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CastAI Automation Cut Wasted Compute and Improved Cost Transparency
What do you like best about the product?
What stood out to me most was the automation. Once it was set up, CastAI continuously analyzed our workloads and adjusted resources in real time. We saw noticeable reductions in wasted compute, especially around underutilized nodes. The platform’s ability to automatically leverage Spot instances without compromising stability was a big win for us. It handled the complexity in the background, which gave our team more time to focus on product work instead of infrastructure tuning.
The visibility into costs has also been valuable. Being able to break down spending by cluster and workload helped us understand exactly where our cloud budget was going. That transparency made it much easier to have productive conversations internally about optimization and accountability.
The visibility into costs has also been valuable. Being able to break down spending by cluster and workload helped us understand exactly where our cloud budget was going. That transparency made it much easier to have productive conversations internally about optimization and accountability.
What do you dislike about the product?
I seldom observed wrongful recommendations applied to some workloads where CastAI applied resources higher than the maximum available capacity on our EKS cluster which lead to some services staying in pending state without any way to control it.
What problems is the product solving and how is that benefiting you?
Before implementing CastAI, managing our Kubernetes infrastructure costs felt like a constant balancing act. We were either overprovisioning to stay safe or spending too much time manually tweaking node sizes and autoscaling rules. After integrating CastAI, much of that manual effort disappeared.
CastAI continuously analyzed our workloads and adjusted resources in real time, and we saw noticeable reductions in wasted compute—especially on underutilized nodes. The platform’s ability to automatically leverage Spot instances without compromising stability was a big win for us. It handled the complexity in the background, which gave our team more time to focus on product work instead of infrastructure tuning.
The added visibility into costs has also been valuable. Being able to break down spending by cluster and workload helped us understand exactly where our cloud budget was going. That transparency made it much easier to have productive internal conversations about optimization and accountability.
CastAI continuously analyzed our workloads and adjusted resources in real time, and we saw noticeable reductions in wasted compute—especially on underutilized nodes. The platform’s ability to automatically leverage Spot instances without compromising stability was a big win for us. It handled the complexity in the background, which gave our team more time to focus on product work instead of infrastructure tuning.
The added visibility into costs has also been valuable. Being able to break down spending by cluster and workload helped us understand exactly where our cloud budget was going. That transparency made it much easier to have productive internal conversations about optimization and accountability.
Automates Kubernetes and Cuts Costs Effectively
What do you like best about the product?
I use CAST AI to automatically optimize the Kubernetes workload, which helps cut cloud costs without needing manual tuning. It eliminates the manual effort of managing autoscaling, node provisioning, and performance monitoring, allowing me to focus on building features instead of babysitting infrastructure. I particularly appreciate the completely automated Kubernetes optimization that actually works. I also experience massive cost savings with real-time analytics, and the real-time cost-saving feature lets me see where my money goes. The automated cost-saving means I don't have to manually tune the cluster.
What do you dislike about the product?
I think the documentation and support guidance could be more consistent, particularly in areas like advanced autoscaling configurations. Clear, unified guidance with scenario-based examples and transparent troubleshooting notes would greatly enhance the onboarding experience.
What problems is the product solving and how is that benefiting you?
I use CAST AI to automatically optimize Kubernetes workloads, cutting cloud costs without manual tuning. It eliminates the manual effort of managing autoscaling, node provisioning, and performance monitoring, allowing me to focus on features instead of infrastructure.
Efficient Scaling with Minor Rebalancing Downtime
What do you like best about the product?
I like that CAST AI supports multi-cloud Kubernetes, making it really valuable for my work. The rebalancing feature is also a strong point for me. Additionally, the initial setup was very easy, which was a big plus.
What do you dislike about the product?
I don't like the downtime while rebalancing with CAST AI.
What problems is the product solving and how is that benefiting you?
I use CAST AI for scaling, which helps with node group management and reduces costs. It supports multicloud Kubernetes environments.
Revolutionized our HPC Workloads and Cost Optimization
What do you like best about the product?
I use CAST AI extensively to optimize the scaling of our HPC workloads on AWS EKS. The tech is great, providing both effective features and scalability. Cost optimization for Spot instances on AWS, a pretty tough problem, is solved amazingly well. There is no equivalent in AWS native feature or open-source components that come anywhere close to CAST AI's technology. The user and developer experience is smooth and fits well with the intended audience. Collaborating with the engineering team is fast and efficient, as they deliver fixes and features in record time. I also like the simple initial setup as onboarding through the helm chart requires limited involvement and the readonly mode allow to discover the products and insights without risks.
What do you dislike about the product?
N/A
What problems is the product solving and how is that benefiting you?
I use CAST AI to optimize the scaling of HPC workloads on AWS EKS, solving tough Spot instance cost problems effectively and allowing simultaneous multi-cluster scaling, unlike AWS native autoscaler and Karpenter.
Centralized Kubernetes metrics and intuitive UI to optimize resources
What do you like best about the product?
The centralization of Kubernetes metrics in an intuitive user interface, along with the configuration of nodes and workload autoscalers, facilitates resource optimization.
What do you dislike about the product?
What complicates the use of the tool for us a bit is the installation through Helm, since we deploy it with Terraform using manifests. In that context, some components, such as the evictor, cause us issues when managing them without the user interface.
What problems is the product solving and how is that benefiting you?
Cast AI helps us solve problems of overprovisioning and low efficiency in our Kubernetes infrastructure, as it automatically optimizes resource usage and selects more suitable instances according to actual demand. This mainly translates into a reduction in cloud costs, along with improved performance and greater application stability. Additionally, by automating optimization tasks that previously required manual intervention, it reduces the operational burden on the team and allows us to focus on other priorities.
CAST AI That Automatically Finds the Cheapest Server Every Minute
What do you like best about the product?
The cast AI picks the cheapest server and it is saving alot than manual
What do you dislike about the product?
Cast AI is autonomous, it makes decisions
What problems is the product solving and how is that benefiting you?
Before Cast AI, my day was basically a never-ending game of 'Guess the Server.' I had to decide if our app needed an m5.large or an m5a.large. I'd set high 'safety margins' just so I didn't get paged at 3:00 AM for a crash, but that meant we were paying for 20% more cloud than we actually used
Effortless Cost Optimization with CAST AI
What do you like best about the product?
I like using CAST AI for optimizing our Kubernetes clusters, particularly the right sizing of nodes by using spot instances and rebalancing nodes to fit our workloads. I appreciate the flexibility of automating the rebalance or hibernate processes. The service provides valuable recommendations on cost reduction, which is a big plus. The support from the CAST AI team during setup was also great, making the process comfortable.
What do you dislike about the product?
Nothing for now
What problems is the product solving and how is that benefiting you?
I use CAST AI to optimize our Kubernetes clusters, reducing costs by optimizing nodes, and providing recommendations. It right-sizes nodes with spot instances and rebalances them for workload accommodation. The automation of rebalancing and hibernation adds flexibility.
Notable Simplicity and Integration, Documentation Could Be Improved
What do you like best about the product?
I like the simplicity of use of the dedicated CAST AI web portal and the integration with Kubernetes. I find it valuable because it helps me keep my cloud infrastructure costs under control and allows for detailed monitoring.
What do you dislike about the product?
The configuration part could be described better and documented better.
What problems is the product solving and how is that benefiting you?
Using CAST AI, I control the costs of the k8s infrastructure by monitoring the resources actually used and avoiding waste, allowing for more efficient financial management.
Cost-Effective Scaling with Optimized Performance
What do you like best about the product?
I like CAST AI for its workload autoscaler and optimized performance. It handles scaling and spot instances very well, especially with great bin packing to reduce costs and the capability to manage spot instance interruptions effectively. The initial setup is also straightforward, just requiring a command from the CLI.
What do you dislike about the product?
I think the pricing of CAST AI could be improved.
What problems is the product solving and how is that benefiting you?
CAST AI does great bin packing to reduce costs and handles spot instance interruptions. I primarily use it for scaling and spot instances.
CAST AI Automates Kubernetes Optimization with Measurable Cost Savings
What do you like best about the product?
CAST AI is most helpful because it automates Kubernetes optimization at scale, and the upside is measurable cost savings, improved resource efficiency, and reduced operational overhead.
What do you dislike about the product?
the downsides aren’t about capability — CAST AI does what it’s designed to do well — but around transparency, scope expectations, and fit. It works best when clusters are large, workloads are variable, and teams are comfortable embracing automation.
What problems is the product solving and how is that benefiting you?
Castai solves:
Inefficient resource allocation, Manual scaling complexity, Spot instance, risk management, Cloud cost unpredictability.
And the benefit has been:
Lower infrastructure costs
Better workload stability
Less operational overhead
More time for the team to focus on platform improvements rather than infrastructure tuning
Inefficient resource allocation, Manual scaling complexity, Spot instance, risk management, Cloud cost unpredictability.
And the benefit has been:
Lower infrastructure costs
Better workload stability
Less operational overhead
More time for the team to focus on platform improvements rather than infrastructure tuning
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