Cast AI - EKS fully automated cost optimization and monitoring

My main use case was to migrate development and staging environments to Kubernetes using CAST AI, and with them, we created these environments. We moved them from virtual machines to Kubernetes clusters.

CAST AI helped with that migration by creating a Kubernetes cluster designed to automatically manage infrastructure components. This allowed us to boost the project I work on inside Zazmic. This allowed us to focus on application deployment rather than complex infrastructure provisioning. The team also gave direct feedback to us when we were implementing. They developed specific scripts to automate the creation. This enabled the team to easily spin up or tear down environments on demand as needed for testing and validations.

CAST AI positively impacted our organization by leading the migration of a very high number of pre-production environments from VMs to Kubernetes. We had these specific sandboxes for each developer, and there was a high volume of environments. This helped us make it easier to administer and manage each environment for each developer. It really gave us an opportunity to manage our systems better. Regarding cost and savings, CAST AI's optimization software helped us move away from a mess of machines in our environments to really make progress on that matter, to manage our costs better, and have a more maintained environment, and use our resources better.

CAST AI can be improved by managing the role-based access control better. I think it would be better for CAST AI to improve how access permissions are handled for developers. That would be a beneficial refinement on that matter. Managing node management better is important. We had a recurring issue with our nodes where they were becoming not ready and required manual deletion to be reclaimed by the cluster. Making this automation better would be great. The cost optimization is great, but improving the automation implementation of cost-saving measures, such as weekend scaling, would be something useful as well.

We began to work with CAST AI on April 2021.

CAST AI is stable.

The customer support is great. The customer support and the team effort to help us put the cluster up or maintain it is great. It is really useful.

Before choosing CAST AI, we were evaluating putting up the Kubernetes clusters by ourselves, but we chose CAST AI to help us on that matter first.

We had monthly savings of 2,500 to 3,000 from our bills on cloud.

My experience with pricing, setup cost, and licensing was that the proposal they shared with us included an ongoing monthly cost. There was a one-time setup cost as well, but all that we spent with CAST AI was okay because we have monthly savings that surpassed what we spent with the tool.

Automation and infrastructure optimization features helped my team by creating the cluster to manage infrastructure components. This allowed the booster project to focus on application deployment rather than complex infrastructure provisioning. These features made it easier for the administration of the creation of the cluster and administration of it. We did not have to spend so much time going through the cloud itself to provide this for us.

The best features CAST AI offers include automated infrastructure management, infrastructure optimization, and tooling for automation. These are things that stand out. The possibility to use solutions integrated with it is particularly helpful.

The migration to Kubernetes helped reduce cloud costs by enabling the use of preemptible instances and optimizing cluster infrastructure.

Regarding CAST AI's AI capabilities, I think its governance and security are well-managed. We did not have issues with how it is handled and how we give people access to the systems or to CAST AI itself.

CAST AI's capabilities are really accurate and reliable.

I would rate this product a 9 overall.

Public Cloud

Amazon Web Services (AWS)

What do you like best about the product?

What I love most about Cast AI is the automated, real-time right-sizing. Before using it, our engineering team spent countless hours manually looking at Grafana dashboards, trying to guess which AWS EC2 instances to use, and wrestling with Kubernetes cluster sizing.

Cast AI completely takes that off our plate. It analyzes our actual resource demand in real time and automatically swaps out inefficient nodes for optimized, cost-effective instances without any downtime. The fact that it manages Spot Instances so smoothly—automatically moving workloads to On-Demand nodes if a Spot instance gets interrupted—has given us massive savings on our cloud bill without sacrificing application stability. It’s truly a "set-it-and-forget-it" optimization tool.

What do you dislike about the product?

What I love most is how it optimizes Kubernetes on autopilot. It watches our traffic in real time and automatically swaps out nodes for cheaper, perfectly sized ones without any downtime. It even manages Spot instances seamlessly, slashing our cloud bill without the stress of crashes.

The biggest downside is the pricing—they charge based on your total cluster size, not just the money they save you, so the bill gets expensive fast as you scale. Plus, the automation can be a bit of a black box during sudden traffic spikes, and fixing it means digging through some really messy documentation.

What problems is the product solving and how is that benefiting you?

The biggest problem Cast AI solves is cloud waste from over-provisioning. Our team used to buy way more AWS compute than we needed just to avoid crashes, but Cast AI automatically scales our nodes up and down to match actual demand in real time.

It also takes the risk out of using cheap Spot instances. It predicts when AWS is about to reclaim an instance and moves our workloads to a fresh node before anything drops. For me, that means instantly slashing our cloud bill by thousands of dollars without the stress of managing infrastructure or dealing with late-night alerts.

What do you like best about the product?

I use CAST AI to optimize Kubernetes clusters by automatically reducing cloud costs, right-sizing workloads, and improving application performance. It's especially valuable for production workloads where balancing performance, reliability, and infrastructure costs is important. What I like most about CAST AI is how much of the Kubernetes optimization process it automates. The platform continuously analyzes workloads and makes intelligent scaling and right-sizing decisions. I also appreciate the clear cost visibility, support for Spot instances, and actionable recommendations that help reduce cloud spending without compromising application reliability or performance. Additionally, it was very easy to set up.

What do you dislike about the product?

Overall, CAST AI has been very effective, but there are a few areas that could be improved. The platform has a lot of powerful features, so the initial learning curve can be a bit steep for teams that are new to Kubernetes optimization. I'd also like to see more guided onboarding, additional best-practice templates for common cluster configurations, and more detailed cost forecasting before optimization changes are applied. Expanding the reporting dashboard with deeper historical trends, workload-level insights, and clearer explanations for automated recommendations would also make it easier to understand and trust optimization decisions.

What problems is the product solving and how is that benefiting you?

I use CAST AI to optimize Kubernetes clusters, reducing cloud costs and improving performance. It automates resource management, scaling, and optimizes node sizing, saving me time on infrastructure tasks and letting me focus on building and deploying applications.

What do you like best about the product?

I like CAST AI for its automated optimization, which has made a significant impact on managing multiple client clusters efficiently. Its ability to analyze resource usage and optimize node provisioning helps minimize infrastructure costs and reduces cloud waste. The automation feature enables rightsizing of workloads, maintaining efficiency at scale, and identifying underutilized compute resources. The initial setup process was smooth, making the transition from manual Kubernetes optimization seamless.

What do you dislike about the product?

I think more granular client level reporting, better forecasting capabilities, and enhanced workload recommendations can be improved in the platform. Also, the initial setup process was smooth, but if you are running with multi-client with multi-environment, it will be a learning curve for new users.

What problems is the product solving and how is that benefiting you?

I use CAST AI to optimize Kubernetes workloads, minimize infrastructure cost, and reduce cloud waste. It rightsizes workloads, automates node optimization, and saves effort in managing multiple client clusters, improving efficiency at scale.

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.

Private Cloud

Amazon Web Services (AWS)

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.

Public Cloud

Amazon Web Services (AWS)

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.

Public Cloud

Amazon Web Services (AWS)

My main use case for CAST AI is the optimization of EKS clusters.

CAST AI monitors the workloads in the cluster and optimizes the number of nodes needed, their CPU and their memory so that we pay as little as possible.

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.

CAST AI could be improved by adding some AI agent capabilities.

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

I have been using CAST AI for one year.

I consider CAST AI to be stable.

I would rate the scalability of CAST AI as correct. The platform adapts well to different workloads.

I would rate CAST AI's customer support as good, although I have not had to use it.

I was using Carpenter before CAST AI, and I decided to switch because its configuration and results were not as expected.

I acquired CAST AI through the AWS Marketplace.

I have seen a return on investment with CAST AI.

My experience with CAST AI's pricing, implementation costs, and licensing has been good, as I have not found the price to be too high for the features it provides.

I did not evaluate other options before choosing CAST AI.

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.

Public Cloud

Amazon Web Services (AWS)