Why Cloud Cost Optimization for Engineers Fails | Harness Blog

Cloud cost optimization often fails because engineers receive delayed, aggregated billing data that lacks connection to the code changes, deployments, Kubernetes workloads, or infrastructure configurations that caused spending changes. The post argues that centralized governance and periodic finance-led audits create friction, while over-provisioned container resource requests, idle resources, and inefficient infrastructure-as-code changes can quietly increase costs without clear workload-level attribution. It recommends shifting cost feedback left into pull requests and CI/CD pipelines, where developers can see estimated cost impacts before changes reach production, alongside automated policy-based guardrails that flag or block inefficient configurations without relying on manual approvals. Harness presents its Cost Management Agent as a platform for attributing spend to teams and workloads, detecting anomalies, offering or automating optimization actions, enforcing Open Policy Agent policies, and allowing users to query cloud spending in plain language across AWS, Azure, and GCP.

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