My primary use case for CAST AI is Kubernetes cost efficiency, and since I handle the cloud system as well, CAST AI has been instrumental in helping me. We mainly use it to automate processes in AWS EKS clusters. For my recent use case with CAST AI for Kubernetes cost efficiency in AWS EKS clusters, we are building a sourcing platform in our production environment, where we have deployed an EKS environment filled with workloads. Before CAST AI, we mainly sized node groups, which often led to overpricing. CAST AI automatically provided us with a clearer vision of our clusters based on the use case we are addressing. This is our main use case and example.
Observability Engineer at a tech services company with 11-50 employees
Real User
Top 20
Jun 27, 2026
My main use case for CAST AI is Kubernetes cost optimization and automated node management in AWS EKS clusters. One specific example of how I use CAST AI for Kubernetes cost optimization and node management is in our production EKS environment where workloads fluctuate throughout the day. Before CAST AI, we manually sized the node groups and often over-provisioned resources. CAST AI automatically provisions the most cost-effective instances and continuously right-sizes the cluster based on the workload demand. This significantly reduces the unused capacity while maintaining application performance. I use CAST AI daily to monitor cluster efficiency, optimize resource allocation, and reduce the operational effort required to manage Kubernetes infrastructure.
CAST AI serves as our primary solution for Kubernetes cost optimization and automated node management in our EKS cluster. One example of how we use CAST AI for cost optimization and node management is in our production EKS environment where workload fluctuates throughout the day. Before CAST AI, we manually sized node groups and often provisioned over-provisioned resources. CAST AI automatically provisions the most cost-effective instances and continuously right-sizes the cluster based on workload demand. This significantly reduced unused capacity while maintaining application performance. We use CAST AI daily to monitor cluster efficiency, optimize resource allocation, and reduce the operational effort required to manage Kubernetes infrastructure.
Our main use case for CAST AI is Kubernetes cost optimization, automated node provisioning, and improving cluster efficiency. I can provide a specific example of how we use CAST AI for Kubernetes cost optimization and cluster efficiency. Before implementation, we were manually handling all of these tasks. After implementing CAST AI, we are able to see the cost of each pod and node, and based on the reports from CAST AI, we can determine how to optimize our costs. In day-to-day operations, we use CAST AI to monitor all workloads running on our cluster and evaluate how our nodes and pods are performing. We can determine if we need to resize the nodes and pods or if we are spending too much money on pods, which can be optimized through CAST AI's platform.
General Manager at a manufacturing company with 10,001+ employees
Real User
Top 5
Dec 23, 2025
Our main use case for CAST AI is that we use it as a cloud provider and for Kubernetes clusters. We are using secure access roles and all those requirements for right-sizing the containers' workload. We use CAST AI for that purpose, along with optimization of Kubernetes clusters for cost, performance, and resource efficiency. It takes care of all these aspects. A specific example of how we use CAST AI for right-sizing or optimization in our Kubernetes clusters is that Kubernetes environments are dynamic, and manual tuning leads to over-provisioning and inefficiencies. To overcome that situation, we are using CAST AI.
CAST AI is revered for its powerful cloud optimization capabilities, notably in cost reduction, performance enhancement, and security strengthening. It automates resource management and scales operations efficiently, leading to significant organizational improvements in efficiency, cost savings, and smoother cloud integration and management.
My primary use case for CAST AI is Kubernetes cost efficiency, and since I handle the cloud system as well, CAST AI has been instrumental in helping me. We mainly use it to automate processes in AWS EKS clusters. For my recent use case with CAST AI for Kubernetes cost efficiency in AWS EKS clusters, we are building a sourcing platform in our production environment, where we have deployed an EKS environment filled with workloads. Before CAST AI, we mainly sized node groups, which often led to overpricing. CAST AI automatically provided us with a clearer vision of our clusters based on the use case we are addressing. This is our main use case and example.
The main use case for CAST AI is Kubernetes cost optimization and cluster auto-scaling and spot instance management.
My main use case for CAST AI is Kubernetes cost optimization and automated node management in AWS EKS clusters. One specific example of how I use CAST AI for Kubernetes cost optimization and node management is in our production EKS environment where workloads fluctuate throughout the day. Before CAST AI, we manually sized the node groups and often over-provisioned resources. CAST AI automatically provisions the most cost-effective instances and continuously right-sizes the cluster based on the workload demand. This significantly reduces the unused capacity while maintaining application performance. I use CAST AI daily to monitor cluster efficiency, optimize resource allocation, and reduce the operational effort required to manage Kubernetes infrastructure.
CAST AI serves as our primary solution for Kubernetes cost optimization and automated node management in our EKS cluster. One example of how we use CAST AI for cost optimization and node management is in our production EKS environment where workload fluctuates throughout the day. Before CAST AI, we manually sized node groups and often provisioned over-provisioned resources. CAST AI automatically provisions the most cost-effective instances and continuously right-sizes the cluster based on workload demand. This significantly reduced unused capacity while maintaining application performance. We use CAST AI daily to monitor cluster efficiency, optimize resource allocation, and reduce the operational effort required to manage Kubernetes infrastructure.
My main use case for CAST AI is the optimization of EKS clusters.
Our main use case for CAST AI is Kubernetes cost optimization, automated node provisioning, and improving cluster efficiency. I can provide a specific example of how we use CAST AI for Kubernetes cost optimization and cluster efficiency. Before implementation, we were manually handling all of these tasks. After implementing CAST AI, we are able to see the cost of each pod and node, and based on the reports from CAST AI, we can determine how to optimize our costs. In day-to-day operations, we use CAST AI to monitor all workloads running on our cluster and evaluate how our nodes and pods are performing. We can determine if we need to resize the nodes and pods or if we are spending too much money on pods, which can be optimized through CAST AI's platform.
Our main use case for CAST AI is that we use it as a cloud provider and for Kubernetes clusters. We are using secure access roles and all those requirements for right-sizing the containers' workload. We use CAST AI for that purpose, along with optimization of Kubernetes clusters for cost, performance, and resource efficiency. It takes care of all these aspects. A specific example of how we use CAST AI for right-sizing or optimization in our Kubernetes clusters is that Kubernetes environments are dynamic, and manual tuning leads to over-provisioning and inefficiencies. To overcome that situation, we are using CAST AI.