Cast AI - EKS fully automated cost optimization and monitoring

What do you like best about the product?

The time saved scaling workloads automatically!

What do you dislike about the product?

In the past we had problems with their automatic recommendations getting applied multiple times. This is not happening now, they've improved their algorithms!

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

We have cases where rapid resource scaling is necessary. Cast AI helps with it, giving more headroom to workloads that need short-time resouce bursts.

What do you like best about the product?

Turnkey finops automation. Very skilled and highly responsive support team.

What do you dislike about the product?

Limited fine grained auth model in the administrative UI

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

CastAI helps fighting over provisioning of cloud infrastructure, often caused by lack of awareness or knowledge in development teams. With CastAI automation we do not need to train all these teams but can optimize automatically.

What do you like best about the product?

I like CAST AI because it makes Kubernetes optimization truly autonomous. It handles the hardest and most time-consuming parts of K8s operation with features like fully autonomous optimization, which means zero manual tuning for us. The instant and reliable autoscaling is impressive, and I love the intelligent spot instance usage that offers massive savings without risk. The clear and actionable cost visibility is another standout feature. Our initial setup experience with CAST AI was smooth and straightforward, with fast and easy cluster onboarding that had no impact on our existing workloads. Plus, we received clear recommendations right after setup.

What do you dislike about the product?

There are a few areas where the experience could be improved: some advanced features have a learning curve, the UI could be more streamlined in certain areas, recommendations could offer more context, and reporting could be more customizable.

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

I use CAST AI to automate Kubernetes cluster optimization, solve cloud cost challenges, eliminate manual resource tuning, and overprovisioning issues. It provides real-time rightsizing and makes autoscaling efficient, handling spot instance complexity without risk.

What do you like best about the product?

I like how CAST AI has improved my resource efficiency and addressed complexities in Kubernetes. Its features in automation, optimization, and security are very helpful. The unified management, righting, auto-scaling, and compliance make it a powerful tool. It’s clear and more professional, and overall, it’s helping a lot.

What do you dislike about the product?

CAST AI is a powerful tool, but some areas for improvement include customization options, integration with existing tools, and providing more granular reports.

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

CAST AI improves resource efficiency, tackles Kubernetes complexity, security risks, and cloud cost inefficiency. It offers automation, optimization, unified management, auto-scaling, and enhances security and compliance.

What do you like best about the product?

what i like best about cast ai is how it turns kubernetes cost optimization from a manual, reactive task into an automated, intelligent system.

instead of relying on static node groups or basic cluster autoscaler logic, cast ai continuously analyzes pod resource requests, actual utilization, bin-packing efficiency, and real-time spot and on-demand pricing. based on that data, it dynamically selects the most cost-effective instance types without manual intervention.

a few things that stand out:

workload-aware autoscaling that optimizes instance type selection, not just node count

strong spot optimization with stability controls for production workloads

clear visibility into over-provisioning and inefficiencies

reduced operational overhead compared to manually tuning node groups

in my experience using it in a production kubernetes environment, the biggest value is continuous optimization without compromising reliability. it feels less like a monitoring dashboard and more like an active control layer for infrastructure cost efficiency.

What do you dislike about the product?

while cast ai delivers strong automation and cost optimization, there is a learning curve in fully understanding and trusting its automated decision-making, especially for teams used to managing infrastructure at a very granular level. in some cases, having deeper visibility into the exact reasoning behind instance selection or node replacement decisions would add even more confidence during audits or incident reviews. however, these are more about enhancing transparency and familiarity rather than limitations in capability, and with proper kubernetes configuration and governance, the platform performs reliably and efficiently.

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

cast ai is solving the core problem of kubernetes cost inefficiency and over-provisioned infrastructure by continuously optimizing compute selection, bin-packing, and spot utilization in real time. instead of relying on static node groups or periodic manual reviews, it automatically matches workloads to the most cost-effective instance types based on actual usage and market pricing. this has reduced waste from idle resources, improved cluster efficiency, and minimized the engineering time spent on manual tuning and capacity planning. the biggest benefit for me has been shifting from reactive cost control to continuous, automated optimization while maintaining production stability.