CAST AI Reviews

The main use case for CAST AI is Kubernetes cost optimization and cluster auto-scaling and spot instance management.

We use CAST AI for our CPU utilization. It monitors all the CPU utilization, usage, storage, network and node utilization, and then it automatically removes waste. CAST AI has reduced our AWS bills through better utilization and reduced idle resources.

We use the cluster auto-scaler in CAST AI. Instead of manually scaling nodes, CAST AI automatically adds nodes or removes unused nodes. It chooses cheaper instance types whenever we need any node or instance and prevents over-provisioning. This saves a lot of our AWS bills and achieves AWS cost optimization. This saves both engineering effort and cloud costs.

The best features that CAST AI offers are the cluster auto-scaler and spot instance automation. Spot instance automation is one of the strongest capabilities of CAST AI because it automatically finds the cheapest spot instances and replaces any interrupted spot nodes. It balances reliability and savings, allowing many companies to achieve significant savings through this feature. It optimizes cost substantially.

Another feature of CAST AI is workload resizing and right-sizing. It automatically resizes the workloads or allocates the workloads according to application behavior, CPU limit, CPU request, and memory request. It analyzes all of these factors and automatically allocates the workload or the instance.

CAST AI can be improved in that automation policies require careful tuning. Sometimes it can be confusing for non-technical people or managers who are not familiar with technical details. However, it is good for technical people who are already into DevOps or cloud engineering. Spot strategies may need adjustment for sensitive workloads. The reporting and UI part can be somewhat better. Technical support can also be improved. Documentation is somewhat unclear sometimes, but not everywhere.

There are many pros here, including easy onboarding, simple deployment, and excellent Kubernetes visibility, strong spot instance automation, and automated right-sizing. These features are very good for our organization because they reduce a lot of cost and reduce a lot of manual effort. However, some things can be improved, such as automation policies that require careful tuning and may need somewhat more help. Spot strategies can be improved, and some UI and documentation can also be improved.

I have been using CAST AI for around one year.

CAST AI is stable. It does not have any downtime or any other issue.

The scalability is working well. We have a lot of workloads and a lot of instances. Even with these, the scalability of CAST AI is good.

Customer support and technical support for CAST AI was responsive, knowledgeable about Kubernetes optimization, onboarding, and provided guidance on optimization policies.

I have evaluated many other options including KubeCost, PerfectScale, and ScaleOps. CAST AI stood out from all of those options. The automation capabilities to continuously optimize the cluster with minimal manual effort while maintaining application performance really stood out apart from all those other solutions.

The setup was good and easy integration. It has all the steps and clear documentation. I would give this a rating of nine out of ten.

I would give the implementation team a rating of nine.

CAST AI has reduced approximately 40% of our AWS bills and AWS cloud bills. It has really impacted positively in our organization and we are able to use our cloud better.

With that 40% savings, we were able to invest that money into other projects. Rather than wasting money, we were able to save that money and use it on another project, building another project or anything else with the help of that money. This is pretty good for us. The manual effort has also been reduced here. It is an automated system and completely automated, which is good for us.

Even though CAST AI is slightly higher in cost, we are able to optimize and have saved 40% of our cost. This is definitely a win-win situation. Thirty percent to 40% of our money has been saved in cost through CAST AI for our Kubernetes workloads and AWS cloud instances.

CAST AI provides 80% to 85% accuracy. Because it is an AI system, sometimes it can make mistakes, but providing 80% to 85% accuracy is a pretty good number for any normal tool. It is good.

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.

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.

CAST AI has positively impacted our organization because we are now able to control our Kubernetes costs, and the automated node provisioning continuously monitors our application usage to select which node to provision, ensuring the application has sufficient compute power and improving our cluster efficiency.

In terms of cost savings, we have currently reduced our costs by 30 to 40%, and it saves time while managing infrastructure because it continuously monitors and provides the nodes to the application, so we don't need to do anything ourselves. This is a fully automated process. Additionally, manual intervention has decreased significantly because this is a completely automated process.

The best features that CAST AI offers, in my experience, are automated scaling, intelligent node selection, cost recommendations, and workload right-sizing.

The biggest feature that has made a difference for our team is that the platform continuously analyzes our cluster's user-based pattern and makes practical optimization suggestions, which saves our team significant time while helping us control cloud expenses.

CAST AI also helps us reduce the manual effort involved in managing infrastructure while ensuring applications always have the resources they need, which is very valuable.

The limitations of CAST AI include reporting and customization options. I think they can improve in these areas, especially when some advanced settings require a learning curve, particularly for teams new to Kubernetes optimization. More detailed documentation and deeper visibility into certain optimization decisions would also be helpful.

We have been using CAST AI for six to eight months.

CAST AI is 100% stable.

CAST AI is 100% scalable. You don't have to do anything in terms of scaling because it is a SaaS platform that will scale automatically, no matter if you have 100 or thousands of Kubernetes clusters running. CAST AI can handle all the loads you have.

The customer support is very good. I have raised queries numerous times as a new user and found the customer support excellent. I would rate the customer support 10 out of 10.

We haven't used any different solutions prior to this.

The setup process is relatively straightforward. Integrating CAST AI with a Kubernetes cluster and cloud environments doesn't take very long, so the setup is very easy.

We have seen a return on investment, with money saved equating to approximately 30 to 40% ROI. I consider it a very good investment, and the overall ROI is approximately 20 to 30%.

In terms of pricing, I believe the pricing is reasonable because of the amount of savings and operational efficiency it delivers, making it easier to justify the investment. Organizations with larger Kubernetes footprints are likely to see the most value.

We haven't evaluated other options before choosing CAST AI.

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.

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'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.

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.

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.

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.

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.