Regardless of the approach, the types of workloads being deployed on Kubernetes clusters are becoming more diverse.

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A CloudBolt Software survey of 321 Kubernetes practitioners at large enterprises reveals a significant trust gap in automation: while 89% consider automation crucial, only 17% continuously optimize their clusters. Key barriers include the need for human review before resource changes (71%), lack of visibility and transparency (48%), and concerns about rollback capabilities. Manual optimization breaks down around 250 changes per day for two-thirds of respondents. With 54% managing over 100 clusters, the scale of complexity is growing, and underutilized CPU and memory resources are becoming a major TCO problem driving pressure toward greater automation adoption.

Survey: Few IT Teams Can Continuously Optimize Kubernetes Clusters