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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.
Effortless Cost Efficiency and Monitoring in Multi-Cloud Management
What do you like best about the product?
I use CAST AI to manage our infrastructure, especially various EKS and GKE clusters for right sizing. It helps me select cost-efficient instances automatically based on use cases, which avoids over and under-provisioning. I love that it monitors resource utilization and cost across multi-cloud platforms and takes action automatically without downtime. The ability to replace over-provisioned instances with the most cost-efficient ones in real-time, particularly across multiple cloud environments like AWS and GCP, is really beneficial for me. I also like the finance-friendly dashboard and continuous insights that highlight cost-saving opportunities.
What do you dislike about the product?
For a beginner, it seems a little bit harder as a learning curve.
What problems is the product solving and how is that benefiting you?
CAST AI helps manage our EKS, GKE clusters by right-sizing and automatically selecting cost-efficient instances, preventing over or under provisioning. It monitors resource use across multicloud platforms, ensuring uptime without downtime and providing continuous cost-saving insights via a finance-friendly dashboard.
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.
Easy to Use and Delivers a Smooth Experience
What do you like best about the product?
It is easy to use, easy to implement, has good support, and meets all our needs.
What do you dislike about the product?
So far so good, we haven't had any problems
What problems is the product solving and how is that benefiting you?
It helped us optimize our infrastructure and gave us visibility into certain problems
CAST AI Full Autopilot That Actually Executes Changes in Real Time
What do you like best about the product?
What I like most is the Full Autopilot mode. Unlike other tools that just give you a list of recommendations that you have to implement manually, CAST AI actually executes the changes. It handles rightsizing, autoscaling, and spot instance management in real-time without our team having to intervene daily.
What do you dislike about the product?
Honestly, the biggest hurdle was just the initial 'trust fall' with Autopilot. It’s a bit stressful giving an external tool the keys to your production environment to spin up or terminate nodes on its own. We had to spend a good chunk of time in a sandbox environment and go through several security reviews before the team felt comfortable letting it run on full-auto. Once that trust was built, it was fine, but that first week definitely had us on edge.
What problems is the product solving and how is that benefiting you?
Our biggest issue was 'Cloud Waste.' Our engineers used to over-provision pods 'just in case,' and we were paying for massive amounts of idle CPU and RAM. CAST AI solved this by automating the bin-packing process.
It has completely removed the manual guesswork from cluster management. We no longer have to spend hours every week manually adjusting instance types or scaling policies; the tool just picks the most cost-effective compute for the workload in real-time.
It has completely removed the manual guesswork from cluster management. We no longer have to spend hours every week manually adjusting instance types or scaling policies; the tool just picks the most cost-effective compute for the workload in real-time.
Spot Fallback and Node Rebalancing Make a Big Difference
What do you like best about the product?
I liked the spot fallback feature and also like the feature of rebalancing the node.
What do you dislike about the product?
I feel it is costly compared to the other open source alternative tools.
What problems is the product solving and how is that benefiting you?
It is a very useful tool that helps our workflow in a better way to do faster and frequent releases.
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
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