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?
