Cast AI Reviews & Product Details

The Workload Autoscaler has been the most valuable feature for us. Our Kubernetes workloads were heavily over-requested, and it right-sizes CPU and memory requests automatically based on actual usage, so we didn't have to tune hundreds of manifests by hand. That cut our requested capacity and node spend noticeably.

We also get detailed visibility into the cluster: usage vs. requests per workload, efficiency, and cost breakdowns. It covers everything we used Kubecost for, so we were able to drop it and keep one tool for both optimization and cost insight. There's also an MCP server, so we can query cluster and workload data straight from AI assistants without going through the UI or writing API scripts. Review collected by and hosted on G2.com.

The same data shows up in several dashboards, and the numbers don't always match from one page to another. That makes it hard to know which figure to trust or report internally, and we've had to cross-check against our own billing data. Some metrics are also hard to read without documentation, for example whether a value is per node or per template. Review collected by and hosted on G2.com.

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.

Automatically cuts our Kubernetes costs without hurting speed. Review collected by and hosted on G2.com.

The UI is getting crowded. With security scanning, rightsizing, and spend forecasting added, finding a simple autoscaler setting can take three menus, and the docs seem to lag behind new features. I've had to contact support a few times, though they do respond. Node Templates also have a steep learning curve. You need to understand tolerations and node affinities to keep sensitive workloads like Redis and API gateways off spot nodes. Early on, a rebalance during a traffic spike briefly slowed our Retool dashboard. The savings-based pricing is fair to me as an engineer, but it's harder to explain to finance than a fixed license. Review collected by and hosted on G2.com.

What I like best about CAST AI is how effectively it automates cloud cost optimization without requiring constant manual intervention. The platform gives good visibility into our cloud environment, identifies areas where we can reduce unnecessary spend, and helps optimize resources based on actual usage. The automation and recommendations are particularly useful because they allow the team to focus on higher-value engineering work rather than continuously monitoring and adjusting infrastructure. Review collected by and hosted on G2.com.

The initial setup and configuration can take some time, especially when getting familiar with the platform and its different optimization options. Some recommendations may also require additional review before being implemented in a production environment. That said, once the platform is configured properly, the ongoing management is relatively straightforward. Review collected by and hosted on G2.com.

CAST AI provides a powerful approach to Kubernetes infrastructure optimization and workload management.

One of the features I appreciate most is its dynamic compute provisioning capability, which helps automatically adjust Kubernetes compute capacity according to workload requirements. Another major strength is its workload autoscaling capability, particularly its integration with HPA and VPA.

Automated workload rightsizing, infrastructure optimization, workload placement, and resource-utilization capabilities. These features help platform teams improve cluster efficiency and manage Kubernetes infrastructure more effectively.

Overall, CAST AI is a valuable platform for organizations looking to automate Kubernetes infrastructure operations, optimize compute resources, and improve workload resource management Review collected by and hosted on G2.com.

While Cast AI's workload rebalancing capability is useful for optimizing cluster utilization, we have encountered a few issues during rebalancing activities. The support team was responsive and helped resolve the problems but product felt as costly as it charges per core Review collected by and hosted on G2.com.

CastAI is an amazing tool which we use also in production which has reduced our cluster costs massively. The UI is very intuitive helps us realize potential gains as well, and of course, the amount of hooks and controls given by CastAI are great to just tune workloads as per our needs and risk appetite. It also has a great bot that helps us analyze the events and decisions taken. Overall, it has been a great experience using it. Review collected by and hosted on G2.com.

UI is a bit buggy sometimes, and also some of the settings configured via Terraform do not reflect in the UI. Job-like workloads are a bit tricky to be managed by CastAI. Review collected by and hosted on G2.com.

Pretty useful, and I don’t have to chase every team to reduce CPU and memory usage for their services. The UX is nice, and it integrates well with Entra ID. Cast AI can also improve the performance of your services if it detects they’re underscaled. The application, operator, and pods running in the cluster are also very efficient at what they do. It is directly saving us money, so the cost of Cast AI is returned by the cloud cost savings many times over. The support offered by Cast AI is also pretty quick and helpful. Review collected by and hosted on G2.com.

Cast AI is an application that adjusts CPU and other application resources, and it can sometimes downscale services too aggressively. If your workloads are complicated, you’ll likely need to spend some time fine-tuning the scaling policies to get the results you want. Review collected by and hosted on G2.com.

Cast AI is a complete solution for EKS Clusters. It's AI based scaling takes care of Node as well as workload autoscaling. It's very easy to integrate with AWS EKS clusters. I did it from scratch and completed it in an hour. There documentations are also very good. It helps in reducing and managing the costs as well. Overall UI/UX is also very good and user friendly. Performance is also very good depending on the policies that you select. Review collected by and hosted on G2.com.

Cast AI is limited to EKS clusters only. It is also more beneficial if most of our services are deployed on eks only. Billing and pricing models are also little bit complex. Review collected by and hosted on G2.com.

It does a fantastic job scaling our environments while keeping costs down. The UI is easy to learn, and incorporating CastAI into our environments was not too difficult. The dedicated Teams channel we use to message support has been excellent, and it consistently helps us get fast, reliable responses. Review collected by and hosted on G2.com.

I don’t really have anything to dislike about it. It effectively replaces 3–4 separate products that would otherwise be freeware and therefore subject to changes we might not want, or it would force us to rely on a much more expensive product instead. Review collected by and hosted on G2.com.

Cost Optimization: Automatically right-sizes workloads, packs containers efficiently onto nodes, and cuts cloud expenses on AWS, Google Cloud (GCP), Microsoft Azure, and Oracle Cloud. It dynamically provisions and consolidates nodes based on live demand, and it can handle spot instances with automated on-demand fallbacks. Review collected by and hosted on G2.com.

Production Stability Risks: Granting an AI engine full autonomy to delete, move, or provision nodes in real time can sometimes lead to transient network blips, application downtime, or unexpected state disruptions—especially when workloads aren’t perfectly configured for stateless operation. Review collected by and hosted on G2.com.