Cloud cost optimization: 10 strategies for engineering teams | Harness Blog

Cloud cost optimization requires combining technical waste reduction with ongoing visibility, accountability, and governance to preserve workload performance and reliability. Recommended practices include allocating costs by team and workload, safely rightsizing compute and Kubernetes resources, autoscaling services, shutting down idle nonproduction environments, detecting anomalies, purchasing commitments only after usage stabilizes, using spot capacity for interruption-tolerant jobs, optimizing storage and data transfer, applying budgets and showback or chargeback models, and tracking unit economics and realized savings. For Kubernetes, teams should establish cost and service-level baselines, make conservative changes one workload at a time, and monitor latency, errors, memory failures, and scheduling to enable rapid rollback. The guidance also recommends investigating bill increases by isolating affected providers, services, workloads, recent changes, and ownership, while customer examples cite savings from automated shutdowns and improved cost visibility. It further argues that these FinOps practices should extend to AI spending, where fragmented billing and unclear ownership contribute to reported waste.

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