Automation has optimized Kubernetes costs and right-sizing cuts manual cluster work
What is our primary use case?
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.
What is most valuable?
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.
What needs improvement?
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.
For how long have I used the solution?
I have been using CAST AI for around a year.
What do I think about the stability of the solution?
CAST AI is stable.
What do I think about the scalability of the solution?
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.
How are customer service and support?
Customer support is definitely good.
Which solution did I use previously and why did I switch?
I haven't used a different solution. We came across CAST AI and found it good, so we opted for it.
How was the initial setup?
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.
What was our ROI?
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.
What's my experience with pricing, setup cost, and licensing?
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.
Which other solutions did I evaluate?
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.
What other advice do I have?
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.
Which deployment model are you using for this solution?
Public Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
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.
Intuitive and Economical Management of Kubernetes
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.
Automatic Machine Selection Is Great, But Needs Improvement
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
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.
Cost-efficient Kubernetes optimization using AI
What do you like best about the product?
CAST AI's ability to automatically optimize Kubernetes workloads for cost and performance is incredibly powerful. I particularly appreciate the autoscaling and rightsizing features that reduce our cloud bill without manual intervention. The interface is intuitive, and integration with AWS and GCP was seamless. The real-time cost visibility and actionable recommendations help us stay on top of resource usage.
What do you dislike about the product?
There’s a learning curve during the initial setup, especially if you're new to Kubernetes internals. Additionally, I would like to see more documentation around custom policy configurations for companies with unique infrastructure needs. While support is helpful, response time could be a bit faster during critical issues.
What problems is the product solving and how is that benefiting you?
CAST AI is solving the challenge of managing cloud costs and performance in Kubernetes environments. Manually optimizing workloads, scaling clusters, and choosing the right mix of instances across multiple cloud providers is time-consuming and error-prone. CAST AI automates these processes, enabling our team to focus on development rather than infrastructure tuning. Since implementation, we've seen a significant reduction in cloud spend—especially by replacing on-demand nodes with spot instances—and improved cluster efficiency with minimal manual effort.
A solution that cuts costs and reduce operational overhead by optimising Kubernetes resources
What do you like best about the product?
- 30% cloud cost savings over our whole compute
- Increase in my team’s productivity: a task that took seven days now gets done in three or four days
- We also unlocked extra cost savings from Spot Instances thanks to automated instance selection
What do you dislike about the product?
No complaints, the Cast support is great
What problems is the product solving and how is that benefiting you?
Reliability and scalability are important to use. But as we scaled, our infrastructure got more complex and expensive. Cast helps us scale efficiently, cut costs, and reduce manual work without losing reliability. Features like rebalancing and the Workload Autoscaler take care of optimization automatically, so we can focus on what matters.
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
A powerful solution for K8s cost optimization, best suited for cloud-native apps at scale.
What do you like best about the product?
CAST AI automatically rightsizes workloads, removes underutilized nodes, and selects the most cost-effective instances in real time—saving up to ~ 60% on cloud bills without sacrificing performance.
Their autoscaler is fast and responsive, scaling workloads vertically and horizontally with minimal delay, and supports real-time bin-packing.
Built-in security scanning and real-time insights about vulnerabilities and misconfigurations across your clusters are helpful for compliance.
With policies, teams can enforce governance (e.g., disallowing GPU usage, or restricting instance types), which is useful in large or shared environments.
Especially for organizations with dynamic or spiky workloads, CAST AI’s automation leads to substantial cost reductions.
• Simplicity and Speed:
The UI is clean, onboarding is relatively quick, and node replacements happen in minutes.
• Great for SRE/Platform Teams:
Offloads the pain of managing autoscaling, provisioning, and node selection.
What do you dislike about the product?
• Learning Curve on Governance:
Policy setup and interpretation of some recommendations require a bit of learning, especially for teams new to Kubernetes internals.
• Limited Documentation Depth:
Some users may find the documentation lacking for advanced use cases, particularly when integrating with non-standard setups (e.g., GKE workload identity federation, custom networking).
What problems is the product solving and how is that benefiting you?
• Significant Cost Savings:
Especially for organizations with dynamic or spiky workloads, CAST AI’s automation leads to substantial cost reductions.
• Simplicity and Speed:
The UI is clean, onboarding is relatively quick, and node replacements happen in minutes.
• Great for SRE/Platform Teams:
Offloads the pain of managing autoscaling, provisioning, and node selection.
CastAi Review
What do you like best about the product?
Easy to use UI. Consolidation of things at one place. Good cost savings and hibernation concept is really good.
What do you dislike about the product?
Some machine types are not supported. So, we need to select different machine types for our products. Build an overall platform not for just gcp but include other future of cloud as well such as db optimisations etc.
What problems is the product solving and how is that benefiting you?
Our costs have reduced significantly. Support is good, we get the response pretty fast if something goes wrong.
