Kubernetes is often associated with stateless web apps, but it is also a strong platform for batch, stateful, and AI workloads.
Batch workloads with elastic capacity
Kubernetes handles short-lived jobs and batch processing by scheduling containers only when they are needed.
- run jobs with Kubernetes Jobs and CronJobs
- scale worker capacity based on queue depth
- use spot instances for cost-sensitive workloads
This flexibility makes batch processing more responsive and less expensive.
Stateful services in containers
Kubernetes has matured support for stateful workloads through StatefulSets, persistent volumes, and storage classes.
- stable network identities for stateful pods
- reclaimable persistent storage
- predictable scaling for databases and caches
That means companies can run databases, message queues, and other stateful components alongside stateless services.
AI and data workloads
AI workloads benefit from Kubernetes’ ability to manage specialized resources like GPUs and large-scale distributed training.
- schedule GPU-backed pods with device plugins
- use custom resource definitions for AI frameworks
- integrate with data pipelines and feature stores
Business advantage
For organizations building data products, Kubernetes reduces the need for separate execution platforms. It can support a broad spectrum of workloads on a single infrastructure stack.