External reviews
190 reviews
from
External reviews are not included in the AWS star rating for the product.
Excellent Node Autoscaler That Fits Our Needs Perfectly
What do you like best about the product?
The node autoscaler is a great replacement for the one on GCP, and it works really well for our needs. It also lets us mix different types of machines, which makes it much easier to tailor the setup to what we want.
What do you dislike about the product?
So far, the workload autoscaler for our Java workload hasn’t been working as well as we expected.
What problems is the product solving and how is that benefiting you?
GCP’s node autoscaler wasn’t able to provide a solution that lets us mix both the machine kind and the instance type (spot or on-demand). Because of that, we couldn’t get a setup that’s close to our needs while still benefiting from an interesting discount.
Cast AI Delivers Fast Kubernetes Cost Savings with Smart Automation
What do you like best about the product?
Its ability to automatically optimize Kubernetes costs without sacrificing performance stands out. The automation around workload rightsizing and intelligent autoscaling saves a significant amount of time and greatly reduces manual effort. I also appreciate the clear visibility into cluster performance and cost metrics, which makes it easier to make informed decisions and stay on top of usage. Overall, the platform is user-friendly, integrates smoothly with existing cloud environments, and delivers measurable cost savings quickly. setup was guided by the support team and we are frequenlty using this to create nodegroups etc
What do you dislike about the product?
One downside of Cast AI is that the initial setup and fine-tuning can take some time, particularly in more complex Kubernetes environments. Although the automation is powerful, it can take a while to fully understand and configure all of the optimization features, and there may be a learning curve for teams that are new to Kubernetes cost management. In addition, having deeper customization options and more detailed reporting in certain areas would make the platform even stronger overall.
What problems is the product solving and how is that benefiting you?
Cast AI solves cloud cost waste and infrastructure management pain. It continuously optimizes resource usage, autoscaling, and spot instance management, reducing unnecessary spending. This means you spend less time manually tuning clusters and more time on real work, while keeping performance and reliability high. The automation also improves operational efficiency and frees up DevOps capacity for higher-value tasks.
Cut k8s Cluster Costs by ~60% and Freed Budget to Scale
What do you like best about the product?
It helped me reduce the k8 s cluster cost by approx 60%. This revenue saved helped us build and scale up other clusters with better services
What do you dislike about the product?
The UI could be a bit more intuitive. The AI-based auto-sizing also seems extremely aggressive, which has caused some operational issues for us. In the end, we had to turn it off.
What problems is the product solving and how is that benefiting you?
Cost for my infra
Autoscaler scheduling
Autoscaler scheduling
Flexible API with Responsive Engineering Support
What do you like best about the product?
What I have been involved most with is the API. So I like the flexibility it provides. We used it to build our cost dashboards that we need to for our internal cost monitoring
Also we had chats with the engineering team for some of our requests and they were very responsive which made collaboration smooth and helped us get our results in time.
Also we had chats with the engineering team for some of our requests and they were very responsive which made collaboration smooth and helped us get our results in time.
What do you dislike about the product?
My primary interaction was with the API overall Im very happy with it, maybe some more examples in the documentation would be nice.
What problems is the product solving and how is that benefiting you?
We use Cast AI cost exporting API to allocate costs at the workload level live. This level of detail its very important to us it gives us better cost visibility and enables us to take actions based on it.
Efficient Cost Optimization for Multi-Cloud
What do you like best about the product?
I like the valuable cost insights dashboard that helps us track and analyze our cluster expenses over time along with future cost predictions, making it easier to set and maintain our cost goals. The node recommendations by CAST AI ensure workloads run efficiently. If a node is over-provisioned, it suggests the right sized instances. I enjoy the integration with multiple clouds, including AWS, Azure, and GCP, as the onboarding experience with CAST AI has been extremely smooth. Additionally, I appreciate that many teams in our organization have switched to CAST AI because it is efficient and saves time for our team.
What do you dislike about the product?
Sometimes the suggestions are too aggressive for nodes and it may lead to workload discrepancy.
What problems is the product solving and how is that benefiting you?
I use CAST AI for easy k8s cost optimization, efficient infrastructure management, and smooth multi-cloud integration. It helps track expenses, predict costs, and optimize node provisioning, saving our team time and effort compared to our previous manual process.
Intuitive, Accurate Cost Management with CAST AI
What do you like best about the product?
I like CAST AI for its easy-to-navigate UI/UX and the data granularity, which helps in specifically tracking instance types. It gives me near-accurate costing and provides forecasted costs, which really helps with managing cloud accounts and their costs. The initial setup was straightforward, and we adopted CAST AI because of the UI-UX.
What do you dislike about the product?
none as of now
What problems is the product solving and how is that benefiting you?
I use CAST AI to manage cloud accounts and their cost. It gives me near-accurate costing and forecasts. The easy UI/UX helps navigate accounts, and data granularity aids in specific instance type tracking.
Excellent Resource Allocation, High Cost Barrier
What do you like best about the product?
I love CAST AI's ability to let me right-size the instance type based on its findings and recommendations. It helps us identify where we over-provision our EC2 fleet and adjust it optimally to reduce AWS spending. The initial setup was pretty easy.
What do you dislike about the product?
We're running into infrastructure drift that we have to manage outside of CAST AI. Infrastructure drift is a huge issue for us since what we see in our source controls (GitHub) is different from what actually is running in AWS/EKS. Also, from a budget perspective, CAST AI is cost prohibitive.
What problems is the product solving and how is that benefiting you?
Using CAST AI, I right-size workloads and dynamically scale with recommendations, helping identify over-provisioning in our EC2 fleet, which optimizes and reduces AWS spending.
Streamlined Node Management & Cost Optimization with CAST AI
What do you like best about the product?
I use CAST AI primarily for node management and workload optimization within our Kubernetes clusters. I like its ability to automatically select the most cost-effective nodes for our workloads, taking a lot of guesswork out of infrastructure management. The intelligent instance selection feature dynamically chooses the optimal mix based on performance requirements and cost efficiency. Additionally, I appreciate its recommendations for right-sizing workloads, which help to save resources. The setup is also quick and easy, and onboarding clusters onto CAST AI is straightforward, with Terraform assisting in managing CAST AI resources.
What do you dislike about the product?
One area that could be improved is dynamic workload right-sizing. While it's useful, it heavily relies on past actual usage to forecast and adjust resource requests and limits. It works, but in cases where traffic spikes unusually, it doesn't always adapt quickly enough.
What problems is the product solving and how is that benefiting you?
I use CAST AI for node management and workload optimization in Kubernetes clusters. It helps with cluster capacity management, auto scales, and optimizes resource allocation to prevent overprovisioning and high costs. Its intelligent instance selection and dynamic recommendations improve cost efficiency and right-sizing.
Enhancing Cluster Visibility and Reducing Costs with CAST AI
What do you like best about the product?
I’m genuinely impressed with the way CAST AI presents its user interface. The layout feels clean, intuitive, and thoughtfully designed, which makes it incredibly easy to navigate and understand without needing extensive documentation or onboarding. This intuitive experience allows me to make data‑driven decisions with confidence and quickly follow through with corrective actions whenever necessary.
Since adopting CAST AI, I’ve seen an almost 80% reduction in the manual effort previously required for continuous monitoring. Tasks that once demanded constant attention have now become streamlined and largely automated.
One feature I especially appreciate is the clear visibility into cost analytics. CAST AI distinctly highlights the actual cost versus the optimized effective cost, making it simple to understand the financial impact of its automation. The platform also provides transparent insights into savings achieved through right‑sizing and resource allocation based on real usage patterns. This level of clarity significantly helps me with planning, forecasting, and overall execution.
Additionally, the initial setup process was remarkably quick and hassle‑free, allowing me to start leveraging its capabilities almost immediately.
Since adopting CAST AI, I’ve seen an almost 80% reduction in the manual effort previously required for continuous monitoring. Tasks that once demanded constant attention have now become streamlined and largely automated.
One feature I especially appreciate is the clear visibility into cost analytics. CAST AI distinctly highlights the actual cost versus the optimized effective cost, making it simple to understand the financial impact of its automation. The platform also provides transparent insights into savings achieved through right‑sizing and resource allocation based on real usage patterns. This level of clarity significantly helps me with planning, forecasting, and overall execution.
Additionally, the initial setup process was remarkably quick and hassle‑free, allowing me to start leveraging its capabilities almost immediately.
What do you dislike about the product?
I’ve noticed that during the initial pod initialization, CAST AI doesn’t really catch up with the metrics, Following are details
Key Observations About Pod Initialization Metrics in CAST AI
Initial pod‑startup metrics are not fully captured
During the very first phase of pod initialization, CAST AI appears to miss short‑lived spikes in resource demand. This leads to incomplete or inaccurate metric collection for that specific window.
Short bursts of CPU requirements go unreported
If a pod briefly requires a full 1 core at startup—even for a fraction of a second—CAST AI currently does not record this spike. As a result, the platform overlooks an important requirement needed for successful initialization.
Reported CPU utilization does not reflect real startup needs
When the pod’s average CPU usage settles around, say, 300 millicores, CAST AI reports only that average. It does not reflect that the pod initially needed 1 full core to boot successfully.
This leads to misleading CPU insights
Since CAST AI displays only the averaged metrics, it suggests that the pod’s CPU requirement is consistently low. However, operationally the pod still cannot start without that initial 1‑core burst.
Practical implication: startup failures despite “adequate” reported CPU
Even though the dashboard may show that 300 millicores is sufficient, the absence of a guaranteed 1‑core burst at initialization can cause pod startup delays or failures—none of which the current reporting highlights.
Overall effect on capacity planning and rightsizing
This gap in visibility can cause confusion during rightsizing exercises, as CAST AI does not reflect the full picture. Teams might allocate too little CPU based on averaged metrics, unaware of the critical startup requirement.
Key Observations About Pod Initialization Metrics in CAST AI
Initial pod‑startup metrics are not fully captured
During the very first phase of pod initialization, CAST AI appears to miss short‑lived spikes in resource demand. This leads to incomplete or inaccurate metric collection for that specific window.
Short bursts of CPU requirements go unreported
If a pod briefly requires a full 1 core at startup—even for a fraction of a second—CAST AI currently does not record this spike. As a result, the platform overlooks an important requirement needed for successful initialization.
Reported CPU utilization does not reflect real startup needs
When the pod’s average CPU usage settles around, say, 300 millicores, CAST AI reports only that average. It does not reflect that the pod initially needed 1 full core to boot successfully.
This leads to misleading CPU insights
Since CAST AI displays only the averaged metrics, it suggests that the pod’s CPU requirement is consistently low. However, operationally the pod still cannot start without that initial 1‑core burst.
Practical implication: startup failures despite “adequate” reported CPU
Even though the dashboard may show that 300 millicores is sufficient, the absence of a guaranteed 1‑core burst at initialization can cause pod startup delays or failures—none of which the current reporting highlights.
Overall effect on capacity planning and rightsizing
This gap in visibility can cause confusion during rightsizing exercises, as CAST AI does not reflect the full picture. Teams might allocate too little CPU based on averaged metrics, unaware of the critical startup requirement.
What problems is the product solving and how is that benefiting you?
I use CAST AI extensively for end‑to‑end cluster management, including monitoring, analyzing resource utilization, and optimizing both cost and performance. The platform has significantly streamlined my operations by automating many of the routine oversight tasks that previously required continuous manual effort. In fact, it has reduced my manual monitoring workload by nearly 80%, allowing me to focus more on strategic improvements rather than day‑to‑day checks.
The intuitive and thoughtfully designed UI plays a major role in this efficiency. It presents complex metrics and optimization insights in a clear, easy‑to‑interpret manner, enabling me to make informed, data‑driven decisions with confidence. Additionally, CAST AI highlights cost savings transparently—showing both actual and optimized spending—which makes it much easier to track financial impact and justify optimization initiatives.
Overall, CAST AI has become an essential part of my workflow for maintaining efficient, cost‑effective, and high‑performing Kubernetes environments.
The intuitive and thoughtfully designed UI plays a major role in this efficiency. It presents complex metrics and optimization insights in a clear, easy‑to‑interpret manner, enabling me to make informed, data‑driven decisions with confidence. Additionally, CAST AI highlights cost savings transparently—showing both actual and optimized spending—which makes it much easier to track financial impact and justify optimization initiatives.
Overall, CAST AI has become an essential part of my workflow for maintaining efficient, cost‑effective, and high‑performing Kubernetes environments.
CastAI Optimizes Kubernetes Cost & Performance with Best-in-Class Support
What do you like best about the product?
CastAI delivers strong value in optimizing both Kubernetes cluster cost and performance. We’ve been using it in production and can confidently vouch for its impact based on our experience. Their support is best-in-class—the CastAI team works with us daily to resolve issues, plan ahead, and improve our cluster architecture. It’s also easy to implement and operate. We’re in the process of integrating CastAI across all of our clusters to drive additional savings and performance, and we use it regularly.
What do you dislike about the product?
We’d like to see a dashboard that highlights cost savings and performance optimizations at both the enterprise and org levels. This would help our leadership quickly understand, at a glance, how well CastAI is working for us.
What problems is the product solving and how is that benefiting you?
It has helped us save money while also improving the performance of our clusters, which in turn supports the solutions we deliver to our clients.
showing 81 - 90