Cast AI - EKS fully automated cost optimization and monitoring

What do you like best about the product?

The best part of Cast AI is its automated scaling and resource optimization features, which make it easier to manage Kubernetes infrastructure without spending too much time manually adjusting resources. The dashboard also provides a clear overview of resource utilization, which is especially helpful when working on cloud-based projects.

What do you dislike about the product?

For a beginner, a few advanced features may take some time to understand. Also, the documentation is quite complicated, which makes it harder to get started.

What problems is the product solving and how is that benefiting you?

It helps with my project by addressing unnecessary cloud spending caused by underutilized resources. It also provides better visibility into resource usage and helps optimize scaling, which reduces manual work.

What do you like best about the product?

I use CAST AI to automatically scale EKS and GKE resources and save money. It saves me the effort of adjusting Kubernetes resources dynamically and scaling the underlying infrastructure to meet pod needs. I like that it collects and aggregates useful metrics to adjust configurations; those metrics help me decide what to do next and plan ahead. CAST AI also automates capacity adjustments, and I find the UI onboarding easy and straightforward.

What do you dislike about the product?

I find the workload autoscaling powerful, but configuring it to meet the needs of different workloads can be hard, especially for job-like Pods. The UI onboarding is easy, but using Terraform for large-scale deployments is a little troublesome and requires more human effort. I’d like a generator or importer to help with Terraform onboarding.

What problems is the product solving and how is that benefiting you?

I use CAST AI to automatically scale EKS and GKE resources to save money. It saves me the human effort of dynamically adjusting Kubernetes resources and capacity to meet pod needs. Its aggregated metrics help me decide my next move, and many steps that used to be manual are automated.

What do you like best about the product?

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.

What do you dislike about the product?

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.

What problems is the product solving and how is that benefiting you?

Before adopting CAST AI, our Kubernetes clusters were running with a lot of idle, over-provisioned compute, and our team spent countless hours guessing resource limits and manually tweaking YAML files. CAST AI automates this end-to-end by continuously right-sizing pods, bin-packing nodes more efficiently, and safely using Spot instances with automatic on-demand failovers whenever capacity drops. The biggest benefit for us has been a clear reduction in our monthly cloud compute bill, without introducing downtime or performance degradation. Just as importantly, it takes the day-to-day burden of capacity planning off our engineers’ plates, so we can focus on shipping software instead of constantly babysitting cluster resources.

What do you like best about the product?

The cluster looks healthy, but I’m still digging into what’s actually behind that green status. I find myself jumping between workloads, tracing resource behavior, and gradually the performance picture starts to make sense. I can take a quick look at the user interface, make a small adjustment, then go right back to the workload, and that rhythm genuinely suits the way I troubleshoot infrastructure.

What do you dislike about the product?

The onboarding feels too basic for an experienced engineer, with explanations that cover things I already know. On top of that, one integration behaves differently than I expected, which adds an unnecessary pause and interrupts the workflow.

What problems is the product solving and how is that benefiting you?

We were constantly chasing wasted capacity across our Kubernetes clusters, and our workloads needed manual tuning whenever traffic shifted. Cast AI started giving us AI-driven recommendations for resource allocation, and the ROI became clear once we applied those changes and began cutting unnecessary cloud spend.

What do you like best about the product?

I like CAST AI for its innovative approach to multi-cloud GPU capabilities, which allow me to choose GPUs across multiple cloud providers and even on-prem. This feature has been particularly beneficial for us. I also appreciate the resource optimizations, with memory right-sizing helping us identify workloads that need additional capacity. The automated workload optimization and extra insights on GPU optimization are also valuable. Additionally, the setup was very easy, and the customer support from CAST AI has been great.

What do you dislike about the product?

I think one area where CAST AI could improve is by increasing the transparency of its optimization decisions. It would be really helpful if the platform could offer richer insights into CPU and memory performance, especially around memory kills. This would allow us to proactively right-size the nodes. Having dashboards, alerts, and data-driven usage insights would also be beneficial. For example, understanding how minimum and maximum node settings impact applications that can have irregular operations throughout the month would be great.

What problems is the product solving and how is that benefiting you?

I use CAST AI for automated workload optimization and resource right-sizing. It helps identify workloads needing more capacity and offers insights on GPU optimization across multi-cloud providers.