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.
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
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.
CAST AI Tool review
What do you like best about the product?
CAST AI helps organizations in managing cloud resources and optimizing the costing part with high availability at low cost . Their consulting team is very dependable and are available anytime almost.
Integration to CAST AI is super easy and we have connect our cluster to CAST AI tool and using node templates and resource allocation the machine type and resources were scheduled to the pods in the Google Kubernetes Engine cluster.Even we can define the region where to spawn our pods.
We used terraform script to connect our clusters to CAST AI tools.The CAST AI team is very helpful in the integration part by providing the documentation to connect our cluster to CAST AI.
What do you dislike about the product?
Sometimes there are some issues that popup like machine types that might not supported by apps we provisioned by CAST AI tool which really annoys me.
What problems is the product solving and how is that benefiting you?
CAST AI helps us in reducing the costing part for cloud resource maintenance and they also helps us to rightsize the workloads in gke
Amazing service that save a lot of unnecessary cloud expense
What do you like best about the product?
The support engineers and it's easy to use.
What do you dislike about the product?
The UI can be improved and reports also.
What problems is the product solving and how is that benefiting you?
Instead of checking how much resources do I need on any point of time, CastAI adjust it automatically by VPA and HPA which lead to select the optimal compute family according to usage, this save us a lot of cost.
Simple to get going, fast and dedicated support will get you fully optimized
What do you like best about the product?
Simple configuration to get going, user friendly interface. You get a safe way to test out the features in a "view" mode before committing to your purchase, and you can activate feature by feature easily.
Deep customization using kubernets primitives and you can tune how aggressive the rebalancer is.
Dedicated support monitors your clusters metrics and utilization, and they let you know if you should consider rebalancing certain nodes, very quick reponse time and they are quick to find solutions if you run into problems.
What do you dislike about the product?
still needs work on custom replica sets, for example ones controlled by Strimzi Kafka and ArangoDB.
What problems is the product solving and how is that benefiting you?
Keeping costs down while still scaling based on the needs of the cluster.
The CAST Ai Technology and Team are outstanding.
What do you like best about the product?
The CAST AI Team and ease of implementation.
What do you dislike about the product?
The lack of being able to use the CAST console reflects actual cloud cost (after discounts) vs public on-demand costs.
What problems is the product solving and how is that benefiting you?
CAST AI is enabling us to reduce our cloud waste and cost and become more stable and efficient overall.
Cast.AI: Innovative savings automation meets outstanding support
What do you like best about the product?
Cast.AI's cloud cost automation offers a robust solution for resource optimization through its innovative technology, significantly alleviating the burden on DevOps teams. As it integrates seamlessly into our Kubernetes clusters, it has become a critical component of our infrastructure. Without the solid support from Cast.AI, the integration process could have been risky and complex.
What truly sets Cast.AI apart is how it simplifies onboarding and ensures that users can effectively utilize all features. The support team is always prompt and effective, providing the necessary guidance to navigate the complexities of the tool. This level of assistance instills confidence in both the platform and the dedicated professionals behind it.
What do you dislike about the product?
I have no significant complaints about Cast.AI; any issues encountered during our integration journey have been promptly resolved by the support team or even directly by the developers.
What problems is the product solving and how is that benefiting you?
Reduce resource waste and lowering overall cost of our cloud infrastructure
