CAST AI Reviews

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 and biggest feature of CAST AI is that with its help, I am able to automate Kubernetes, and because of that, cost efficiency and cost optimization are significantly better than before, along with workload balancing and intelligent automations. These are the main features.

CAST AI has positively impacted our organization in all three areas, where it has reduced the cost of our cloud infrastructure, the manpower that we were previously applying to optimize anything, and utilization is very much improved as we spend less time managing infrastructure.

In the last Q2 result, because of using CAST AI, we have reduced our manpower, money, and cost by 20 to 30%, which indicates substantial funding reduction.

I would like to see CAST AI improved with deeper and more intelligent answers and solutions, along with additional optimization and customization options. The customization option in particular could be enhanced to help further.

Overall, the platform is very strong, and most improvements could include advanced customization, advanced reporting, and documentation on a large scale.

We have been using CAST AI for more than one year.

My advice to others looking into using CAST AI is that if you are new to this and do not know much, you can use CAST AI to learn things and to gain hands-on experience on production level applications.

Before concluding, I would like to say that if you are new to this, CAST AI is very efficient before making a decision, and it is also very good from a cost point of view, saving considerable resources overall. I would rate this review a 9 out of 10.

Our primary use case for CAST AI is Kubernetes cost optimization and automated node management in AWS EKS cluster and Azure AKS cluster.

One example of how we use CAST AI for Kubernetes cost optimization and automated node management is in our production EKS environment where workloads fluctuate throughout the day. Before CAST AI, we manually sized node groups and often over-provisioned resources. CAST AI automatically provisions the most cost-effective instance and continuously right-sizes the cluster based on our workload demand. This significantly reduces unused capacity while maintaining application performance.

We use CAST AI daily to monitor clusters, efficiency, optimize resource allocation, and reduce the operational effort required to manage Kubernetes infrastructure, which is a very tedious task.

CAST AI has reduced cloud cost, improved resources utilization, and allowed our team to spend less time managing infrastructure and more on the platform improvements.

CAST AI has positively impacted our organization by reducing our cloud cost. It has improved the utilization of our resources and allowed our team to spend less time managing infrastructure and more on the development side.

Since using CAST AI, we have achieved approximately 30 to 40 percent reduction in our Kubernetes infrastructure cost. We also reduced manual cluster management activities significantly, especially around node scaling and capacity planning.

The best features CAST AI offers, in my experience, are automated Kubernetes cost optimization, intelligent autoscaling, spot instance management, workload right-sizing recommendation, cluster visibility, and analytics.

Automated node provisioning and optimization stand out the most for our team as it has the biggest impact. It reduced the need for manual intervention and helped ensure we are always running the most cost-efficient infrastructure.

To improve CAST AI, I would like to see more granular reporting, deeper cost allocation insights, and additional customization options for optimization policies.

Overall, the platform is very good, and most improvements would be around reporting and advanced governance capabilities for larger organizations.

CAST AI is generally accurate and reliable. We always validate major changes, but in most cases, the optimization suggestions are practical and effective.

I give CAST AI a nine because the governance and security controls are solid. It provides sufficient visibility into cluster changes and optimization actions. Although more advanced policy controls would be beneficial.

The governance and security of CAST AI are solid, providing sufficient visibility into cluster changes and optimization actions.

For others looking into using CAST AI, enhanced forecasting capabilities and more detailed workload-level cost analytics would be useful. I rate this review a nine.