Cast AI - EKS fully automated cost optimization and monitoring

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.

CAST AI's primary use case in my organization is optimization and automated node management in AWS EKS clusters. I use CAST AI daily to monitor cluster efficiency, optimize resource allocation, and reduce the operational effort required to manage Kubernetes infrastructure.

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

CAST AI has positively impacted my organization by reducing cloud costs, improving resource utilization, and allowing my engineering team to spend less time managing infrastructure and more time on platform improvements.

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

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

Overall, the platform is strong, and most improvements for CAST AI would be around reporting and advanced governance capabilities for larger organizations.

Enhanced forecasting capabilities and more detailed workload-level cost analytics would be useful for CAST AI. CAST AI's governance and security controls are solid, providing sufficient visibility into cluster changes and optimization actions, although more advanced policies controls would be beneficial.

I have been using CAST AI for about a year.

CAST AI has proven to be stable and reliable in production environments.

CAST AI's scalability is very good, as it scales effectively with cluster growth and increasing workload complexity.

My experience with CAST AI's customer support has been positive; response times are reasonable, and the team is knowledgeable.

Before CAST AI, I relied mainly on native Kubernetes autoscaling and manual monitoring processes.

The setup process for CAST AI was straightforward, pricing was reasonable considering the cost savings achieved, and licensing was easy to understand.

The ROI with CAST AI was visible within a few months through cloud cost reduction alone, and additionally, my team spends less time manually managing Kubernetes infrastructure.

Before choosing CAST AI, I looked at AWS native optimization tools and a few Kubernetes cost management platforms due to its automation capabilities.

I would advise others looking into using CAST AI to start with a non-production cluster to understand the optimization recommendations, establish baseline cost metrics, and then gradually expand adoption across environments. CAST AI has been a valuable addition to my Kubernetes platform operations; it has helped me reduce cloud spending while simplifying cluster management, and I would recommend it to organizations looking to optimize Kubernetes costs at scale. I rate this product an 8 out of 10.

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 has positively impacted our organization by achieving approximately a 30% to 40% reduction in Kubernetes infrastructure cost. We also reduced the manual cluster management activities significantly, especially around node scaling and capacity planning. The overall response is positive.

I measured the cost reduction by tracking our AWS infra cost month over month after enabling CAST AI across our Kubernetes cluster. The biggest improvement came after we enabled automated node provisioning, workload resizing, and spot instance optimization. Within the first two to three months, we saw a consistent reduction in compute costs while maintaining the same application performance and availability. We also compared resource utilization before and after the deployment and found that the clusters were running much more efficiently with significantly less over-provisioned capacity.

The best features CAST AI offers, as per my experience, would be the automated Kubernetes cost optimization, intelligent auto-scaling, workload right-sizing recommendations, cluster visibility and analytics, and spot instance management.

Intelligent auto-scaling has helped my team by automatically adjusting cluster capacity based on real-time workload demand. Earlier, we had to manually plan for specific traffic spikes, which often resulted in over-provisioned resources during low-usage periods. With CAST AI, nodes are added or removed automatically as workloads change, helping us maintain application performance while reducing unnecessary cloud costs. It has also reduced the operational effort required to manage Kubernetes clusters on a daily basis.

I would appreciate seeing CAST AI improved with more granular reporting, deeper cost allocation insights, and additional customization options for optimization policies.

Overall, the platform is strong. The most needed improvements would be around reporting and advanced governance capabilities for large organizations.

The reason I did not give it a perfect score is that I would still prefer to see more advanced cost reporting and workload-level analytics.

Some additional improvements needed with CAST AI would include enhanced forecasting capabilities and more detailed workload-level cost analytics, which would be very useful.

We have been using CAST AI for about a year now.

CAST AI has proven to be stable and reliable in production environments.

CAST AI's scalability is very good. It scales effectively with cluster growth and increasing workload complexity.

My experience with CAST AI's customer support has been very positive. Response times are reasonable, and the team is very knowledgeable.

Before using CAST AI, we mainly relied on native Kubernetes auto-scaling and manual monitoring processes.

The setup process was straightforward.

I have seen a return on investment. The ROI was visible within a few months through cloud cost reduction alone. Additionally, our team spends less time manually managing Kubernetes infrastructure.

My experience with pricing, setup cost, and licensing has been positive. Pricing was reasonable considering the cost savings achieved, and licensing was easy to understand.

Before choosing CAST AI, we evaluated other options including AWS native optimization tools and a few Kubernetes cost management platforms before selecting CAST AI due to its automation capabilities.

Regarding CAST AI's AI capabilities, I think its 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 accuracy and reliability of CAST AI's output are generally very good. The recommendations are generally accurate and reliable. We always validate major changes, but in most cases, the optimization suggestions are practical and effective.

We purchased CAST AI directly through the vendor, not through the AWS Marketplace.

I would rate the customer support an eight out of ten.

I would advise others looking into using CAST AI to start with a non-production cluster to understand the optimization recommendations, establish baseline cost metrics, and then gradually expand adoption across environments.

Overall, CAST AI has been a valuable addition to our Kubernetes platform operations. It has helped us reduce cloud spending while simplifying cluster management. I would recommend it to organizations looking to optimize Kubernetes costs at scale. I have given CAST AI a rating of eight out of ten.

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.

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

Automated node provisioning and optimization has made the biggest difference for us. It reduced the need for manual intervention and helped ensure we are always running the most cost-efficient infrastructure.

CAST AI has positively impacted our organization by reducing cloud costs, improving resource utilization, and allowing our engineering team to spend less time managing infrastructure and more time on platform improvements.

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

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

Regarding CAST AI's AI capabilities, the governance and security controls are solid. It provides sufficient visibility into cluster changes and optimization actions, although more advanced policy would be beneficial.

Enhanced forecasting capabilities and more detailed workload-level cost analytics would be useful improvements for CAST AI that I have not mentioned yet.

I have been using CAST AI for more than a year.

CAST AI has proven to be stable and reliable in production environments.

CAST AI's scalability is very good; it scales effectively with cluster growth and increasing workload complexity.

Our experience with customer support has been positive. Response times are reasonable and the team is knowledgeable.

Before CAST AI, we relied mainly on Kubernetes auto-scaling and manual monitoring processes.

Before choosing CAST AI, we looked at AWS native optimization tools and a few Kubernetes cost optimization platforms before selecting CAST AI due to its automation capabilities.

I have seen a return on investment, and the ROI was visible within a few months through cloud cost reduction alone. Additionally, our team spends less time manually managing Kubernetes infrastructure.

My experience with pricing, setup cost, and licensing was that the setup process was straightforward, pricing was reasonable considering the cost savings achieved, and licensing was easy to understand.

Since adopting CAST AI, we achieved approximately 30-40% reduction in Kubernetes infrastructure costs. We also reduced manual cluster management activities significantly, especially around node scaling and capacity planning.

The best features CAST AI provides are automated Kubernetes cost optimization, intelligent auto-scaling, spot instance management, cluster visibility and analytics, and workload right-sizing recommendations.

Regarding the accuracy and reliability of CAST AI's AI capabilities, recommendations are generally accurate and reliable. We always validate major changes, but in most cases, the optimization suggestions are practical and effective.

My advice to others looking into using CAST AI is to start with a non-production cluster to understand the optimization recommendations, establish baseline cost metrics, and then granularly expand adoption across environments.

Overall, CAST AI has been a valuable addition to our Kubernetes platform operations. It helped us reduce cloud spending while simplifying cluster management, and I would recommend it to organizations looking to optimize Kubernetes cost at scale. I rated CAST AI as an eight out of ten.

CAST AI allows me to have test workloads that use spot-type machines.

The best features that CAST AI offers are its machine learning algorithms that listen to the data generated by the cluster in order to optimize the workloads.

With just a couple of clicks and a very high-level definition of what is needed, CAST AI starts gathering the data and executes the actions automatically while producing quite a lot of reports.

It is also interesting that the same tool works for different clouds.

CAST AI has positively impacted my organization through cost reduction.

On average, I think the savings are between 15 and 20%, and for certain workloads, those savings can be even higher.

Improving the documentation would help the platform reach a perfect rating.