Kubernetes or Something Simpler? A Solo DevOps Engineer's Guide to Container Orchestration in 2024

For the solo DevOps engineer or the two-person platform team supporting a growing application, few infrastructure decisions carry more long-term consequence than the choice of container orchestration strategy. Kubernetes has become the industry default — referenced in job postings, celebrated at KubeCon, and assumed as a baseline in most cloud-native architecture discussions. But default is not synonymous with appropriate, and the operational burden of running Kubernetes correctly is substantial enough that choosing it without careful consideration can consume the very engineering capacity it was meant to free up.

This guide is written for practitioners who need a clear-eyed comparison, not a vendor pitch. It examines Kubernetes alongside Docker Swarm, HashiCorp Nomad, and managed cloud-native alternatives, with the goal of helping individual operators make a decision calibrated to their actual workload and team size.

Why the Kubernetes Default Deserves Scrutiny

Kubernetes was originally designed to manage workloads at Google's scale. Its architecture reflects that origin: a control plane composed of multiple components, a declarative API surface spanning hundreds of resource types, and an ecosystem of add-ons — ingress controllers, certificate managers, secret operators, CNI plugins — that each introduce their own configuration surface and failure modes.

For a team of 50 engineers running hundreds of microservices, that complexity is justified by the operational leverage Kubernetes provides. For a solo engineer running eight services across two environments, the calculus looks very different. The time spent managing etcd backups, debugging coredns failures, and keeping cluster components patched is time not spent improving the application.

This is not an argument against Kubernetes categorically. It is an argument for evaluating it honestly.

The Alternatives Worth Considering

Docker Swarm

Docker Swarm remains a viable option for operators who are already comfortable with Docker Compose workflows and need basic orchestration without significant retraining. Swarm uses a familiar YAML syntax, handles service scaling and rolling updates, and integrates natively with Docker networking primitives.

Its limitations are real. The project has not received significant development investment in recent years, and its ecosystem of third-party integrations is narrow compared to Kubernetes. Swarm lacks native support for advanced scheduling constraints, has limited observability tooling, and does not support the CRD-based extensibility that makes Kubernetes adaptable to specialized workloads.

Best fit: Small teams running stateless web services on a single cloud provider or on-premises hardware, where operational simplicity is the primary concern and workload complexity is low.

HashiCorp Nomad

Nomad occupies a distinctive position in the orchestration landscape. Unlike Kubernetes, it is not exclusively a container scheduler — it can manage containerized workloads, raw binaries, Java applications, and batch jobs within a single unified control plane. Its architecture is deliberately simpler than Kubernetes: a single binary for both client and server, straightforward job specification files, and a smaller operational footprint.

For solo engineers managing heterogeneous workloads — perhaps a mix of containerized services, legacy Java applications, and periodic batch processing — Nomad's flexibility is genuinely valuable. It integrates cleanly with other HashiCorp tools including Vault for secrets management and Consul for service discovery, which matters for teams already invested in that ecosystem.

Nomad's community is smaller than Kubernetes', and its managed hosting options are more limited. Engineers who encounter edge cases may find fewer Stack Overflow threads and GitHub issues to reference.

Best fit: Operators running mixed workload types, teams already using HashiCorp tooling, or engineers who need a simpler operational model without sacrificing scheduling sophistication.

Managed Cloud-Native Solutions

Amazon ECS, Google Cloud Run, and Azure Container Apps represent a different philosophical approach: offload the control plane entirely to the cloud provider and interact with orchestration through a higher-level abstraction. The operational burden drops significantly — there is no cluster to patch, no etcd to back up, and no CNI plugin to debug.

The trade-offs are portability and cost. Workloads built around ECS task definitions or Cloud Run service configurations are tightly coupled to their respective provider's primitives. Migration to a different platform requires meaningful rework. At higher traffic volumes, managed services can also be more expensive than self-managed clusters, though this calculation depends heavily on workload characteristics and reserved instance pricing.

For individual operators who are not philosophically opposed to cloud lock-in and whose workloads fit the managed service model — primarily stateless, containerized, HTTP-driven services — these platforms can provide an excellent operational experience with minimal overhead.

Best fit: Solo engineers prioritizing time-to-production and operational simplicity over portability, particularly those running workloads that align naturally with serverless or event-driven patterns.

A Decision Framework for Individual Operators

The following criteria can help structure the evaluation process.

Workload complexity: If your services require advanced scheduling (GPU workloads, topology-aware placement, custom resource types), Kubernetes is difficult to replace. If your workload is primarily stateless HTTP services and background jobs, simpler alternatives are likely sufficient.

Team growth trajectory: If your organization is hiring engineers who will arrive with Kubernetes experience and expectations, standardizing on Kubernetes reduces onboarding friction. If you are the sole operator for the foreseeable future, the learning investment may not pay off.

Existing tooling investment: Teams embedded in the HashiCorp ecosystem will find Nomad more natural to adopt. Teams already running on AWS with existing IAM and VPC configurations may find ECS the path of least resistance.

On-call burden tolerance: Every layer of infrastructure you manage is a layer you may be paged about at 2 a.m. Managed services shift that burden to the cloud provider. Self-managed Kubernetes shifts it entirely to you.

Portability requirements: If avoiding vendor lock-in is a hard requirement — common in regulated industries or organizations with multi-cloud mandates — managed cloud services are a poor fit. Kubernetes, despite its complexity, at least runs consistently across providers through distributions like EKS, GKE, and AKS.

When Kubernetes Actually Makes Sense for Small Teams

There are legitimate scenarios where a solo or small-team operator should choose Kubernetes despite its complexity. If the application demands horizontal pod autoscaling tied to custom metrics, sophisticated canary deployment workflows, or the ability to run operators for stateful workloads like databases, Kubernetes provides capabilities that lighter alternatives cannot replicate without significant custom engineering.

In these cases, the recommendation is to use a managed Kubernetes distribution rather than self-hosting. EKS, GKE, and AKS all abstract the control plane, leaving the engineer responsible only for node management and workload configuration. Tools like k3s or k0s reduce the footprint further for on-premises or edge deployments.

Making the Call

The right container orchestration choice is the one that matches your actual operational capacity to your actual workload requirements — not the one that looks best on a resume or aligns with industry trends. For most solo DevOps engineers in 2024, a managed cloud-native service or a well-configured Nomad cluster will deliver more reliable outcomes than a self-managed Kubernetes deployment maintained by a single person.

Reserve Kubernetes for the problems it was built to solve. For everything else, simpler is usually better.