MLOps teams often face a choice: use managed AI platforms or build a Kubernetes-native stack. The managed route is faster to start but can become expensive and inflexible. The Kubernetes route offers control, but only if you assemble the right pieces. A recent KodeKloud overview sets out a 2026 consensus stack that is worth treating as a practical ladder rather than a shopping list.
The core components
The stack begins with Kubeflow Pipelines and Kubeflow Trainer for experiment tracking and training orchestration. These are mature, open-source tools that fit naturally onto Kubernetes. They let teams define reproducible training workflows without reinventing orchestration.
For job scheduling, Kueue has become the preferred option. It handles queues, priorities and fair sharing of cluster resources across teams. Without a scheduler like Kueue, a Kubernetes cluster serving multiple ML teams quickly turns into a free-for-all where one large training job blocks everything else.
Model serving is handled by KServe, which provides a standard inference layer with scaling, canarying and explainability hooks. For autoscaling, KEDA is the usual companion, scaling workloads based on custom metrics such as queue depth or inference latency rather than simple CPU thresholds.
For continuous delivery, Argo CD deploys models and pipelines declaratively. Observability comes from Prometheus and Grafana, with Evidently added for model and data drift monitoring. Together these cover the full lifecycle: train, schedule, serve, deploy, watch and retrain.
Why a ladder matters
Teams get into trouble when they install the whole stack at once. Each tool introduces operational overhead: upgrades, access control, debugging and integration testing. A more reliable path is to start with the part of the lifecycle that hurts most.
If the pain point is reproducible training, begin with Kubeflow Pipelines and Trainer. If it is sharing GPUs fairly, start with Kueue. If models are deployed manually and drift silently, begin with KServe and Evidently. Solve one class of problem well before expanding the footprint.
This staged approach also lets the team build operational skill. Kubernetes-native MLOps requires people who understand both ML workflows and cluster operations. Rushing the tooling without the skills produces a brittle platform that few people can fix.
What to watch out for
Networking and storage are the usual surprises. Distributed training generates large east-west traffic between workers. Inference workloads need low-latency access to models, which may be stored in object storage or on local volumes. These details matter more than the choice of pipeline framework.
Security is another area that is easy to overlook. Training jobs often need access to sensitive datasets. Serving endpoints need authentication, rate limiting and audit logging. Treat the MLOps platform with the same rigour as any production system.
The practical takeaway
The Kubernetes-native MLOps stack is not a theory. It is a set of proven tools that work together. The key is to treat adoption as a sequence of capability improvements, not a single project. Pick the bottleneck, add the right tool, stabilise it, then move to the next layer. That is how control without chaos is achieved.