What stands out most about CAST AI is how genuinely hands-off and dependable its automated Kubernetes cost optimization feels. Rather than only surfacing recommendations in a dashboard that engineers still have to implement manually, the autoscaler actively rightsizes workloads, bin-packs pods efficiently, and provisions just-in-time compute while avoiding cluster instability. Its handling of Spot instances is another big highlight. The automated fallbacks and graceful drain mechanisms let us confidently use Spot pricing for production-adjacent and batch workloads without the constant worry of downtime or unexpected node evictions. On top of that, the read-only savings report you get immediately after connecting a cluster provides clear, transparent visibility into idle compute waste before turning on full automation. Review collected by and hosted on G2.com.

The automated autoscaler can sometimes be overly aggressive with resource limits, which leads to pod churn or minor throttling during rapid traffic spikes. To mitigate this, I end up spending extra time fine-tuning disruption budgets and setting appropriate safety headroom. There’s also some friction between making manual adjustments in their dashboard and keeping everything consistent when managing state through Terraform. During onboarding, granting deep, cluster-level RBAC permissions can take a while to get approved by security teams. Lastly, the cost model doesn’t feel very economical for smaller workloads that don’t already have significant cloud compute waste. Review collected by and hosted on G2.com.