Kubernetes has transformed how modern organizations deploy and operate scalable infrastructure, and the hype around automated cloud-native orchestration has made its adoption nearly ubiquitous over the past 10+ years. Yet behind the scenes, most teams embarking on their Kubernetes journey quickly encounter operational complexity, configuration challenges, and costly maintenance that few vendors highlight.
Drawing from years of real-world experience architecting, building, and maintaining Kubernetes, we recently hosted a webinar sharing five hard-earned lessons to help organizations get started using the container orchestration tool. In this post, we’ve paired each lesson with useful resources and examples of how to navigate managing Kubernetes at scale, whether supporting your own teams, deploying across multiple clusters, or seeking outsourced expertise through managed Kubernetes-as-a-Service.
The Kubernetes community knows that spinning up a cluster is straightforward, especially if you use a managed provider such as AKS, EKS, or GKE. But in reality, running a production environment means managing all the hidden add-ons: DNS controllers, networking, storage, monitoring, logging, secrets, security, and more. Supporting internal users (dev teams, ops, and data scientists) adds significant overhead for any company running Kubernetes.
Internal Slack channels are often flooded with requests, driving the rise of platform engineering and developer self-service solutions to reduce overhead. Of course, someone on the backend needs to have created all the capabilities to make it easy for developers to deploy their applications, and every layer of abstraction affects support and troubleshooting. As more complexity is hidden from developers, it becomes harder for them to debug issues independently. Successful teams strike a careful balance between usability and transparency.
Managed platforms and cloud vendors promise quick cluster creation, which is true — it’s quick and easy to spin up a cluster. But these clusters are rarely ready for real workloads. They lack hardened security, proper resource requests and limits, key integrations, and monitoring essentials.
Production readiness means planning server access, role-based access control (RBAC), network policy, add-ons, CI/CD integration, and disaster recovery before deploying a single business application. Deploying a secure, production-ready Kubernetes environment requires careful attention to configuration details and resource specifications. Getting these details right protects both your system and your client data.
Default settings are almost never secure. In fact, you’re going to need to think about what kind of cluster access you want to give — and which people you want to give access to. You’ll have to create RBAC permissions, RBAC roles, and cluster roles. Specs may look simple, but clusters contain multiple objects with confusing names. Understanding them is required for true security. Keep in mind: Kubernetes lacks built-in identity and access management (IAM), so you must carefully manage access permissions and endpoint exposure.
All this complexity often leads to overprovisioned built-in cluster roles, giving users far more permissions than necessary. These days, cloud providers are making some of this a lot easier by integrating IAM and RBAC tools, like AWS IAM Roles for Service Accounts (IRSA), into their authentication mechanisms.
While Kubernetes namespaces offer logical isolation, by default there is no network separation. True isolation requires explicit network policies and a compatible CNI, which means planning, testing, and ongoing tuning.
Despite a lot of news about software supply chain issues, many organizations still don’t scan container images before using them. Pulling container images from public registries is convenient, but it also introduces potential risks. Images are usually built of many layers of other images, and they can contain vulnerabilities.
Always scan, validate, and track the provenance of every image — understand where images come from, how they’re built, and who maintains them. Establish a plan to mitigate any vulnerabilities you discover during scanning. Some vendors offer patched, secure images by subscription, reducing your team’s burden when it comes to CVE mitigation and vulnerability management.
Kubernetes excels at scaling. You no longer need to manually provision new servers or manage spike-time connections. Kubernetes handles that complexity automatically.
The initial setup is deceptively simple: dropping in a Cluster Autoscaler and a Horizontal Pod Autoscaler (HPA) and telling them to go. But this simplicity hides two major considerations that, if ignored, lead to problems: runaway costs and inconsistent performance.
Node autoscalers are essential for elasticity but can create serious financial risk if not properly bounded. Always set upper limits to prevent runaway cloud bills and oversized, expensive nodes. Also, without explicit guidance on instance families, tools like Karpenter can select expensive, oversized nodes. This common mistake can lead to teams celebrating high availability without realizing they are also incurring massive costs.
The HPA is easy to deploy, but choosing the right metric is difficult. The simplest route is scaling based on generic metrics like CPU and memory. However, this is rarely accurate, as most modern applications are not truly bottlenecked by these resources. Effective, cost-efficient scaling requires moving to custom metrics, such as requests per second or queue size. This is more complex to implement, but it provides a clear, accurate reflection of application load, preventing over-scaling (and over-paying) while ensuring consistent user performance.
Ultimately, these scaling components are not isolated. They form a complex mesh that ties your Pod's resource settings to the Cluster Autoscaler's decisions. The biggest lesson here is realizing that Kubernetes makes distributed computing accessible, but you need to configure these automated systems carefully to make them work together effectively.
The foundational lesson in managing Kubernetes is that people are your most critical resource. Kubernetes is a complex piece of infrastructure that has only been widely adopted for a handful of years. This brief lifespan, combined with the technology’s depth, means:
It’s also important to understand the difference between knowing Kubernetes and operating it at scale. While running a Kubernetes cluster on a local machine or as a hobby project is great for learning, it does not translate to the production demands of a cloud platform. Real-world experience involves managing upgrades, ensuring stability, controlling costs, and dealing with complex integrations.
Without genuine K8s expertise, organizations risk stagnation. We've seen environments where the entire cluster was deployed via rudimentary batch scripts written by a single developer. When that person left, no one felt comfortable upgrading the system, leaving the company stuck on an outdated version for years. Does your team have the requisite experience to operate and maintain this platform, or have you budgeted for the resources necessary to manage it for you?
While moving to the cloud and Kubernetes eliminates the need to upgrade physical servers or operating systems, it introduces a new form of technical debt centered on the evolving ecosystem.
This debt manifests in two primary ways.
You must constantly manage updates to maintain security and stability:
This work takes significant, dedicated time for research, testing, and deployment. When teams are occupied with developer support and troubleshooting, upgrade work is frequently delayed. Tech debt piles up until a CVE forces a massive, risky, and time-consuming jump across several versions at once.
Beyond upgrading existing tools, the Kubernetes ecosystem itself is always evolving, introducing better patterns that render older approaches obsolete or deprecated.
If your team isn’t dedicating time to tracking new CNCF projects and assessing whether new tools solve old problems, you risk becoming locked into a deprecated tool that stops receiving important security patches, forcing a chaotic, emergency migration. Staying secure and reliable requires constant awareness of the ecosystem.
Teams often adopt Kubernetes before asking if their business needs justify its complexity. But some workloads benefit more from simple hosting or a dedicated VM. There’s no reason to run a personal blog, simple data pipeline, or one-off batch job on Kubernetes just because it’s trendy.
Start with business needs and use Kubernetes where it solves real problems. Avoid the temptation to deploy “service mesh everywhere” or use “Kubernetes by default.” Focus on outcomes, simplicity, and efficiency.
When working with Kubernetes, the beauty of its declarative API is the ability to enforce all security and best practice rules as structured policy.
There are a number of open source policy engines to manage this, including:
The most critical takeaway is to enable these policy engines from the beginning. If you start deploying apps on a bare-bones cluster and then implement a policy engine later, you’ll suddenly block insecure deployments. This will lead to frustration and resistance because the devs’ established (but insecure) workloads will no longer be allowed to run.
Kubernetes is a game-changer for infrastructure, but sustainable adoption requires practical knowledge, ongoing investment, and a willingness to seek help when needed. By learning from those who’ve managed hundreds of clusters at scale, and embracing community and expert support, organizations can build robust, secure, and cost-effective Kubernetes environments that empower innovation rather than inhibit it.
To truly succeed with Kubernetes, whether self-managed or outsourced, organizations should:
If your team is planning a new Kubernetes deployment or struggling with production stability, these lessons are a foundation for long-term success.
If you’re ready to eliminate operational headaches and focus on value, consider Fairwinds Managed Kubernetes-as-a-Service for clarity, reliability, and expert support in today’s cloud-native world.
Watch the full webinar on demand: 5 Things I Wish I Knew Before Managing Kubernetes.
