. That means:
- NIST’s Federal Information Processing Standards (FIPS) for encrypting data.
- FedRAMP Vulnerability Scanning Requirements for cloud security.
- SOC 2 and HIPAA compliance for handling sensitive data.
As Gaurav Rishi, VP of Product at Kasten by Veeam, explains, security has improved dramatically in the last five years, but compliance is still a major roadblock. Enterprises want the flexibility of Kubernetes but need enterprise-grade security to go with it.
This is why tools like Kubernetes-native security platforms, policy enforcement engines, and automated compliance frameworks are growing fast. But security remains an ongoing battle, and companies that ignore it will learn the hard way.
Kubernetes is the future, but usability must catch up
Kubernetes is here to stay. It’s already the standard for cloud-native applications, and its influence will only grow. By 2025, Gartner predicts that 95% of applications will be deployed on cloud-native platforms. That means Kubernetes is inevitable.
But the harsh reality is that Kubernetes is still too complex. Companies are adopting managed services, automation tools, and platform engineering approaches to make it easier, but the learning curve is still steep.
The cloud-native ecosystem needs better abstractions, so developers and enterprises can use Kubernetes without needing to be Kubernetes experts. This means:
- Smarter automation for scaling, security, and performance tuning.
- More intuitive developer tools that don’t require deep Kubernetes knowledge.
- Platform engineering teams that build internal Kubernetes-based platforms for developers.
The good news is that the industry is moving in the right direction. AI is making Kubernetes smarter, security tools are getting more automated, and managed services are making it more accessible.
“As Andreas Grabner, DevOps activist at Dynatrace, points out, AI-driven observability tools are already making it easier to diagnose and optimize Kubernetes clusters automatically. This is just the beginning.”
Kubernetes is the foundation of cloud computing, but it needs to get easier. Companies that invest in Kubernetes now will be in a prime position to dominate the next wave of cloud innovation. The only question is—will Kubernetes become simple enough before the complexity drives businesses away?
Time will tell.
