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.
If you want to use CAST AI, first understand your workload capability. Whether you are using Kubernetes, you should go for CAST AI if you are using Kubernetes at a higher scale. You cannot go with CAST AI if you have only one to ten users. If you have a minimum of 1,000 customers who are using your Kubernetes workloads, then CAST AI will definitely decrease your workloads and analyze your workloads to decrease your cost. It has all the automated features, including cluster auto-scaler and workload right-sizing. These features really help optimize the cost of cloud automatically rather than manually optimizing the cost of cloud. I give this product an overall rating of nine out of ten.
Observability Engineer at a tech services company with 11-50 employees
Real User
Top 20
Jun 27, 2026
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.
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 delivers strong value through automation and cost optimization, but there are still a few areas where usability and reporting could be improved. Overall, it has a positive impact on our infrastructure management. Their governance is compliant with all frameworks, and in terms of security, I believe they are very secure. Their accuracy is approximately 80 to 90%, and in terms of reliability, it is the same—approximately 80 to 90% reliable for the output it provides. Teams struggling with Kubernetes costs, especially larger teams with multiple Kubernetes clusters or workloads, should consider using CAST AI. It offers a very good return on investment while saving both operational time and money. I would rate this review an 8 out of 10.
General Manager at a manufacturing company with 10,001+ employees
Real User
Top 5
Dec 23, 2025
For others looking for a product such as CAST AI to improve their overall containerized platform efficiency, my advice is to start with conservative policies, observe the behavior closely, and gradually expand automation as the confidence grows. CAST AI delivers the most value for teams running production Kubernetes at scale. I give this product a rating of 8 out of 10.
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 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.
If you want to use CAST AI, first understand your workload capability. Whether you are using Kubernetes, you should go for CAST AI if you are using Kubernetes at a higher scale. You cannot go with CAST AI if you have only one to ten users. If you have a minimum of 1,000 customers who are using your Kubernetes workloads, then CAST AI will definitely decrease your workloads and analyze your workloads to decrease your cost. It has all the automated features, including cluster auto-scaler and workload right-sizing. These features really help optimize the cost of cloud automatically rather than manually optimizing the cost of cloud. I give this product an overall rating of nine out of ten.
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.
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.
My advice for other professionals who are considering implementing CAST AI is that they should try it. I would rate this product 9 out of 10.
CAST AI delivers strong value through automation and cost optimization, but there are still a few areas where usability and reporting could be improved. Overall, it has a positive impact on our infrastructure management. Their governance is compliant with all frameworks, and in terms of security, I believe they are very secure. Their accuracy is approximately 80 to 90%, and in terms of reliability, it is the same—approximately 80 to 90% reliable for the output it provides. Teams struggling with Kubernetes costs, especially larger teams with multiple Kubernetes clusters or workloads, should consider using CAST AI. It offers a very good return on investment while saving both operational time and money. I would rate this review an 8 out of 10.
For others looking for a product such as CAST AI to improve their overall containerized platform efficiency, my advice is to start with conservative policies, observe the behavior closely, and gradually expand automation as the confidence grows. CAST AI delivers the most value for teams running production Kubernetes at scale. I give this product a rating of 8 out of 10.