Kubernetes Spot Instances: How to Cut Compute Costs Without Gambling on Reliability

Kubernetes Spot instances can reduce compute costs by 60–90%, with the cited 2025 benchmark reporting average savings of 77% for Spot-heavy clusters and 59% for mixed fleets, but providers may reclaim capacity with short notices: two minutes on AWS and about 30 seconds on GCP and Azure. They are best suited to fault-tolerant workloads such as stateless services, batch processing, CI/CD runners, and checkpointed machine-learning jobs, while stateful databases, control-plane services, payment processing, and strict-SLA workloads should remain on on-demand capacity. Reliable operation depends on disruption planning, including correctly configured PodDisruptionBudgets, replica counts, readiness checks, cloud-specific shutdown grace periods, node draining tools such as AWS Node Termination Handler or Karpenter, and scheduling policies that prefer Spot while allowing on-demand fallback. Spot capacity, pricing, and interruption rates vary substantially by provider and region, with broad instance-type diversity improving availability and enabling Karpenter to optimize placement and, when configured, consolidate workloads onto cheaper Spot options. At larger scale, automated platforms such as Cast AI aim to forecast interruptions, proactively replace affected nodes, and move fallback workloads back to Spot capacity when it returns, reducing ongoing operational work and potential overreliance on on-demand savings-plan commitments.

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