Cast AI - EKS fully automated cost optimization and monitoring

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?

What I like best about CAST AI is how effectively it automates cloud cost optimization without requiring constant manual intervention. The platform gives good visibility into our cloud environment, identifies areas where we can reduce unnecessary spend, and helps optimize resources based on actual usage. The automation and recommendations are particularly useful because they allow the team to focus on higher-value engineering work rather than continuously monitoring and adjusting infrastructure.

What do you dislike about the product?

The initial setup and configuration can take some time, especially when getting familiar with the platform and its different optimization options. Some recommendations may also require additional review before being implemented in a production environment. That said, once the platform is configured properly, the ongoing management is relatively straightforward.

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

CAST AI is helping us address cloud cost management and resource optimization. It gives us better visibility into where cloud resources are being over- or under-utilized and helps automate optimization based on actual workload requirements. This reduces the amount of manual effort needed to manage infrastructure and helps us avoid unnecessary cloud spend, while allowing the engineering team to focus more on application and product priorities.

What do you like best about the product?

Cast AI allows us to scale based on the needs of our traffic while saving lots of money. The team members are amazing! Chuck, Manav, Jenny, and Nathan.

What do you dislike about the product?

There isn’t much we dislike about Cast AI. If anything, the main improvement we’d like to see is the ability to scale ElastiCache as well.

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

We are wanting to scale horizontally now instead of vertically and that's a slow process so far.

What do you like best about the product?

What I like best about Cast AI is how it actually takes the daily grind of managing Kubernetes costs off my plate. The automated rightsizing and bin-packing just quietly clean up overprovisioned resources without me having to babysit node groups or run constant audits. I like the UI We’ve seen solid cost drops (around 40% in our case) while performance stayed stable, and the Spot Instance handling has been surprisingly reliable fewer interruptions than I expected. Integrations with AWS, GCP, and Azure felt pretty seamless, and onboarding was straightforward we were up and running without a ton of hand-holding. Support has been responsive whenever we’ve had questions, which made the whole setup a lot less stressful. The real win is the time back: instead of spending hours every week tuning clusters, the team can focus on actual product work.

What do you dislike about the product?

The main thing that can feel a bit rough is the initial learning curve with the more advanced policies and node templates. If your team isn’t already deep into Kubernetes, it takes a little time to get comfortable with all the knobs and make sure everything is set up the way you want. The cost reporting is useful, but sometimes I wish it went a bit deeper or was easier to slice by team or workload without extra work. It’s not a deal-breaker, but it can slow things down when finance asks for more detailed breakdowns. Overall the tool still saves us a lot of time and money, these are just the small friction points we run into.

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

Before Cast AI we were constantly fighting overprovisioned clusters and unpredictable cloud bills. Our team spent a lot of time manually adjusting node groups, rightsizing workloads, and trying to catch underutilized resources before the invoice landed. Now the platform handles most of that automatically continuous rightsizing, bin-packing, and smarter Spot usage mean we run leaner without sacrificing performance. We’ve cut our Kubernetes spend by roughly 40% and freed up several hours a week that used to go into cluster babysitting. That time goes straight back into shipping features instead of firefighting infrastructure.