Cast AI - EKS fully automated cost optimization and monitoring

What do you like best about the product?

Cast AI is that it automatically helps optimize cloud resources. It makes it easier to keep infrastructure costs under control, improve workload performance, and cut down on the amount of manual cloud management required.

What do you dislike about the product?

The main thing I dislike about Cast AI is that it can take some time to understand and configure properly. The platform could also provide simpler controls and clearer explanations for some of its more advanced features.

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

Cast AI helps me tackle the challenge of managing and optimizing cloud infrastructure more efficiently. It automates resource optimization, cuts down on unnecessary cloud spending, and improves workload performance. Overall, it saves me time and gives me better visibility and control over cloud costs.

What do you like best about the product?

What I like best about cast AI is its fully autonomous, real-time kubernetes optimization that actively right-sizes clusters without requiring manual YAML tuning or constant oversight. For our high-concurrency streaming infrastructure, its predictive spot instance management is invaluable, seamlessly migrating workloads before interruptions hit to maintain 100% uptime while significantly driving down cloud compute spend.

What do you dislike about the product?

What I dislike about cast AI is that its aggressive auto-scaling policies can occasionally cause temporary pod scheduling latency during sudden, massive traffic spikes. Additionally, navigating its detailed cost-allocation dashboards requires a bit of a learning curve to properly configure custom budget alerts for specialized node pools.

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

Cast AI solves the issue of sky-high cloud compute costs and manual cluster management by automating continuous kubernetes right-sizing and spot instance provision. For our streaming operations, this eliminates over provisioned infrastructure during lull periods and ensures our high-concurrency video delivery scales cost-effectively without manual operator intervention.

What do you like best about the product?

What impresses me most about Cast AI is how much of the tedious Kubernetes infrastructure management it automates without compromising application reliability. Before using Cast AI, our team spent way too many hours manually tweaking node group configurations, reviewing rightsizing recommendations, and trying to balance cost against capacity.


Cast AI essentially puts cluster optimization on autopilot. Its real-time autoscaler dynamically provisions the exact instance sizes required based on actual pod demand, rather than relying on rigid, pre-defined node pools. The way it manages Spot instances is also incredible—it predicts interruptions before they hit and smoothly shifts workloads over to On-Demand instances with zero application downtime. Seeing clear cost breakdown dashboards alongside immediate 30% to 50% cloud bill reductions right out of the gate makes it one of the highest-ROI tools in our stack.

What do you dislike about the product?

While the automation works remarkably well, the initial learning curve can be a bit steep if you are relatively new to advanced Kubernetes concepts or complex autoscaling policies. Because the platform has so many granular toggles, guardrails, and policy options, taking the time to configure them properly to align with your organization's security and compliance standards requires careful planning.


Additionally, troubleshooting why the platform made a specific node selection or scaling decision during edge-case traffic bursts isn't always immediately obvious in the logs, so you sometimes have to dig through the dashboard telemetry to understand the automated logic behind certain provisioning choices.

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

Managing cloud infrastructure costs in a growing Kubernetes environment is a constant battle against over-provisioning. Developers naturally request more CPU and memory than their applications actually consume, which leads to massive amounts of wasted cloud spend across our clusters.


Cast AI solves this by continuously monitoring actual pod resource utilization and rightsizing workloads in real-time. It continuously replaces underutilized nodes with cost-effective, perfectly sized instances without manual engineering intervention. The benefit for our business has been twofold: we slashed our monthly cloud compute infrastructure bills by almost half, and our DevOps engineers no longer have to spend hours babysitting cluster configurations and manual scaling policies, freeing them up to focus on core product delivery.

What do you like best about the product?

Cast AI helps make Kubernetes cloud usage more cost‑efficient through smart automation.

What do you dislike about the product?

Cast AI can feel complicated to set up at first, and the pricing can ramp up quickly as you scale.

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

Cast AI helps reduce wasted cloud spend by automating Kubernetes scaling, which saves both money and time.

What do you like best about the product?

Easy to set up with my existing infrastructure, and it required only minimal code changes.

What do you dislike about the product?

The pricing plans are a bit on the higher side, and there isn’t much room for customization.

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

No need to manually set up my GCP clusters anymore; it automatically monitors them and makes decisions for me.

What do you like best about the product?

Cast AI is a tool that doesn't just stop at recommendations: it also executes. I manage several projects at once (Texuo, trading bots, and ERPs), I don't have time to be manually adjusting the requests and limits of the pods in Kubernetes.

What do you dislike about the product?

I am concerned about the costs when scaling my systems.

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

Cast AI automatically adjusts the resource requests and limits of my pods based on actual usage. For projects like Texuo ERP, where usage spikes are unpredictable, this means I don't have to pay for capacity that I almost never use.

What do you like best about the product?

Cast AI has made managing and optimizing our Kubernetes infrastructure costs significantly easier, automatically right-sizing resources and identifying waste that would be tedious to catch through manual monitoring. The interface is clear and easy to navigate, surfacing cost-saving opportunities without requiring deep Kubernetes expertise to interpret. Integration with our existing cloud setup was smooth, connecting directly to our infrastructure without needing major reconfiguration. Performance-wise, automated scaling decisions have kept resource allocation efficient without sacrificing application responsiveness.

What do you dislike about the product?

Some of the more aggressive automated scaling decisions occasionally needed manual review to make sure they aligned with actual traffic patterns rather than just cost efficiency. Initial setup for more complex

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

Cast AI has automated a significant part of our Kubernetes cost optimization, catching inefficiencies and right-sizing resources that would otherwise require constant manual monitoring. This has reduced our infrastructure spend meaningfully while keeping performance stable, without needing a dedicated engineer focused solely

What do you like best about the product?

The best thing I like about this Cast AI platform is its ability to automate Kubernetes and cost optimisation instead of only showing recommendations. It also identifies the over-provisioned resource such as right size workloads and adjust clusters capacity based on demand and this also made our lower cost instances on a very much efficient level. Their dashboard also provides clear visibility into our cost across the clusters and workloads, which made the namespaces making it easier to understand where cloud spending is going. Their user interface is very much intuitive and clean, and their integration is also a bit extensive, I would say.

What do you dislike about the product?

Their initial configuration failed can feel complex, particularly when setting automation policies and permission workload constraints and spotting instance rules. Teams can also find bit difficult on automation on protection infrastructure without even understanding its decision-making process for the 3rd party platform. The reporting interface is useful but some uses are a bit difficult, along with managing several other clusters.

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

This platform helps us by solving the Kubernetes over-provisioning and underutilised resources, where we can predict cloud cost and the manual effort required to continuously optimise clusters. It also helps us by automating match capacity with workload demand and provides a detailed cost allocation along with the Kubernetes resources, which is a major add-on. Overall, this platform helped us by reducing the unnecessary cloud spending and also saved the engineering team time and allowed the cluster to scale more effectively, while making the overall workload availability and hence the overall platform increased our overall cloud performance, I would say.

What do you like best about the product?

What I like best about CAST AI is its ability to automatically optimize Kubernetes clusters while significantly reducing cloud costs. The platform automates tasks like workload right-sizing, node scaling, and Spot instance management, which saves time and minimizes manual infrastructure management. I also appreciate the clear dashboard and cost visibility, making it easy to track resource usage, identify optimization opportunities, and monitor savings in real time. Overall, it helps improve operational efficiency without sacrificing application performance or reliability.

What do you dislike about the product?

One downside of CAST AI is that it can take time to understand and configure all of its optimization features, especially for users who are new to Kubernetes. Some advanced settings and recommendations could be explained more clearly, and troubleshooting automated decisions isn't always straightforward. More detailed documentation, simpler onboarding, and deeper customization of automation policies would make the platform easier to use.

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

CAST AI solves the challenge of managing Kubernetes infrastructure efficiently while keeping cloud costs under control. It automates resource optimization, cluster scaling, and workload placement, reducing the need for manual intervention. This helps lower cloud spending, improves application performance and reliability, and allows our team to spend more time on development instead of infrastructure management.

What do you like best about the product?

Autonomous Kubernetes cost optimization with performance scaling.

What do you dislike about the product?

Risk of temporary service disruptions from aggressive automated node draining

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

Excessive cloud spend caused by over-provisioned Kubernetes clusters. Time-consuming, manual infrastructure rightsizing and capacity planning. High operational risk and complexity when managing spot instances.