How Do You Run Production Workloads Safely on Spot Instances? A Practical Playbook for Small Teams

Spot instances can reduce compute costs substantially but require workloads and infrastructure designed to withstand abrupt node reclamation, with warning periods varying from about two minutes on AWS to roughly 30 seconds on GCP and Azure. Suitable candidates include replicated stateless services, asynchronous workers, CI runners, retryable batch jobs, and non-production environments, while primary databases, stateful singletons, uncheckpointed long-running jobs, and license-bound software should remain on on-demand or reserved capacity. The recommended production model combines a diversified spot fleet across instance types and availability zones with an on-demand baseline for critical system and stateful components, plus automatic fallback when spot capacity is unavailable. Kubernetes safeguards such as multiple replicas, PodDisruptionBudgets, topology spreading, termination handling, readiness probes, graceful SIGTERM shutdowns, workload scheduling controls, and interruption-aware node draining are presented as essential. The piece compares Cluster Autoscaler, Karpenter, cloud-native offerings, and commercial tools such as Spot Ocean, Cast AI, and Qovery, noting trade-offs between operational effort, automation, cloud support, and pricing. It advises a staged rollout beginning with non-production workloads and progressing through async jobs to carefully monitored stateless production services, while emphasizing that spot savings depend on compute’s share of total spending and should complement rightsizing, auto-stop policies, and reserved-capacity discounts.

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