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Fast Scalability Solutions with Great Value in Every Recommendation
What do you like best about the product?
fast scalability soutions, balancing benefit/price in every recommendation
What do you dislike about the product?
Problems generating cost reports with the platform. I need to develop tools to obtain that information for the bussines
What problems is the product solving and how is that benefiting you?
Before using Cast AI, I was constantly overwhelmed by the manual effort required to manage Kubernetes clusters and the anxiety of rising cloud costs. It solved the 'manual toil' of scaling, and turned my cloud bill from a mystery into a roadmap. In short, Cast AI transitioned me from being an 'infrastructure firefighter' to a strategic developer.
Essential Tool for Kubernetes Efficiency and Cost Control
What do you like best about the product?
What I value most is how Cast AI turns complex Kubernetes optimization into a seamless, automated process. It consistently delivers significant cost savings (often up to 50%) by intelligently handling Spot instances and right-sizing workloads in real-time. The automated autoscaling is much more responsive than native cloud tools, and the 'set and forget' nature of the platform has effectively removed the burden of manual cluster tuning from our DevOps team.
What do you dislike about the product?
Initial Trust & Security Approval: Because Cast AI requires deep access to your cloud environment to perform automated actions (such as adding or deleting nodes), the initial security review—especially in larger organizations—can be lengthy and quite rigorous.
Learning Curve for Advanced Policies: While the “read-only” setup is fast to get up and running, getting comfortable with the more advanced automation policies—and correctly fine-tuning the “Auto-Remediation” settings—takes time and a strong understanding of Kubernetes to avoid unexpected behavior.Cast AI solves the persistent issues of cloud waste and operational complexity. Before, we spent too much time manually adjusting resources and overpaying for idle capacity. Now, the platform benefits us by automatically selecting the most cost-effective nodes without compromising performance. This hasn't just lowered our monthly bill; it has given our engineers the peace of mind and the time to focus on building features rather than managing infrastructure.
Learning Curve for Advanced Policies: While the “read-only” setup is fast to get up and running, getting comfortable with the more advanced automation policies—and correctly fine-tuning the “Auto-Remediation” settings—takes time and a strong understanding of Kubernetes to avoid unexpected behavior.Cast AI solves the persistent issues of cloud waste and operational complexity. Before, we spent too much time manually adjusting resources and overpaying for idle capacity. Now, the platform benefits us by automatically selecting the most cost-effective nodes without compromising performance. This hasn't just lowered our monthly bill; it has given our engineers the peace of mind and the time to focus on building features rather than managing infrastructure.
What problems is the product solving and how is that benefiting you?
Cast AI addresses the ongoing problem of cloud waste and the heavy, manual burden of cluster tuning. Previously, we struggled with overprovisioning and with the complexity of managing Spot instances safely.
By automating real-time selection of the most cost-effective nodes, it has reduced our cloud bill significantly while still maintaining high availability. For us, the biggest benefit is the operational peace of mind: our DevOps team no longer spends hours on infrastructure rightsizing and can instead focus on shipping better code.
By automating real-time selection of the most cost-effective nodes, it has reduced our cloud bill significantly while still maintaining high availability. For us, the biggest benefit is the operational peace of mind: our DevOps team no longer spends hours on infrastructure rightsizing and can instead focus on shipping better code.
Cost Reduction and Efficient Automation
What do you like best about the product?
I really like the vulnerability visualization feature that CAST AI provides; it identifies vulnerabilities and shows the vulnerable images, highlighting their criticality. I also really appreciate all the information it provides and the possibility of automations, which is very important for the organization and greatly eases the daily workload.
What do you dislike about the product?
One of the areas where CAST AI needs to evolve is in the matter of multiple organizations within the same company. Today, there is no centralized management, which makes daily operations quite difficult. If you have an environment that requires an organization, you need to create another one, which makes managing these tokens more difficult. Furthermore, there is no centralization in terms of a main organization and subsidiary organizations. This ends up multiplying the users, and they need to be managed manually, complicating the management of the tool.
What problems is the product solving and how is that benefiting you?
I use CAST AI to optimize costs, improve the visualization of our Kubernetes-based environments, and automate tasks, which eases our daily workload. It solves cost issues in large environments, provides insights into container vulnerabilities, and aids in the predictability of operations.
Intelligent cost optimization in Kubernetes
What do you like best about the product?
Automate cost optimization in Kubernetes efficiently, reducing waste and operational effort, without compromising high performance and reliability.
What do you dislike about the product?
Some more advanced features still lack clearer and well-organized documentation, which may end up delaying the adoption of the tool and the resolution of problems by new users.
What problems is the product solving and how is that benefiting you?
We had difficulty with high costs in Kubernetes, but now we automatically optimize, reducing waste and cutting infrastructure costs.
Efficient Cost Optimization with CAST AI
What do you like best about the product?
I like how CAST AI is great for our Kubernetes cluster cost optimization and helps maximize CPU and memory resource usage. I appreciate its in-place pod right sizing and container live migration capabilities. With in-place right sizing, our 5xx errors have significantly decreased. The container live migration feature is beneficial for consolidating our stateful workloads. Setting up CAST AI was a straightforward process.
What do you dislike about the product?
NA
What problems is the product solving and how is that benefiting you?
I use CAST AI for Kubernetes cluster cost optimization and to maximize CPU and memory usage. It offers node consolidation and improves pod right sizing, which has significantly reduced our 5xx errors. Container live migration helps with stateful workload consolidation.
Effortless Cost Optimization and Autoscaling
What do you like best about the product?
I like CAST AI for its workload autoscaling, which helps with optimization while maintaining availability. It has helped me achieve about 70% cost optimization with better bin packing, rebalancing, and pod mutation to maintain a diverse workload. I also appreciate having a single place to manage multiple clusters and daily costing. The initial setup was very easy and well-guided by the CAST AI team.
What do you dislike about the product?
I think it could be better for handling StatefulSets in bin packing. It can handle better StatefulSets to make sure pods should handle state while eviction and reschedule in other nodes.
What problems is the product solving and how is that benefiting you?
I use CAST AI for cost optimization and autoscaling in our K8s clusters. It solved issues with node choices, better autoscaling, bin packing, and minimizing sudden cost increases. It helped achieve 70% cost optimization and provides a single place to manage clusters and daily costs.
Simple UI with Quick Cost Insights
What do you like best about the product?
Simple UI and Quick cost insights to reduce the cluster costs.
What do you dislike about the product?
Tool is expensive and it doesn't have the backward /forward compatibility for other products
What problems is the product solving and how is that benefiting you?
We evaluated the product for internal use, but it’s too expensive to roll out across our environment. We do have a way to reduce costs, and choosing this product at this stage would still involve additional work for us.
Automates Scaling with Ease, High on Cost-Effectiveness
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.
Short experience with CAST AI
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.
Centralized Monitoring and Cost Savings for solutions using k8s multi-tenancy
What do you like best about the product?
For a platform that uses k8s multi-tenancy , monitoring and optimizing costs easily across many clusters and namespaces is very important. Cast gives us a easy way to achive this.
What do you dislike about the product?
For un-even workloads across Pod-s in one Deployment or StatefulSet auto-scaling does not always yield the best results, but this is true for all k8s auto-scaling options out there. With cast.ai you can manually tweak the requests/limits
What problems is the product solving and how is that benefiting you?
Cost monitoring and optimization across a large number of k8s clusters , each cluster with many namespaces
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