Cast AI - EKS fully automated cost optimization and monitoring

What do you like best about the product?

Cast AI has made managing and optimizing our Kubernetes infrastructure costs significantly easier, automatically right-sizing resources and identifying waste that would be tedious to catch through manual monitoring. The interface is clear and easy to navigate, surfacing cost-saving opportunities without requiring deep Kubernetes expertise to interpret. Integration with our existing cloud setup was smooth, connecting directly to our infrastructure without needing major reconfiguration. Performance-wise, automated scaling decisions have kept resource allocation efficient without sacrificing application responsiveness.

What do you dislike about the product?

Some of the more aggressive automated scaling decisions occasionally needed manual review to make sure they aligned with actual traffic patterns rather than just cost efficiency. Initial setup for more complex

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

Cast AI has automated a significant part of our Kubernetes cost optimization, catching inefficiencies and right-sizing resources that would otherwise require constant manual monitoring. This has reduced our infrastructure spend meaningfully while keeping performance stable, without needing a dedicated engineer focused solely

What do you like best about the product?

The best thing I like about this Cast AI platform is its ability to automate Kubernetes and cost optimisation instead of only showing recommendations. It also identifies the over-provisioned resource such as right size workloads and adjust clusters capacity based on demand and this also made our lower cost instances on a very much efficient level. Their dashboard also provides clear visibility into our cost across the clusters and workloads, which made the namespaces making it easier to understand where cloud spending is going. Their user interface is very much intuitive and clean, and their integration is also a bit extensive, I would say.

What do you dislike about the product?

Their initial configuration failed can feel complex, particularly when setting automation policies and permission workload constraints and spotting instance rules. Teams can also find bit difficult on automation on protection infrastructure without even understanding its decision-making process for the 3rd party platform. The reporting interface is useful but some uses are a bit difficult, along with managing several other clusters.

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

This platform helps us by solving the Kubernetes over-provisioning and underutilised resources, where we can predict cloud cost and the manual effort required to continuously optimise clusters. It also helps us by automating match capacity with workload demand and provides a detailed cost allocation along with the Kubernetes resources, which is a major add-on. Overall, this platform helped us by reducing the unnecessary cloud spending and also saved the engineering team time and allowed the cluster to scale more effectively, while making the overall workload availability and hence the overall platform increased our overall cloud performance, I would say.

What do you like best about the product?

What I like best about CAST AI is its ability to automatically optimize Kubernetes clusters while significantly reducing cloud costs. The platform automates tasks like workload right-sizing, node scaling, and Spot instance management, which saves time and minimizes manual infrastructure management. I also appreciate the clear dashboard and cost visibility, making it easy to track resource usage, identify optimization opportunities, and monitor savings in real time. Overall, it helps improve operational efficiency without sacrificing application performance or reliability.

What do you dislike about the product?

One downside of CAST AI is that it can take time to understand and configure all of its optimization features, especially for users who are new to Kubernetes. Some advanced settings and recommendations could be explained more clearly, and troubleshooting automated decisions isn't always straightforward. More detailed documentation, simpler onboarding, and deeper customization of automation policies would make the platform easier to use.

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

CAST AI solves the challenge of managing Kubernetes infrastructure efficiently while keeping cloud costs under control. It automates resource optimization, cluster scaling, and workload placement, reducing the need for manual intervention. This helps lower cloud spending, improves application performance and reliability, and allows our team to spend more time on development instead of infrastructure management.

What do you like best about the product?

Autonomous Kubernetes cost optimization with performance scaling.

What do you dislike about the product?

Risk of temporary service disruptions from aggressive automated node draining

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

Excessive cloud spend caused by over-provisioned Kubernetes clusters. Time-consuming, manual infrastructure rightsizing and capacity planning. High operational risk and complexity when managing spot instances.

What do you like best about the product?

What stands out most about Cast AI is its ability to automatically optimize our AWS infrastructure costs without requiring constant manual intervention. The Kubernetes autoscaling works intelligently rightsizing nodes and pods based on actual workload demand rather than static configurations. This has directly translated into significant cloud cost savings for our team while maintaining application performance and availability. The visibility it provides into resource utilization across our AWS environment makes it easy to identify waste and act on it quickly. For any team managing cloud infrastructure at scale, Cast AI delivers

What do you dislike about the product?

The initial configuration and onboarding process can be complex, especially for teams that are new to Kubernetes cost optimization. Fine-tuning the automation policies to match our specific workload patterns required considerable trial and error before we saw optimal results. Additionally, the pricing model can become costly as cluster size grows, making it harder to justify for smaller workloads. Better guided onboarding documentation and more granular policy controls would significantly improve the overall experience for new users.

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

Cast AI directly addresses the challenge of uncontrolled cloud spending on AWS, which is a common pain point for teams running Kubernetes workloads at scale. Managing node sizes, pod resources, and cluster capacity manually is time-consuming and error-prone — Cast AI automates all of this continuously in the background. The immediate benefit for our team has been measurable AWS cost reduction without sacrificing application performance or reliability. Beyond cost, it sol

What do you like best about the product?

What I like best about Cast AI is its ability to automate Kubernetes cost optimization without requiring constant manual intervention. It continuously analyzes workloads and automatically adjusts cloud resources to ensure applications have the capacity they need while minimizing unnecessary spending. This helps engineering teams reduce cloud costs, improve resource utilization, and spend less time on infrastructure management. I also appreciate its real-time monitoring, intelligent autoscaling, and clear visibility into cloud usage, which make it easier to optimize performance while maintaining reliability.

What do you dislike about the product?

One downside of Cast AI is that it has a learning curve, especially for teams that are new to Kubernetes or cloud cost optimization. Because it automates infrastructure decisions, it can take time to fully understand and trust its recommendations. Some advanced features may also require careful configuration to align with an organization's specific policies and workloads. Additionally, while the platform provides valuable automation and insights, organizations may still need experienced engineers to oversee optimization strategies and handle complex or highly customized environments.

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

Cast AI helps solve the challenge of managing cloud infrastructure efficiently while controlling costs. It automates Kubernetes resource optimization, reducing overprovisioning and eliminating wasted cloud spending without compromising application performance. This allows our team to spend less time manually monitoring and adjusting infrastructure and more time focusing on delivering products and supporting customers. The result is lower operational costs, improved resource utilization, greater infrastructure reliability, and increased productivity across engineering and operations teams.

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.