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Automatic Scaling That Saves Time
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.
Turnkey FinOps Automation with Highly Responsive Support
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.
Autonomous K8s Optimization with Smooth Onboarding
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.
Powerful Tool for K8s Management and Cloud Efficiency
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.
CAST AI Makes Kubernetes Cost Optimization Truly Automated and Reliable
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.
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.
Automated Rightsizing That Cuts Cloud Spend Without Sacrificing Performance
What do you like best about the product?
The automated workload optimization and rightsizing capabilities are the most effective features. The platform’s ability to analyze resource requirements in real-time and automatically adjust instances ensures that the infrastructure remains efficient without requiring constant manual intervention from the engineering team. This lead to a measurable reduction in cloud spend while maintaining cluster performance.
What do you dislike about the product?
While the initial returns were simple to realize on lower environments, the transition to production required more effort. We found that iterative tuning was necessary to ensure there was no application degradation or performance impact. A more seamless "plug-and-play" experience for high-traffic production workloads would be a valuable improvement.
What problems is the product solving and how is that benefiting you?
Cast AI addresses the challenge of managing complex cloud infrastructure costs and resource allocation. By automating the rightsizing of clusters and optimizing instance selection, it removes the manual burden of infrastructure tuning from our engineering teams.
Clear Infrastructure Cost Insights with Cast Ai
What do you like best about the product?
Cast Ai provides a clear overview of my infrastructure costs. It helps me understand where the spend is going and makes it easier to identify the lowest prices.
What do you dislike about the product?
It has recently become more expensive. Maybe that’s due to scaling up or adding extra features we don’t need.
What problems is the product solving and how is that benefiting you?
It significantly lowers the cost of the nodes we need to run our jobs. We use it primarily for cost efficiency.
Beautiful Scaling Mapping and Read-Write View for Zero-Downtime Strategies with Less Cost
What do you like best about the product?
The best thing and the best feature of Cast AI is beautiful mapping of
1. nodes scaling
2. horizontal scaling
3. provides read only view so LLMs can learn and optimise the strategy instead of just implementing directly to our environment and learns itself 0 downtime strategies.
4. Their customer support is too good, For P0 issues Chandani from castAI is available on prompt basis
5. We can implement castAI just by providing the enough IAM permissions, and easily installing castAI into our EKS environment within 30 mins.
6. Our infra cost is reduced to 30% by using castAI for just like 40-50 days.
1. nodes scaling
2. horizontal scaling
3. provides read only view so LLMs can learn and optimise the strategy instead of just implementing directly to our environment and learns itself 0 downtime strategies.
4. Their customer support is too good, For P0 issues Chandani from castAI is available on prompt basis
5. We can implement castAI just by providing the enough IAM permissions, and easily installing castAI into our EKS environment within 30 mins.
6. Our infra cost is reduced to 30% by using castAI for just like 40-50 days.
What do you dislike about the product?
There is nothing to dislike, but there can be one improvement
We can have correct mapping if we are using nginx-ingress, as we have to map target groups of nginx ingress in castAI console.
We can have correct mapping if we are using nginx-ingress, as we have to map target groups of nginx ingress in castAI console.
What problems is the product solving and how is that benefiting you?
We donot have to bump to EKS console to view things, as castAI gives best user interface with extra capabilties and eliminated the need of having karpenter. It also eliminated the need of mapping nodeSelectors, affinity, taints, tolerations as we can manage them on castAI by just a go.
Easy-to-Use Tool with Useful Workload and Node Balancing
What do you like best about the product?
Workload balancing and node balancing are useful overall, and the tool is easy to use.
What do you dislike about the product?
It’s expensive. For now, it pays for itself, but it’s definitely not really cheap.
What problems is the product solving and how is that benefiting you?
uese AI to balance workfload
Node Type Flexibility and Rebalancing Make Maintenance Easy. And the customer support is Bravo
What do you like best about the product?
Being able to choose specific types of nodes has helped us a lot. I also love the rebalancing feature, which makes upgrades and ongoing maintenance much easier.
What do you dislike about the product?
Well the logs/audit-logs are a bit less informative to debug somethings.
What problems is the product solving and how is that benefiting you?
The biggest problem its solving is scaling right amount/size of nodes and with features like graceful draining.
Also I like the feature that we can have multiple node templates/configuration and that can be used using labels and selectors
Also I like the feature that we can have multiple node templates/configuration and that can be used using labels and selectors
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