CAST AI Reviews

My main use case for CAST AI is to optimize cloud costs and to see if there are any improved performances, especially automating Kubernetes platforms. I don't use it on a daily basis, but this represents my major use case.

I used CAST AI as a prototype while experimenting with it. I used it for three different approaches: workload right-sizing, intelligent node auto-scaling, and spot instance optimization with respect to cloud costs. I analyzed resource consumption to avoid paying for compute resources that were not being used. With respect to Kubernetes automation, I configured auto-scaling policies so that the platform could automatically provision nodes and optimize workload placement, removing any unnecessary capacity that was being used.

Majorly, I was experimenting with CAST AI to optimize Kubernetes infrastructure and to rebalance the clusters being used on the cloud.

Regarding cost saving, I could optimize certain cloud utilization costs. That was my major use case for CAST AI—understanding how much cloud resources we were using and where I could cut down costs. With respect to time saving, I monitored certain resource utilization, adjusted certain node capacity, and managed the scaling aspect, which was easily tuned with CAST AI's auto-scaling features. Regarding performance issues, CAST AI helped me allocate resources more efficiently to respond to changing workload demands, which improved the infrastructure utilization and consistency between infrastructures during heavy traffic overload or traffic fluctuation.

I don't have an exact calculation on cost savings, but I could reduce approximately 25 to 30% of cloud cost reduction. I could save nearly 2,000 to 3,000 a month.

The best features are the automated node auto-scaling, where Kubernetes worker nodes are based on application demand and select cost-efficient instance types. I used it for some of my EC2 instances. I would also mention the spot instance automation, which features certain fallback mechanisms. It supports fallback to on-demand capacity and offers prediction, which reduces compute costs between workloads and tolerates interruptions. CAST AI also includes Kubernetes security, which helps reduce risk alongside infrastructure optimization. Additionally, it supports multi-cloud Kubernetes support, which is valuable since I use different cloud architectures based on different use cases, enabling consistency across different cloud networks.

I used CAST AI primarily to have automated Kubernetes and cloud cost optimization. I made CAST AI an infrastructure layer. I used CAST AI primarily on AWS EKS, which measured improvement through multiple EC2 infrastructure costs and node utilization, and how I could optimize certain costs.

CAST AI helped me scale more dynamic instances while maintaining excess capacity of these compute resources on the cloud. I also tried to experiment with optimization.

Regarding features, the continuous optimization capability is valuable instead of manually reviewing Kubernetes resource utilization. I can adjust the infrastructure every few weeks, and it continuously evaluates workloads. The combination of cost efficiency, automation, and scalability makes CAST AI valuable and more scalable.

CAST AI is a useful platform for automating Kubernetes infrastructure and reducing cloud costs. However, I see improvement needed in cost visibility and reporting. There is a dashboard, but it doesn't showcase predictive scaling. A predictive scaling feature could be added where I can see more transparency around automated infrastructure decisions, especially when nodes and workloads are being replaced. There is a predictive cost involved, and that should be readily seen by CAST AI's dashboard so I could make better infrastructure choices and auto-scale in a better way. I would position myself to focus on better cost reporting and smarter predictive scaling as an added feature which could be a selling point for CAST AI.

Additionally, there is the team saving concept where there could be visibility for different teams.

Regarding understanding CAST AI's use case, I attended AWS re:Invent, where they sent out a demo to use CAST AI and other resources. I downloaded the necessary features and undertook the course to understand most of CAST AI's features. Although there was a demo and educational materials, CAST AI's use case needs to be more user-friendly for new users to understand what CAST AI is, what it is truly useful for, and where it exactly stands. The engagement with new users was limited.

I could watch videos and have understanding about Kubernetes and where I could use CAST AI, but what about a person who would not know about Kubernetes or any scaling features? If a new organization wants to know about CAST AI and its use cases, they might face feasibility issues in understanding CAST AI and its applications. If there is a handbook, playbook, or basic guide showing where CAST AI can work well for them, that would help a lot for new users of CAST AI.

I have mentioned most of the improvements I identified.

I have used CAST AI as a launch product, which I was introduced to last year as part of the AWS re:Invent program where they reached out to me to use their services. That is how I have been using CAST AI.

There are some stability issues with CAST AI, especially during fluctuating workloads, where I saw that it was predicting not precisely or accurately. However, I feel this occurs only on certain instances, but not majorly on similar instances.

As I mentioned earlier, I did face some scalability or stability issues, but CAST AI does handle a lot of auto-scaling needs, especially with Kubernetes, and that is why I would use CAST AI. There were many workloads on Kubernetes which had to be automated and auto-scaled based on the different infrastructures involved in different projects. CAST AI provided great instances for all of these use cases. My experience with changing workloads, scaling up or down of services was positive.

I never faced any issues since I was using CAST AI as a playground or a black box. I didn't face many issues, and I believe CAST AI has a good customer support team that is always ready to troubleshoot and assist with auto-scaling.

Before CAST AI, I used some other cloud-native tools. On AWS, there was Cost Explorer. On Kubernetes, there was Cluster Autoscaler. I used Prometheus and Grafana for monitoring CPU, memory, and cost utilization. These were the tools I used before CAST AI. CAST AI can combine all of these tools into a single platform and provide me with most of the answers for my use cases. I got to know about CAST AI because I attended the AWS re:Invent conference last year, when I started using CAST AI.

I already mentioned the cost savings of approximately 25 to 30% reduction in cloud infrastructure investment costs, which was a positive factor for me. Regarding fewer employees, I don't think CAST AI would replace employees at this time. That would be ambitious.

The setup cost is positive, especially considering the cost savings and infrastructure automation it provides. The connection is straightforward with the Kubernetes cluster. I didn't face many issues with configuring IAM permissions or the auto-scaling policies. It can take additional effort, especially in production environments where there are workload requirements and other necessary factors which would make the setup process less straightforward. A guide with recommended configurations or easier troubleshooting would be helpful during the setup process.

I used different tools as previously mentioned.

I would rate CAST AI a seven.

There are major improvements that can be made. With respect to auto-scaling, most cloud-based services are capable of showing these features. Since we are moving towards agents and multi-agent orchestration, there are certain features most cloud services provide. The novelty and usability of CAST AI could be replaced. However, where they cannot be replaced is the reason I add seven.

Since I was experimenting with CAST AI as a tool for optimizing core features, I didn't share much of my data with CAST AI, or I made it masked. I didn't have much sensitive data to provide.

I would say CAST AI is accurate and reliable because I saw a major reduction in cost savings for cloud optimization and the compute costs I was facing. That was a reliable resource where I could optimize costs. It was accurate in understanding where it could auto-scale certain tasks where there was traffic fluctuation. I would say it is reliable.

I would advise learning more about CAST AI because it requires some pre-learning to use it. It doesn't come as a free tool or service where you would need no understanding about CAST AI and could just incorporate it.

That wouldn't be a good procedure to adopt CAST AI into an organization. Learn what its best use case is, or you would majorly lose money if you don't want to use CAST AI in your use case but are still using it. If you don't have many instances on cloud-based infrastructure or compute instances, or a major Kubernetes auto-scaling need, then using CAST AI for your use case would provide limited value. Think about what use case you are looking into and understand where CAST AI and its features can be incorporated, then make an investment. That is my advice.

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.

My main use case for CAST AI is that we have two main use cases. The first one is that we don't want to actually handle all the nodes. On top of that, we are looking for optimization on provisioning containers.

A quick, specific example of how I use CAST AI for those purposes involves my company being a company dependent on demand usage. For example, we have campaigns when people start at 11:00 and finish at 2:00 PM. Our infrastructure during those hours is very large. After that, we need to resize the whole infrastructure, and that is when CAST AI starts working for us. We start decreasing the number of pods and after that CAST AI automatically starts deleting nodes. So basically, we don't have over-provisioned nodes anymore. For the second use case, when developers create an application and they don't know exactly how much resources that application is going to take, we use CAST AI to reduce the request. For example, if a developer creates an application which takes around 4 GB and two CPUs, CAST AI provisions one CPU and 1 GB.

About how I use CAST AI, I also use it for vulnerabilities. Our Docker images have been scanned by CAST AI, and they give me the severities of each vulnerability.

CAST AI has positively impacted my organization because we are able to save a lot of money.

Regarding those savings, I can tell you that before CAST AI, we were paying 10,000 for an environment and after CAST AI, we pay around 7k, including the CAST AI fee.

The best features CAST AI offers in my opinion are node provisioning, hibernation, and support.

Of those features, the node autoscaler has made the biggest difference for me and I use it day-to-day because every single day our infrastructure is being rotated, especially in our case where we use PHP. We have memory leaks when the application doesn't freeze the resources frequently enough.

I would also add hibernation as a feature, where basically we can power off the whole cluster for a period of time.

I believe CAST AI can improve in the data, showing customers actually what is the impact of CAST AI in our organization.

I think that now they are integrating with Kinshi, another product inside of CAST AI. It should be great to integrate as other products as well.

I believe the improvements needed for CAST AI that I haven't mentioned yet involve the installation part from the Helm chart, as it's not optimized at the moment.

CAST AI is very stable.

CAST AI's scalability is important, and we don't care about that because CAST AI is a service. We use the service and they need to over provision their machines in the cloud and handle those things.

The customer support is the best one I have ever used. They use a Slack channel. We ask things there and they come back to the solution in less than one hour.

On a scale of one to ten, I would rate the customer support as ten.

Previously, I used DevZero, which is a company trying to do the same as CAST AI, but on top of that, they use Karpenter, which is a node provisioning tool that I don't prefer.

We have seen a return on investment because we have fewer people focused on the DevOps side thinking about how to provision a node and what CPU and what memory. We delegate that to CAST AI and we focus on our business.

I am the expert regarding my experience with pricing, setup cost, and licensing.

Before choosing CAST AI, I evaluated other options including DevZero, as I mentioned, and Kubecost, which can be found on PeerSpot.

On a scale of one to ten, I would rate CAST AI overall as ten.

I choose that number because I've been using CAST AI for four years and I'm very happy with all the processes they have. I'm happy with the product, happy with business analytics, and happy with the responsible team members.

Regarding CAST AI's AI capabilities, I think its governance and security are very good because they have the data here in Europe and they don't use it for any other purpose.

Regarding CAST AI's AI capabilities, I think its accuracy and reliability of output are strong. It's very reliable since we can provision nodes in less than five minutes.

CAST AI is deployed in our organization in our Kubernetes environment, but we are a SaaS platform and basically we are an enterprise client.

My recommendation to others reading reviews on PeerSpot is that CAST AI is a fantastic tool to focus on your business. Don't focus on your platform or on infrastructure. Focus on your business.

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.

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 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.