Cast AI Reviews & Product Details

What I like best about Cast AI is how effectively it combines cost optimization with operational simplicity. It continuously analyzes our Kubernetes workloads and automatically right-sizes nodes, scales clusters, and leverages spot instances without requiring constant manual tuning from our DevOps team. The visibility into resource utilization and savings is clear and actionable, which makes it easier to justify infrastructure decisions internally. Beyond the cost savings, the real value is the time saved and the confidence that the cluster is always running in an optimized state without daily intervention. Review collected by and hosted on G2.com.

One downside is that some of the more advanced configuration and optimization features require a deeper understanding of Kubernetes and cloud infrastructure to fully leverage. While the basics are easy to set up, fine tuning policies and understanding the impact of certain automation decisions can take time. In addition, more granular cost reporting and forecasting capabilities would be helpful for organizations that need detailed financial breakdowns across teams or projects. Review collected by and hosted on G2.com.

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 Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

It helps us optimize our K8 clusters and reduce costs. The UI is great and clearly shows how much we’ve saved so far, as well as what can still be improved within our cluster. The workload optimizer is also a really useful feature. Review collected by and hosted on G2.com.

It’s hard to find logs for certain things, and it’s also hard to understand why something isn’t working when an issue comes up. For example, recently my scheduled rebalancing wasn’t working correctly, and even the support team couldn’t figure out why at first. After a lot of digging, we found it was because one machine was stuck in a weird state after a previous rebalancing. It wasn’t easy to track down what caused this, and it seemed like support wasn’t able to identify the issue right away either. Review collected by and hosted on G2.com.

Cast AI automates node orchestration and right-sizing, using advanced bin-packing to ensure high cluster density and performance. It effectively manages spot instance lifecycles with automated fallback, significantly reducing compute overhead and manual infrastructure tuning Review collected by and hosted on G2.com.

The initial configuration of IAM permissions and automation guardrails requires a careful setup phase before fully handing over infrastructure control Review collected by and hosted on G2.com.

Instead of teams spending hours right-sizing nodes, tweaking autoscalers, or guessing instance types, Cast AI continuously analyzes workload requirements and automatically:

Selects the most cost-efficient instance types

Optimizes cluster autoscaling in real time

Uses spot instances intelligently to reduce costs

Eliminates overprovisioning without impacting performance Review collected by and hosted on G2.com.

Vendor dependency: Relying on automation to make core infrastructure decisions increases our operational dependence on a third party. Review collected by and hosted on G2.com.

I appreciate the range of diverse resources that the tool offers and, in addition to the constant updates focused on improving the tool, it is possible to notice the appreciation of those who work on this project and their commitment to excellence. The ability to identify how much each team's application running on the cluster costs is sensational. Review collected by and hosted on G2.com.

Sometimes, its scaling is not that interesting. I don't know if this happens because of the calculations of Requests and Limits, but it often tries to spin up some nodes with absurd amounts of memory, which would make me spend much more than save. Therefore, it is necessary to impose several limits and not let it run loose; otherwise, the cost may end up increasing instead of decreasing. Review collected by and hosted on G2.com.

I love how CAST AI doesn't just point out where I'm overspending, but actually goes in and automates the fixes so I don't have to manually tweak clusters all day. Its ability to handle Spot Instances is a total lifesaver because it keeps my apps running smoothly while cutting my cloud bill by more than half. Honestly, it’s like having a dedicated DevOps pro working 24/7 to make sure we’re never paying for more than we actually use. Review collected by and hosted on G2.com.

One thing I find frustrating is that giving up that much control to an autonomous "black box" can be nerve-wracking, especially since it occasionally restarts workloads to move them and the reporting isn't always as granular as the native cloud billing I'm used to. Review collected by and hosted on G2.com.

I appreciate the range of diverse features that CAST AI offers, and in addition to the constant updates focused on improving the tool, it's possible to perceive the appreciation of those working on this project and their commitment to excellence. I like how the tool revolves around AI, but it allows for fine-tuning of configurations to achieve excellence. The ability to allocate different machines to nodes within the same pool without requiring multiple configurations is particularly beneficial. The initial setup is super simple, basically running two or three commands on the cluster and all the configuration is ready. Review collected by and hosted on G2.com.

I believe the notification aspect of CAST AI could be improved; it's very simple, which is good for usability, but bad because it makes it limited. It would be interesting to have the possibility of receiving error alerts and alerts when recovering from those errors, allowing us to actively monitor the tool's operation. Review collected by and hosted on G2.com.

I really like the way CAST AI is designed because it aligns well with our company culture. Their support is also very helpful, which I appreciate. I find the AI support integration features beneficial for addressing simple questions and debugging scenarios. The ease of enabling features like vertical auto scaling and scheduling rebalancing is quite user-friendly. I also value the early access feature that allows us to hibernate our development and staging clusters during off hours, which is very helpful for us. And one big feature that helped us a lot was the ability to allocate a part of workload in to spot machines. Review collected by and hosted on G2.com.

There's definitely a learning curve to the platform, especially understanding the concepts and how it scales costs for you. Also, the billing aspect takes some time to understand. And some guarantees on the billing aspects (like if the cost reduction doesn't happen as expected) would reduce the initial inhibition of customers to adapt the system. For us personally another challenge was understanding how to measure cast AI's performance once we start using their system but also our own system starts growing. Actually there are indicators in the cast AI for this (like the cluster score, CPU & Memory over-provisioning percentage etc). Tracking these actually helps us understand if the cast AI's optimisations are as good as when we started. This could be documented as an indicator for cast AI's efficiency which will help the customers a lot. Review collected by and hosted on G2.com.

I really like the simplicity of setting up CAST AI; it was super easy and took less than an hour. We just followed the docs, and within minutes, everything was running smoothly. One of the standout benefits for us has been the cost reduction. We've managed to reduce our costs by a huge 60% since using it. I appreciate how CAST AI ensures that pod limits and requests align with real usage thanks to rightsizing. Previously, managing node pools manually was a hassle. With CAST AI, it's fully automatic, so we don't have to worry about manually creating new node pools to match our resource needs anymore. Review collected by and hosted on G2.com.

We had a small glitch at some point where the cluster scaled up because of a poorly configured workload but it didn't scale down automatically which end-up augmenting our costs a lot. We definitely need a way to have alerting when the daily price is more than x% of the previous days. Review collected by and hosted on G2.com.