Kubernetes has quickly made itself known as the de facto platform for today’s applications and the most common way to build an infrastructure platform for application developers. Kubernetes offers immense flexibility and power, but it can introduce its own unique set of operational challenges. If you find yourself spending more time chasing down cluster issues than helping your developers work hassle-free, this guide is for you. Think of this as your practical field manual: a structured approach to identifying common Kubernetes pain points and how you can address them effectively with tools like Komodor. The Rise of Platform Engineering Platform engineering as a practice isn’t new. In fact, it dates back several years to the rise of DevOps and the principles of infrastructure automation. The arrival of Kubernetes around 2014 significantly reshaped platform engineering. With Kubernetes came automated infrastructure provisioning, enhanced observability, and better troubleshooting. Essentially, Kubernetes standardized the deployment and management of containerized applications at scale, fundamentally changing how we manage infrastructure. On the one hand, Kubernetes accelerated the rise of platform engineering. But, at the same time, it has introduced new challenges due to its complexity. Your teams may find themselves negotiating a steep learning curve, juggling YAML configurations, or spending too much time tuning clusters to deal with problematic runtime issues. Visibility into what's happening inside the cluster is often limited or fragmented, making troubleshooting slow and reactive. And, of course, this leaves you with frustrated developers and slower delivery cycles. Enter Komodor. Komodor helps by turning Kubernetes into a system you can manage and control. It pulls together data from all your clusters—on-prem, cloud, hybrid, you name it—and brings it into a centralized, intuitive UI. Instead of piecing together clues from scattered sources, you get context from a full-stack view with clear, actionable insights. You can see what changed, when it changed, what the impact was, and how to fix it. Typical Kubernetes Challenges and How Komodor Can Help Here are some of the most common — and painful — Kubernetes challenges, along with tips on how to tackle them. No Real-time Centralized View Do you find yourself juggling operations in a large-scale complex environment—whether multicloud, on-prem, hybrid, cluster fleets, or edge deployments? Does each environment use its own observability stack, permission model, and dashboard? When an issue like a degraded service or failed deployment shows up, it can take multiple tools just to gather the basic context. In short, debugging across environments is manual, error-prone, and slow. Best practice: Set up a centralized operational view that cuts across cloud providers and infrastructure layers. Find a tool that can provide a single unified view that brings everything together—regardless of where it lives or how it’s configured. No more switching between tabs or hunting for credentials. You’ll regain control, resolve issues faster, and confidently scale operations without losing visibility. You Know Something’s Wrong But Not What or Why Traditional monitoring tools flood you with raw data—metrics, logs, events, and traces—but rarely surface the root cause. They tell you something’s wrong — but not what, why, or what to do next. Without Kubernetes-specific intelligence to help you pinpoint the source of the problem, you’re stuck piecing the story together. Best practice: Use platforms that enrich your raw data using AI and contextual analytics to offer you actionable intelligence. You can shorten your mean-time-to-resolution with a tool based on deep domain expertise that goes beyond MELT (metrics, events, logs, traces). Ideally, you want AI insight that automatically connects the dots between symptoms and causes—to flag misconfigured rollouts, bad Helm releases, or repeatedly crashing pods. Troubleshooting is Brutal Without Full-Stack Context What if the root cause of your issues lies below the application layer: in certificate renewal failures, misconfigured autoscalers, policy enforcement gaps, or broken service mesh rules? Without full-stack visibility, diagnosis is guesswork. You need contextual insight across the stack, from auto-scaling and policies to networking, streaming, storage, workflows, and more. Best practice: Establish deep visibility across the full Kubernetes ecosystem, not just the core app. Single-pane-of-glass visibility will enable you to correlate your deployments, configuration changes, and incidents with contextual insights and optimizations. Superior Kubernetes Management platforms will incorporate add-ons like Karpenter, Redis, Istio, and CI/CD systems, to give you a complete picture for fast and confident troubleshooting. Reactive Firefighting Is Undermining Reliability Recurring incidents and manual triage are signs that you’ve got deeper system issues. When engineers repeatedly fix the same problems—like node crashes from missing resource limits—you’re stuck in reactive mode. Best practice: Proactive prevention and continuous optimization should be your reality, not something on your wish list. Invest in a tool that powers a continuous feedback loop for optimized reliability and performance. It should be able to flag risky patterns pre-deployment (e.g., missing probes, CPU hogs), analyze runtime anomalies, and surface long-term trends for optimization. By setting up guardrails and automatic detection, you can prevent recurring issues and reduce firefighting. Workspace Overload Is Hurting Developer Experience Not everyone on your team needs cluster-wide access or wants to sift through every single Kubernetes resources. Your developers just want to know if their app is working, and the data engineers need to debug data workflow pipelines without getting a crash course in Helm. Ideally, every role should have access to a workspace that reduces cognitive load and bubbles up only what matters. Best practice: Implement tailored workspaces and role-specific dashboards. When you reduce the ‘noise’, everyone sees only what is relevant to their services and applications. Developers can zero-in on service health, restart history, and actionable insight. Operators get deep control to manage system-wide resources, while data engineers get relevant insights. Cognitive load drops, collaboration is smoother, self-service rises, and tickets go down. Managing Access is a Growing Governance Headache Giving people the right access and keeping it up to date across multiple clusters and teams can become incredibly complex. Mistakes open up security risks, auditing becomes painful, and you may be looking at custom scripts or old tools to enforce policies. Is it possible to achieve governance without the headache? Best practice: Adopt a simplified access management. Focus on a tool that has built-in RBAC, SSO integration, just-in-time privileges, policy enforcement, and full audit capabilities. You want a single interface from which you can control and govern who has access to what, when, and why—without custom scripts or third-party tools. Overprovisioning is Driving Up Costs Overprovisioning is one way to avoid risk, but it’s also expensive and wasteful. On the flip side, right-sizing is difficult to do with confidence, especially without accurate platform-wide visibility. The stand-alone cost solutions available lack an in-depth understanding of your KPIs so they can’t provide nuanced optimization. Mostly, you either waste money or risk degraded performance. Do you know what fraction of your resources are being used? Best practice: Your platform tool should be able to analyze the real-world behavior of your apps and infra to provide actionable remedies. As opposed to raw numbers, you should be getting smart suggestions based on actual usage and context. This will help you find the right balance between cost and performance, without guesswork. From Maintenance to Momentum To make life with Kubernetes manageable: Focus on proper planning and strategy by conducting thorough assessments of your current infrastructure and applications. Look for Kubernetes-based platforms that provides automatic insights to help you get to the source, see the evidence, and remediate the situation. Implement tailored workspaces and role-specific dashboards to reduce confusion. Invest in centralized access management that lets you control and govern who has access to what and why. Find a tool that ‘understands’ the context and actual usage of your resources so you can correctly balance between cost and performance, without under- or over-provisioning. Invest in education and collaboration so your teams stay up-to-date with best practices and new developments in the Kubernetes ecosystem. Implement tools like Komodor to simplify all your platform management; they provide valuable insights and capabilities to help manage, monitor, and troubleshoot Kubernetes resources. Your goal as a platform engineer is not just to keep systems afloat, but to create an environment that is scalable, reliable, and empowering for developers. With Kubernetes at the core, this vision can become a reality—provided you have the right tools to manage the operational load. When issues arise, Komodor surfaces root causes fast, with suggestions on how to fix the problem. No more guessing. No more sifting through too much data. Engineers can triage issues with confidence, without worrying they will break something or make things worse. And because Komodor uses AI to track and visualize changes over time, it can help you spot patterns, optimize configs, create guardrails, and prevent repeat incidents. By understanding the challenges and leveraging tools like Komodor, you can successfully navigate day two operations and take full advantage of the opportunities that Kubernetes provides.
