Cast AI - EKS fully automated cost optimization and monitoring

What do you like best about the product?

The cluster looks healthy, but I’m still digging into what’s actually behind that green status. I find myself jumping between workloads, tracing resource behavior, and gradually the performance picture starts to make sense. I can take a quick look at the user interface, make a small adjustment, then go right back to the workload, and that rhythm genuinely suits the way I troubleshoot infrastructure.

What do you dislike about the product?

The onboarding feels too basic for an experienced engineer, with explanations that cover things I already know. On top of that, one integration behaves differently than I expected, which adds an unnecessary pause and interrupts the workflow.

What problems is the product solving and how is that benefiting you?

We were constantly chasing wasted capacity across our Kubernetes clusters, and our workloads needed manual tuning whenever traffic shifted. Cast AI started giving us AI-driven recommendations for resource allocation, and the ROI became clear once we applied those changes and began cutting unnecessary cloud spend.

What do you like best about the product?

I like CAST AI for its innovative approach to multi-cloud GPU capabilities, which allow me to choose GPUs across multiple cloud providers and even on-prem. This feature has been particularly beneficial for us. I also appreciate the resource optimizations, with memory right-sizing helping us identify workloads that need additional capacity. The automated workload optimization and extra insights on GPU optimization are also valuable. Additionally, the setup was very easy, and the customer support from CAST AI has been great.

What do you dislike about the product?

I think one area where CAST AI could improve is by increasing the transparency of its optimization decisions. It would be really helpful if the platform could offer richer insights into CPU and memory performance, especially around memory kills. This would allow us to proactively right-size the nodes. Having dashboards, alerts, and data-driven usage insights would also be beneficial. For example, understanding how minimum and maximum node settings impact applications that can have irregular operations throughout the month would be great.

What problems is the product solving and how is that benefiting you?

I use CAST AI for automated workload optimization and resource right-sizing. It helps identify workloads needing more capacity and offers insights on GPU optimization across multi-cloud providers.

What do you like best about the product?

One of the strengths of CAST AI is its cost optimization feature. I really like how it reduces costs using bin-packing technology and monitors available resources worldwide to operate at the most optimal cost.

What do you dislike about the product?

Due to different infrastructure configurations in each company, security settings were not easy. In particular, more integration capability is needed to resolve conflicts with security solutions. The initial setup was fine in the development network, but it took time to resolve firewall or security solution conflicts in the production network.

What problems is the product solving and how is that benefiting you?

The CAST AI in use maintains system stability while optimizing costs. It allows monitoring of available resources worldwide at the most efficient cost across various infrastructure configurations.

What do you like best about the product?

Its automated Kubernetes cost optimization which helped reduce cloud spend, improves resource efficiency, and removes much of the operational overhead of managing cluster scaling and infrastructure.

What do you dislike about the product?

The biggest downside is the learning curve, along with the need to build trust in the automation. Teams may need some time to understand how the optimization decisions are made and how to configure the platform in the best way for their environment.

What problems is the product solving and how is that benefiting you?

Cast AI addresses the challenge of Kubernetes cost optimization and infrastructure management. It helps identify overprovisioned resources, automate scaling, optimize node usage, and take advantage of Spot Instances to reduce cloud spend. As a result, we spend less time manually tuning clusters, get stronger resource utilization, and have clearer visibility into our cloud costs while still maintaining application performance.

What do you like best about the product?

Automatically cuts our Kubernetes costs without hurting speed.

What do you dislike about the product?

The UI is getting crowded. With security scanning, rightsizing, and spend forecasting added, finding a simple autoscaler setting can take three menus, and the docs seem to lag behind new features. I've had to contact support a few times, though they do respond. Node Templates also have a steep learning curve. You need to understand tolerations and node affinities to keep sensitive workloads like Redis and API gateways off spot nodes. Early on, a rebalance during a traffic spike briefly slowed our Retool dashboard. The savings-based pricing is fair to me as an engineer, but it's harder to explain to finance than a fixed license.

What problems is the product solving and how is that benefiting you?

Our biggest problem was a Kubernetes bill we couldn't explain. We run self-hosted inference, vector databases, and background jobs behind n8n and Retool, and I over-provisioned out of fear of latency spikes and cold starts. High-memory nodes and GPUs ran around the clock while sitting idle most of the time. Tuning requests and limits by hand was painful, and the native cluster autoscaler was too slow.
CAST AI fixed this in three ways. First, it showed us the waste. Our GPU utilization was in the single digits, and the projected savings were 40-50%. Second, it made spot instances usable. Batch document processing, transcription, and embedding jobs now run on cheap spot nodes, and it drains nodes gracefully before interruptions, so we don't get failed jobs or lost requests. Third, its bin packing keeps consolidating workloads onto the cheapest nodes, so we're not paying for idle capacity.
The result is lower compute costs, and I get my time back. I no longer babysit Grafana dashboards or explain why our GCP bill doubled after deploying a new model. Our real-time inference endpoints stay responsive too, as long as pod disruption budgets are set up properly.

What do you like best about the product?

Before using it, our engineering team spent hours manually adjusting node groups and resizing instances. Cast AI completely automated this workflow through its real-time autoscaling and advanced bin-packing algorithms. The UI/UX is incredibly clean and made the onboarding process seamless—we were able to run a read-only analysis first to see exact potential savings.

What do you dislike about the product?

The main area for improvement is the initial trust barrier and configuration complexity when setting up fully automated policies for mission-critical production workloads. Letting an automated platform dynamically manage node scaling and spot instances can be daunting for large-scale enterprise environments. While the read-only onboarding dashboard is excellent, configuring the fine-grained mutation policies to ensure specific pods never experience disruption requires a steep learning curve. Additionally, while their core Kubernetes cost optimization is top-tier, the visibility and optimization tools for non-containerized cloud infrastructure or managed services (like databases and storage) could be more robust. Expanding their AI intelligence to offer deeper suggestions for idle non-K8s resources would make it a truly complete cloud financial management tool.

What problems is the product solving and how is that benefiting you?

Cast AI is solving the massive problem of cloud waste and the operational overhead associated with managing Kubernetes infrastructure. Before adopting the platform, our engineering team spent significant time manually monitoring cluster utilization, resizing node groups, and trying to safely utilize spot instances. Cast AI has turned this into a completely autonomous process. The primary benefit has been a direct 50%+ reduction in our monthly compute spend. Beyond the immediate financial ROI, it has freed up our DevOps engineers from reactive infrastructure tweaking. They can now focus on product feature development rather than cloud cost engineering. It has bridged the gap between our finance team's demand for budget control and our engineering team's need for high performance.

What do you like best about the product?

The autoscaling section really stands out to me, since the Workload Autoscaler keeps resource allocation much closer to actual demand. I also like how the UI keeps the infrastructure context visible, so the cost data doesn’t feel detached from the underlying workloads and clusters. It’s equally noticeable that the integrations fit naturally into the Kubernetes environment, which means there’s no need to completely rethink the existing operational model.

What do you dislike about the product?

I don’t feel that spot optimization is a universal answer, since workload suitability still needs to be evaluated. The intelligence also still requires human context around application criticality and overall business priorities. Aside from that, everything else seems to be working properly so far.

What problems is the product solving and how is that benefiting you?

Bin Packing has brought the capacity picture together and reduced fragmented resource usage as before adopting Cast AI, several workloads used to sit across underused nodes. Savings Reports give us a clearer basis whenever finance asks whether the optimization work is producing measurable ROI.

What do you like best about the product?

It was great because it made it easy to optimize both costs and performance. It also offered a clear, at-a-glance view of wasted resources, which was really helpful when I needed to present supporting data to my colleagues and leadership.

What do you dislike about the product?

There’s still room for improvement when it comes to completeness, but I believe that’s something that will get better over time.

What problems is the product solving and how is that benefiting you?

Cast AI helps address real-time Kubernetes cost waste by dynamically autoscaling nodes and optimizing instance types. For us, it’s been valuable because it automatically reduces our cloud bill while also freeing our DevOps team from tedious, day-to-day infrastructure management.

What do you like best about the product?

I like how CAST AI gives me a clearer view of resource usage and helps identify optimization opportunities in our Kubernetes clusters. It makes Kubernetes costs feel less like a guessing game by providing visibility alongside optimization recommendations. This is genuinely useful for my day-to-day work, allowing me to focus on impactful changes rather than piecing everything together manually. I appreciate that it makes it easier to communicate spending details to people outside the DevOps team. CAST AI turns optimization into an ongoing process by keeping us aware of changing workloads, providing a clearer starting point for decisions without waiting for a cost spike. The tool's recommendations help transform detailed usage information into actionable next steps, which saves time and helps prioritize impactful work.

What do you dislike about the product?

The main thing I would improve is how easy it is to understand the reasoning and trade-offs behind an optimization recommendation. Saving money matters, but from a DevOps perspective, I also need to feel confident that a change would not affect workload stability. Clearer explanations, practical examples in the documentation, and a simpler way to find relevant settings would help with that. I don’t see it as a deal breaker. It’s more about making it easier to move from "this looks promising" to "I am comfortable applying it."

What problems is the product solving and how is that benefiting you?

I use CAST AI to manage Kubernetes costs effectively, providing clear insights into resource usage and optimization opportunities. It simplifies cost management, replaces guesswork, and aids in decision-making, making cost explanations and spending decisions easier for my team.

What do you like best about the product?

The best thing about CAST AI for me is how it simplifies Kubernetes infrastructure optimization. Instead of spending a lot of time manually analyzing resource utilization and looking for cost-saving opportunities, CAST AI provides clear visibility and recommendations in one place.

I particularly like its focus on automation, cost optimization, and efficient resource utilization. For someone working in Cloud Operations, these capabilities are valuable because they can help reduce operational overhead while maintaining an efficient cloud environment.

What do you dislike about the product?

From a Cloud Operations perspective, I would like to see more granular explanations behind some of the optimization recommendations. Before enabling automation or making infrastructure changes, it is important to clearly understand the potential impact. More detailed insights, documentation, and configuration guidance within the platform would make the overall experience even better.

What problems is the product solving and how is that benefiting you?

CAST AI helps address challenges around Kubernetes cost optimization, resource utilization, and infrastructure management. It provides better visibility into cluster usage and helps identify over-provisioned or underutilized resources.

For me, the main benefit is reducing the manual effort required to analyze infrastructure utilization and find optimization opportunities. This helps improve resource efficiency, control cloud costs, and allows the Cloud Operations team to focus more on operational stability and other critical tasks.