Kubernetes has become the default assumption for container orchestration. Teams adopt it because that’s what everyone seems to use, because it looks good on resumes, because “we might need to scale.”
But Kubernetes is complex. Running it well requires significant investment. For many workloads, that investment doesn’t pay off.
What Kubernetes Actually Solves
Kubernetes addresses real problems—at certain scales. Understanding what those problems are helps clarify whether you actually have them.
Container orchestration across machines. When you have enough containers that managing them manually becomes impractical, Kubernetes automates scheduling, placement, and resource allocation. The platform decides which physical machines run which containers, balances load across your infrastructure, and handles the complexity of resource constraints. This matters when you’re managing dozens or hundreds of containers; it’s overhead when you have five.
Service discovery and networking. In dynamic environments where containers come and go—scaling up and down, failing and restarting—something needs to help services find each other. Kubernetes handles internal DNS automatically, routes traffic to healthy instances, and provides network policies for controlling communication between services. This solves real problems when your service topology changes frequently; it’s complexity you don’t need when your services run on predictable infrastructure.
Self-healing and scaling. Kubernetes restarts failed containers, replaces unresponsive nodes, and scales workloads based on demand—all automatically based on rules you define. When a container crashes, Kubernetes notices and starts a new one. When load increases, it can add capacity. When a node fails, it redistributes workloads elsewhere. These capabilities are genuinely valuable for production systems that need high availability without constant human intervention.
Declarative configuration. Rather than issuing commands to change your infrastructure, you describe the desired state and Kubernetes works to achieve and maintain it. If reality drifts from the declared state—containers die, nodes fail, deployments update—Kubernetes continuously reconciles the difference. This model is powerful for managing complex systems, but it also requires learning to think about infrastructure differently.
These capabilities matter when you’re running dozens of services across multiple machines with complex scaling requirements. The question is whether that description matches your situation.
Signs You Don’t Need It
If any of these describe your situation, simpler alternatives probably make more sense. Being honest about where you actually are—rather than where you aspire to be—saves significant time and frustration.
You have a handful of services. Three services don’t need an orchestrator. Docker Compose running on a single well-configured server, a straightforward deployment script, or a managed container service handles this scenario with far less overhead. Kubernetes adds value through coordination at scale; when there’s not much to coordinate, that value doesn’t materialize while the complexity remains.
Your team is small. Kubernetes requires specialized knowledge that takes meaningful time to develop. The platform has a large surface area—pods, deployments, services, ingress controllers, persistent volumes, RBAC, network policies, and much more. If you don’t have someone who understands these concepts deeply, your team will spend more time fighting the platform than benefiting from it. That time could be spent building product instead.
You’re not actually scaling dynamically. If your workload is predictable—traffic patterns are consistent, capacity needs are stable—and you’re not adding or removing compute capacity regularly, Kubernetes’s intelligent scheduling isn’t helping you. The platform excels at managing dynamic environments; if your environment isn’t dynamic, you’re paying complexity costs without receiving the corresponding benefits.
Managed services cover your needs. AWS ECS with Fargate, Google Cloud Run, Azure Container Apps—these platforms handle containerized workloads without exposing you to Kubernetes complexity. They have constraints: less flexibility, specific limitations on configuration, sometimes higher per-unit costs. But those constraints often don’t matter for your specific use case, and what you give up in flexibility you gain in operational simplicity.
Your bottleneck isn’t infrastructure. If you’re not deploying frequently, if development time is the constraint, if the limit on your velocity is product decisions or engineering capacity rather than infrastructure capability, adding infrastructure complexity makes things worse, not better. Kubernetes doesn’t speed up development; it enables certain operational patterns that matter at certain scales.
The Hidden Costs
Teams underestimate what Kubernetes actually requires. The learning curve and operational burden are both substantial, and they persist even after initial deployment is complete.
Learning curve. Kubernetes has a large surface area that takes real time to understand well. The core concepts—pods, deployments, services, ingress, persistent volumes—are just the beginning. Understanding RBAC for security, network policies for traffic control, resource requests and limits for stability, and the interaction between all these components takes months of dedicated learning. You can get something running quickly; running it well requires depth.
Operational overhead. Kubernetes clusters require ongoing care, even when using managed offerings like EKS, GKE, or AKS. Cluster version upgrades need planning and testing—Kubernetes releases new versions regularly and deprecates old ones. Node management involves patching, rotating, and scaling the underlying compute. Certificate rotation, monitoring setup, logging infrastructure, and backup procedures all need attention. The “managed” part of managed Kubernetes handles some of this, but less than most teams expect.
Debugging complexity. When something breaks in a Kubernetes environment, you’re debugging at multiple layers simultaneously. Is the problem in your application code? The container configuration? The pod spec? The deployment? The service routing? The ingress controller? Network policies blocking traffic? Resource constraints causing evictions? The failure modes multiply compared to simpler deployment models, and understanding where to look requires familiarity with all those layers.
YAML sprawl. Kubernetes configurations are verbose by design—explicitness is a feature, not a bug. But a simple application easily requires separate YAML files for deployment, service, ingress, configmap, secrets, and potentially more. Managing this configuration becomes its own challenge: keeping environments in sync, preventing drift, reviewing changes, understanding what configuration actually applies to what workload.
Tooling ecosystem. Kubernetes alone rarely suffices for production use. You’ll likely add Helm or Kustomize for templating and managing configuration across environments. ArgoCD or Flux for GitOps-style deployment automation. External-secrets or sealed-secrets for secret management. Cert-manager for automated certificate handling. Prometheus and Grafana for monitoring. Each tool in this ecosystem has its own learning curve, upgrade path, and maintenance requirements. The full platform you build around Kubernetes can dwarf Kubernetes itself in complexity.
Simpler Alternatives
For many workloads, simpler deployment options work better and let you focus on building product rather than managing infrastructure. Each comes with tradeoffs, but for many applications those tradeoffs are acceptable.
Managed container services. AWS App Runner, Google Cloud Run, and Azure Container Apps represent a different philosophy: you provide a container, the platform handles everything else. Scaling is automatic. Networking is configured. HTTPS certificates are managed. You don’t think about nodes, pods, or ingress controllers. The tradeoff is flexibility—these platforms have opinions about how applications should work, and fighting those opinions is frustrating. But if your application fits their model, the operational simplicity is substantial.
AWS ECS with Fargate. ECS occupies a middle ground: container orchestration with more flexibility than serverless platforms but without Kubernetes’s complexity. You define tasks and services in terms that make sense for your application. Fargate eliminates server management entirely—you don’t provision or manage EC2 instances. For teams that need more control than Cloud Run provides but don’t need Kubernetes’s full feature set, ECS with Fargate often hits a sweet spot.
Single-server deployment. For applications that don’t need horizontal scaling—and many applications don’t—a well-configured server with Docker Compose handles deployment simply and reliably. This isn’t glamorous architecture, but it’s easy to understand, easy to debug, and easy to maintain. Add a load balancer in front if you need redundancy or want seamless deployments. Many successful applications run this way indefinitely.
Platform-as-a-Service. Heroku, Render, Railway, and similar platforms abstract away infrastructure entirely. You push code; they handle building, deploying, scaling, and operations. The flexibility is limited—you can’t do everything you might want—but the operational overhead is minimal. For many applications, especially early-stage products where development velocity matters more than infrastructure optimization, PaaS platforms provide excellent value.
When Kubernetes Makes Sense
Kubernetes is the right choice in specific situations. When these conditions genuinely apply, the platform’s complexity becomes justified investment rather than unnecessary overhead.
Genuine scale. When you’re running dozens of services across multiple machines with dynamic scaling requirements, Kubernetes’s orchestration capabilities earn their complexity cost. The platform excels at managing heterogeneous workloads—some services need to scale based on CPU, others on memory, others on custom metrics. Multi-region deployments with consistent management patterns become more tractable. At this scale, simpler alternatives break down or require building Kubernetes-like capabilities yourself.
Specific Kubernetes features. Custom controllers, operators, and sophisticated scheduling requirements exist because some problems genuinely need them. If your workload requires placement constraints that consider node hardware, or you’re building a platform that provisions resources dynamically, or you need the extensibility that custom resource definitions provide—these features don’t exist in simpler tools because simpler tools don’t solve these problems. If you need what Kubernetes uniquely provides, simpler alternatives won’t work regardless of their other merits.
Standardization across environments. If you’re running consistent deployments across multiple clouds, or managing hybrid infrastructure that spans on-premises data centers and cloud providers, Kubernetes provides a common abstraction layer. You can use the same deployment configurations, the same tooling, the same operational procedures across different underlying infrastructure. This standardization has genuine value for organizations operating in multiple environments.
Team with Kubernetes expertise. If you already have people who know Kubernetes well—deeply, not just surface familiarity—the learning curve is sunk cost. The calculus changes: you’re not comparing “Kubernetes complexity” against “simpler alternative simplicity,” you’re comparing “capabilities your team can deliver with Kubernetes” against “the learning curve of a different approach.” Existing expertise has real value.
Questions to Ask
Before adopting Kubernetes, honestly answer these questions. The emphasis is on honesty—it’s easy to rationalize adoption based on aspirational scale or hypothetical requirements rather than current reality.
- How many services are we actually running? Not how many we might have someday, but how many exist today and will exist in the near term. Three services don’t benefit from container orchestration the way thirty do.
- Are we scaling dynamically or is capacity predictable? If your traffic patterns are stable and you can predict capacity needs, Kubernetes’s dynamic scheduling provides little value. The platform shines in environments that genuinely change.
- Who will operate the cluster and do they have the expertise? Kubernetes doesn’t operate itself. Someone needs to handle upgrades, troubleshoot issues, manage the tooling ecosystem, and respond when things break. If that expertise doesn’t exist on your team, you’re committing to either hiring it, developing it, or suffering through operational difficulties.
- Have we evaluated managed alternatives for our specific workload? Before deciding Kubernetes is necessary, test whether simpler options actually fail to meet your requirements. Often, perceived limitations turn out not to matter for your specific use case.
- What problem are we solving that simpler approaches can’t address? This is the crucial question. If you can’t articulate a clear, specific answer—not “we might need to scale” but “we need X capability that only Kubernetes provides”—Kubernetes probably isn’t the right choice yet.
If you can’t articulate a clear answer to that last question, it’s worth reconsidering. You can always adopt Kubernetes later when complexity is genuinely necessary; it’s harder to un-adopt it once you’ve built around it.
The Real Pattern
Most applications that “need Kubernetes” actually need reliable container deployment with automatic scaling. Managed services—Cloud Run, App Runner, ECS with Fargate—provide exactly this with far less overhead for the majority of workloads. The features that differentiate Kubernetes from these alternatives matter intensely for certain use cases and not at all for others.
Kubernetes is an excellent tool for teams that genuinely need its power and can invest in operating it well. The organizations that benefit most have substantial scale, specific technical requirements that simpler platforms can’t meet, or existing deep expertise. For everyone else, simpler options let you focus on building product instead of managing infrastructure—which is usually the higher-value activity.
The best infrastructure is the least infrastructure that meets your needs reliably. Sometimes that means Kubernetes, with its complexity earning its keep through capabilities you actually use. Often it means something simpler, freeing your team to work on problems that are unique to your business rather than problems that platforms have already solved.