Cast AI - EKS fully automated cost optimization and monitoring

Our main use case for CAST AI  is that we use it as a cloud provider and for Kubernetes  clusters. We are using secure access roles and all those requirements for right-sizing the containers' workload. We use CAST AI  for that purpose, along with optimization of Kubernetes  clusters for cost, performance, and resource efficiency. It takes care of all these aspects.

A specific example of how we use CAST AI for right-sizing or optimization in our Kubernetes clusters is that Kubernetes environments are dynamic, and manual tuning leads to over-provisioning and inefficiencies. To overcome that situation, we are using CAST AI.

CAST AI helps us with automated node provisioning, workload right-sizing, intelligent auto-scaling, and overall cost visibility of the containerized systems that we have on the cloud.

The best features CAST AI offers are the Kubernetes auto-scaling mechanism, continuous analysis of the pod-level CPU and memory usage, and ensuring that workload right-sizing is being done and our nodes are not over-provisioned. Identifying inaccuracies in the resource request is what we find quite useful with CAST AI.

It definitely saves time and money as well, along with peace of mind because CAST AI continuously analyzes the pod-level CPU and memory usages. This helps us to optimize the request and the limits adjustments on our usage pattern, and overall, right-sizing improves the packing and reduces the wasted compute that we have in the cloud.

In terms of overall impact on the organization, CAST AI has definitely helped us optimize our Kubernetes resources and given us automation capabilities. It is definitely helping us reduce the manpower and overall compute which is wasted. We can definitely save these using CAST AI. We will be notified upfront and proactively about any wastages that are happening, or any cost leakages that are happening in our system.

The documentation of CAST AI can definitely be improved for first-time users. When we are onboarding a new user, the team needs some time to tune the policies and build confidence in automation because it actively makes infrastructure-level changes that must be validated against the real production workloads.

The user interface can definitely be optimized further. Support-wise, they are good.

I have been using CAST AI for around a year.

CAST AI is stable.

Scalability-wise, CAST AI is good. We haven't seen any issues scaling it to multiple environments, multiple clusters, workloads, and node count as they grow. It appears to be designed for large, dynamic Kubernetes environments, and I definitely see value in this. As the complexity increases, it is scalable as well as stable.

Customer support is definitely good.

I haven't used a different solution. We came across CAST AI and found it good, so we opted for it.

In terms of setup cost, licensing, and pricing, I find the experience good. It's enterprise-grade, and the pricing is usage-based with no heavy upfront setup cost, which makes the onboarding straightforward. The licensing aligns well with the value they deliver.

We have definitely seen a return on investment because we could see a significant ROI in terms of efforts saved, which is proportional to the time and money saved. We observed a 20 to 30% reduction in Kubernetes infrastructure cost. Node utilization is improved, and we could see a 60 to 70% reduction in our manual cluster optimization efforts that we used to put initially.

In terms of setup cost, licensing, and pricing, I find the experience good. It's enterprise-grade, and the pricing is usage-based with no heavy upfront setup cost, which makes the onboarding straightforward. The licensing aligns well with the value they deliver.

Before choosing CAST AI, we had a couple of other tools to evaluate, including native Kubernetes auto-scaling, cloud provider auto-scaling tools, and a few Kubernetes cost visibility platforms.

For others looking for a product such as CAST AI to improve their overall containerized platform efficiency, my advice is to start with conservative policies, observe the behavior closely, and gradually expand automation as the confidence grows.

CAST AI delivers the most value for teams running production Kubernetes at scale.

I give this product a rating of 8 out of 10.

Public Cloud

Amazon Web Services  (AWS )

What do you like best about the product?

I use CAST AI to automate cluster scaling and reduce manual work in maintaining our Kubernetes infrastructure. I appreciate how it helps reduce cloud costs by taking care of scaling automatically. I like how well CAST AI handles spot instances and cluster autoscaling. It manages spot instances flawlessly, ensuring I don’t have to worry about interruptions. What used to be a manual, tedious process is now sorted automatically. I particularly enjoy that CAST AI can automatically manage spot instances and fall back to on-demand without causing downtime. Additionally, the setup process was straightforward, allowing us to get up and running with minimal effort.

What do you dislike about the product?

Sometimes, the pricing feels a bit high for small clusters.

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

I use CAST AI to automate cluster scaling, reduce manual maintenance for Kubernetes infrastructure, and minimize cloud costs by handling spot instances without interruptions.

What do you like best about the product?

I love how CAST AI is quite intuitive, making the management of my company's Kubernetes easier and straightforward. The simplicity in the initial setup really impressed me, as it was very simple and facilitated a quick start. The cost reduction functionality through spot instances and the ability to quickly and easily hibernate non-productive clusters are features I consider extremely valuable. This not only optimizes my financial resources but also improves operational efficiency. Furthermore, the 10 out of 10 recommendation I would give to CAST AI reflects my complete satisfaction and confidence in the platform.

What do you dislike about the product?

Some type of self-healing before swapping instances could be improved. I would like there to be a check before shutting down a node to ensure that the applications on the target node are initialized correctly to avoid downtime.

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

I use CAST AI to manage Kubernetes, reducing costs with spot instances and hibernating non-productive clusters, which optimizes resources. It is intuitive, with quick cluster hibernation, making management easy without complications.

What do you like best about the product?

Automatically selecting the machine type

What do you dislike about the product?

Please add more feature documentation on console

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

I dont have to sit and select the machine type and cost saving are very good

What do you like best about the product?

CAST AI offers robust Kubernetes cost monitoring, providing clear visibility into resource usage and expenses across clusters. Its actionable cost recommendations are particularly helpful, guiding users on how to optimize or reduce spending with specific, practical steps. Additionally, CAST AI supports org-level cluster monitoring, making it easy for organizations to manage and analyze the cost and performance of multiple clusters in one place. Overall, CAST AI is an effective tool for enterprises looking to gain better control over their Kubernetes costs and efficiency.

What do you dislike about the product?

While CAST AI offers powerful cost optimization features, there are a few areas that could be improved. The initial setup and integration with existing Kubernetes environments can be complex and may require substantial time and expertise. Some users have reported a learning curve when navigating the UI and understanding the full range of functionalities. Additionally, as a third-party platform, there may be concerns around data security and handing over cluster management. Pricing could also become a consideration for smaller teams or organizations with limited budgets.

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

CAST AI is solving the problem of high and unpredictable Kubernetes cloud costs by providing real-time cost monitoring, intelligent optimization, and actionable recommendations. It helps identify inefficiencies, unused resources, and overprovisioning in my clusters, allowing me to automate scaling and adjust workloads for maximum cost efficiency. This results in significant cost savings, better resource allocation, and improved visibility across the organization’s clusters, making cloud infrastructure management much simpler and more predictable.