--- title: Cast AI Reviews meta_title: 'Cast AI Reviews 2026: Details, Pricing, & Features | G2' meta_description: Filter 229 reviews by the users' company size, role or industry to find out how Cast AI works for a business like yours. aggregate_rating: rating_value: 4.5 review_count: 229 scale: '5' date_modified: '2026-09-07' parent_category: name: IT Management url: https://www.g2.com/categories/it-management --- # Cast AI Reviews **Vendor:** Cast AI **Category:** [Cloud Cost Management Tools](https://www.g2.com/categories/cloud-cost-management) **Average Rating:** 4.5/5.0 **Total Reviews:** 229 **AI Verified:** At least 10 G2 reviewers have confirmed using this product's AI features and functionality. ## About Cast AI Cast AI is an automation platform for operating cloud-native and AI infrastructure at scale. It keeps applications fast and stable by continuously optimizing production systems and eliminating manual operations as environments scale. ## Cast AI Pros & Cons Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews. **What users like:** - Users benefit from **cost management** features in CAST AI, achieving significant savings and improved cluster management. (53 reviews) - Users value the **cost-saving benefits** of CAST AI, significantly reducing cloud expenses through efficient resource management. (53 reviews) - Users appreciate the **ease of use** of Cast AI, highlighting its straightforward setup and intuitive features. (50 reviews) - Users appreciate the **massive cost savings** offered by CAST AI through intelligent optimization and efficient resource management. (49 reviews) - Users find CAST AI's **pricing advantages** compelling, appreciating cost savings and easy management for optimal efficiency. (48 reviews) - Users commend CAST AI for its **cost-effective solutions** , achieving up to 60% savings on cloud bills without sacrificing performance. (42 reviews) - Auto Scaling (41 reviews) - Users value the **24/7 customer support** from CAST AI, ensuring quick resolution of issues around the clock. (41 reviews) - Users value the **cost-saving recommendations** from CAST AI, enhancing optimization and automation for Kubernetes clusters. (40 reviews) - Automation (39 reviews) **What users dislike:** - Users experience **scaling issues** with CAST AI, particularly during traffic spikes, causing disruptions and inefficiency. (13 reviews) - Users find the **pricing high** for small clusters, making Cast AI seem expensive despite its benefits. (12 reviews) - Users note a significant **learning difficulty** with advanced features, requiring time to fully understand the platform. (11 reviews) - Users face **poor documentation** leading to confusion and misunderstanding, impacting their overall experience with Cast AI. (10 reviews) - Users express concerns about **pricing issues** , noting complexities in cost understanding and the need for better transparency. (10 reviews) - Steep Learning Curve (10 reviews) - Users find the **UI navigation to be slow** , suggesting simplification and better accessibility for an improved experience. (10 reviews) - Difficulty in Usage (9 reviews) - Users express concerns about **integration issues** affecting cost accuracy and workload optimization in Cast AI. (9 reviews) - Complexity (8 reviews) ## Cast AI Reviews ### 1. Cast AI: Easy EKS Integration with Smart AI Scaling and Cost Control **Rating:** 4.5/5.0 stars **Reviewed by:** Anshuman S. | Advance Software Engineer, Mid-Market (51-1000 emp.) **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** Validated through LinkedIn **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** August 27, 2026 **What do you like best about Cast AI?** Cast AI is a complete solution for EKS Clusters. It's AI based scaling takes care of Node as well as workload autoscaling. It's very easy to integrate with AWS EKS clusters. I did it from scratch and completed it in an hour. There documentations are also very good. It helps in reducing and managing the costs as well. Overall UI/UX is also very good and user friendly. Performance is also very good depending on the policies that you select. **What do you dislike about Cast AI?** Cast AI is limited to EKS clusters only. It is also more beneficial if most of our services are deployed on eks only. Billing and pricing models are also little bit complex. **What problems is Cast AI solving and how is that benefiting you?** Cast AI helps in managing and autoscaling services workloads as well as node autoscaling. Cast AI also helps in saving costs based on our usage and it also recommends that how much resource we are using and how much actually we are consuming. It plays a very significant role in savings costs which goes upto 50% savings. After using Cast AI, our environments are more stable and cost effective. ### 2. Stop Overpaying for Kubernetes! This One Tool Changed How We Manage K8s **Rating:** 5.0/5.0 stars **Reviewed by:** Hruthik G. | Spatial Data Operations Associate, Computer Software, Small-Business (50 or fewer emp.) **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** Validated through LinkedIn **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** August 12, 2026 **What do you like best about Cast AI?** 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 Cast AI?** 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 Cast AI 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. ### 3. Automated Kubernetes Optimization with Clear Cost Visibility and Real Savings **Rating:** 4.5/5.0 stars **Reviewed by:** Ankit K. | Designer, Small-Business (50 or fewer emp.) **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** Validated through LinkedIn **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **G2 Icon:** Our network of Icons are G2 members who are recognized for their outstanding contributions and commitment to helping others through their expertise. **Reviewed Date:** August 05, 2026 **What do you like best about Cast AI?** 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 Cast AI?** 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 Cast AI 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. ### 4. Cast AI Simplifies Kubernetes Cost Optimization with Clear Insights and Smooth Integration **Rating:** 4.0/5.0 stars **Reviewed by:** Muhammed A. | Technical Project Manager , Information Technology and Services, Mid-Market (51-1000 emp.) **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** Validated through LinkedIn **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **G2 Icon:** Our network of Icons are G2 members who are recognized for their outstanding contributions and commitment to helping others through their expertise. **Reviewed Date:** August 06, 2026 **What do you like best about Cast AI?** 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 Cast AI?** 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 Cast AI 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 ### 5. Set-and-Forget Kubernetes Autoscaling With Major Cloud Cost Savings **Rating:** 4.0/5.0 stars **Reviewed by:** Arjun D. | QA Engineer, Mid-Market (51-1000 emp.) **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** Validated through Google One Tap using a business email account **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** July 29, 2026 **What do you like best about Cast AI?** What I love most about Cast AI is the automated, real-time right-sizing. Before using it, our engineering team spent countless hours manually looking at Grafana dashboards, trying to guess which AWS EC2 instances to use, and wrestling with Kubernetes cluster sizing. Cast AI completely takes that off our plate. It analyzes our actual resource demand in real time and automatically swaps out inefficient nodes for optimized, cost-effective instances without any downtime. The fact that it manages Spot Instances so smoothly—automatically moving workloads to On-Demand nodes if a Spot instance gets interrupted—has given us massive savings on our cloud bill without sacrificing application stability. It’s truly a "set-it-and-forget-it" optimization tool. **What do you dislike about Cast AI?** What I love most is how it optimizes Kubernetes on autopilot. It watches our traffic in real time and automatically swaps out nodes for cheaper, perfectly sized ones without any downtime. It even manages Spot instances seamlessly, slashing our cloud bill without the stress of crashes. The biggest downside is the pricing—they charge based on your total cluster size, not just the money they save you, so the bill gets expensive fast as you scale. Plus, the automation can be a bit of a black box during sudden traffic spikes, and fixing it means digging through some really messy documentation. **What problems is Cast AI solving and how is that benefiting you?** The biggest problem Cast AI solves is cloud waste from over-provisioning. Our team used to buy way more AWS compute than we needed just to avoid crashes, but Cast AI automatically scales our nodes up and down to match actual demand in real time. It also takes the risk out of using cheap Spot instances. It predicts when AWS is about to reclaim an instance and moves our workloads to a fresh node before anything drops. For me, that means instantly slashing our cloud bill by thousands of dollars without the stress of managing infrastructure or dealing with late-night alerts. ### 6. Autonomous Kubernetes Scaling That Cuts Cloud Spend 30–50% **Rating:** 5.0/5.0 stars **Reviewed by:** Darwyn M. | Closer, Small-Business (50 or fewer emp.) **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** Validated through Google One Tap using a business email account **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** August 27, 2026 **What do you like best about Cast AI?** The autonomous scaling automation for Kubernetes clusters (AWS EKS, GKE, or AKS) stands out right away. The platform handles real-time workload rightsizing, chooses the most cost-effective instance types, and predictively manages Spot nodes without causing service disruptions. In our case, it helped cut cloud infrastructure spend by 30% to 50% without the need for constant manual tuning. **What do you dislike about Cast AI?** The initial learning curve for configuring more advanced optimization policies can feel a bit steep. Also, giving a third-party SaaS automated access to our production clusters calls for a thorough internal security review before we can roll it out fully. **What problems is Cast AI solving and how is that benefiting you?** It eliminates the need to manually tweak YAML files or constantly manage node pools. It helps prevent over-provisioning, saves DevOps engineers hours of tedious maintenance work, and offers granular visibility into cloud spending by cluster or service. ### 7. Intelligent Automation in Kubernetes that Drastically Reduces Cloud Bills **Rating:** 5.0/5.0 stars **Reviewed by:** Yostyn C. | Recruitment Coordinator, Small-Business (50 or fewer emp.) **Validated Reviewer:** Validated through LinkedIn **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **AI Translated:** This review has been translated from Spanish; Castilian using AI. **Reviewed Date:** August 25, 2026 **What do you like best about Cast AI?** its total and intelligent automation for cost optimization in Kubernetes environments. Unlike traditional tools that only deliver manual recommendations, Cast AI acts autonomously by adjusting node sizes in real-time (right-sizing), managing the purchase and replacement of Spot instances without service interruptions, and selecting the most efficient infrastructure according to actual demand. The ability to drastically reduce cloud bills (often between 50% and 70%) without requiring constant human supervision or altering the daily work of DevOps teams is its greatest strength. **What do you dislike about Cast AI?** Although it is an extremely powerful platform, its main weakness is often the initial learning curve and concerns about control. **What problems is Cast AI solving and how is that benefiting you?** Cast AI primarily addresses the waste of financial and computational resources in Kubernetes clusters, as well as the manual operational burden faced by engineering teams. The platform tackles the problem of idle capacity being paid for—where companies pay for underutilized or poorly sized servers—by automating scaling, precise instance type selection, and secure integration of more economical Spot servers. ### 8. Great cost reductions on Kubernetes, but mandates careful IAM and security preparation. **Rating:** 4.5/5.0 stars **Reviewed by:** Krish P. | Web Development Intern, Computer Software, Enterprise (> 1000 emp.) **Validated Reviewer:** Validated through LinkedIn **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** August 18, 2026 **What do you like best about Cast AI?** I am a cyber security employee with a medium-sized tech company, and recently integrated CAST AI into our AWS EKS clouds to automate scaling and cost optimization of EKS clusters. My use case for it is to audit the IAM roles on the platform, to track the status of the many automated node-provisioning functions, and to prevent any migration of workloads from the platform from breaching our very tight security postures or compliance regulations.A feature that was most beneficial from a security point of view was the fact that they started with only an initial read-only analysis mode. We first checked the analyzer to determine exactly what it was that it wished to modify our infrastructure, and did not give it permission to do it beforehand. There is also surprisingly clear documentation on Role Based Access Control (RBAC) as well. When we were ready to enable active optimization, we had a clear idea of what the minimums IAM policy we needed were. The pleasant surprise is that CAST AI dynamically reallocates (rotates) pods so they are located at the least expensive nodes. This actually helped us to spot 2 or 3 poorly configured pods that were unknowingly using privileges of a host level and when moved and broke it disclosed these shortcomings and thus we can patch them. **What do you dislike about Cast AI?** This is the largest challenge: essentially, the feature of providing a third party SaaS solution automated access to your production Kubernetes clusters. The security board was a challenging process to obtain approval. It's critical that you double-check all the IAM permissions they are asking, otherwise you risk opening yourself up to future dangers. As mentioned before another weird thing is its noise for our security operations center. Due to the constant node creation and destruction to save costs, many false positive alert many alerts in our SIEM tools get raised from CAST AI. This is the type of change I found was rattling my threat detection systems a fair bit because it's a switch to infra and so this often showed anomalous behavior, which required a lot of custom exclusion rules to step down the alarm sirens. **What problems is Cast AI solving and how is that benefiting you?** Our engineering teams were spending too much money provisioning cloud compute as the largest issue that CAST AI addressed. They were scared to manually scale down the infrastructure in the event of unexpected traffic and they thus ended up with idle nodes sitting around. Through CAST AI, this whole lifecycle was seamless. It made the whole engineering department think about how to enhance the security of containers from my part of the safety fence. Due to the auto-rescheduling capabilities of the AI across various instance types and availability zones, our developers were forced to structure their containers as stateless and isolated, giving certain instances new static IP addresses.As a result of the AI's ability to reschedule workloads across different instances with different static IP addresses and availability zones, our developers were required to build the containers to be properly isolated and stateless. ### 9. Autonomous Kubernetes Optimization That Cuts Costs and Keeps Streaming Uptime High **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User in Computer Software | Mid-Market (51-1000 emp.) This reviewer's identity has been verified by our review moderation team. They have asked not to show their name, job title, or picture. **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** Validated through a business email account **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** August 14, 2026 **What do you like best about Cast AI?** 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 Cast AI?** 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 Cast AI 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. ### 10. Automated Kubernetes Cost Optimization With Clear, Intuitive Visibility **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User in Oil & Energy | Mid-Market (51-1000 emp.) This reviewer's identity has been verified by our review moderation team. They have asked not to show their name, job title, or picture. **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** Validated through a business email account **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **G2 Icon:** Our network of Icons are G2 members who are recognized for their outstanding contributions and commitment to helping others through their expertise. **Reviewed Date:** August 05, 2026 **What do you like best about Cast AI?** 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 Cast AI?** 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 Cast AI 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. ### 11. Cast AI Delivers Hands-Off AWS Cost Optimization with Smart Kubernetes Autoscaling **Rating:** 4.5/5.0 stars **Reviewed by:** Varun K. | Webmethods Administrator / SRE, Enterprise (> 1000 emp.) **Validated Reviewer:** Validated through LinkedIn **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** August 04, 2026 **What do you like best about Cast AI?** What stands out most about Cast AI is its ability to automatically optimize our AWS infrastructure costs without requiring constant manual intervention. The Kubernetes autoscaling works intelligently rightsizing nodes and pods based on actual workload demand rather than static configurations. This has directly translated into significant cloud cost savings for our team while maintaining application performance and availability. The visibility it provides into resource utilization across our AWS environment makes it easy to identify waste and act on it quickly. For any team managing cloud infrastructure at scale, Cast AI delivers **What do you dislike about Cast AI?** The initial configuration and onboarding process can be complex, especially for teams that are new to Kubernetes cost optimization. Fine-tuning the automation policies to match our specific workload patterns required considerable trial and error before we saw optimal results. Additionally, the pricing model can become costly as cluster size grows, making it harder to justify for smaller workloads. Better guided onboarding documentation and more granular policy controls would significantly improve the overall experience for new users. **What problems is Cast AI solving and how is that benefiting you?** Cast AI directly addresses the challenge of uncontrolled cloud spending on AWS, which is a common pain point for teams running Kubernetes workloads at scale. Managing node sizes, pod resources, and cluster capacity manually is time-consuming and error-prone — Cast AI automates all of this continuously in the background. The immediate benefit for our team has been measurable AWS cost reduction without sacrificing application performance or reliability. Beyond cost, it sol ### 12. Powerful Kubernetes Cost Automation, with a Learning Curve **Rating:** 3.5/5.0 stars **Reviewed by:** Jisca N. | IT and Language Specialist , Small-Business (50 or fewer emp.) **Validated Reviewer:** Validated through LinkedIn **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** August 04, 2026 **What do you like best about Cast AI?** What I like best about Cast AI is its ability to automate Kubernetes cost optimization without requiring constant manual intervention. It continuously analyzes workloads and automatically adjusts cloud resources to ensure applications have the capacity they need while minimizing unnecessary spending. This helps engineering teams reduce cloud costs, improve resource utilization, and spend less time on infrastructure management. I also appreciate its real-time monitoring, intelligent autoscaling, and clear visibility into cloud usage, which make it easier to optimize performance while maintaining reliability. **What do you dislike about Cast AI?** One downside of Cast AI is that it has a learning curve, especially for teams that are new to Kubernetes or cloud cost optimization. Because it automates infrastructure decisions, it can take time to fully understand and trust its recommendations. Some advanced features may also require careful configuration to align with an organization's specific policies and workloads. Additionally, while the platform provides valuable automation and insights, organizations may still need experienced engineers to oversee optimization strategies and handle complex or highly customized environments. **What problems is Cast AI solving and how is that benefiting you?** Cast AI helps solve the challenge of managing cloud infrastructure efficiently while controlling costs. It automates Kubernetes resource optimization, reducing overprovisioning and eliminating wasted cloud spending without compromising application performance. This allows our team to spend less time manually monitoring and adjusting infrastructure and more time focusing on delivering products and supporting customers. The result is lower operational costs, improved resource utilization, greater infrastructure reliability, and increased productivity across engineering and operations teams. ### 13. My Experience with CAST AI for Cloud Cost Monitoring **Rating:** 4.5/5.0 stars **Reviewed by:** Anil K. | Analyst, Small-Business (50 or fewer emp.) **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** Validated through a business email account **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** September 06, 2026 **What do you like best about Cast AI?** I liked Cast AI dashboard, it shows all clusters cost monitoring at one place. It can manage many clusters very easily and it works well without lagging. **What do you dislike about Cast AI?** I dislike its pricing for cloud sessions which are on higher compared to other tools and there are many features which took me lot more time to understand and use. **What problems is Cast AI solving and how is that benefiting you?** CAST AI helped me get a better view of cloud infrastructure costs and resource usage. It makes it easier to see where resources are being used and where there may be room to reduce unnecessary usage. ### 14. Cast AI Makes Kubernetes Optimization Effortless **Rating:** 4.5/5.0 stars **Reviewed by:** Abba M. | system administrator, Mid-Market (51-1000 emp.) **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** This review contains authentic analysis and has been reviewed by our team **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** August 24, 2026 **What do you like best about Cast AI?** I like how cast ai automates kubernetes optimization instead of requiring constant manual adjustments it makes resources management much easier **What do you dislike about Cast AI?** the automation is powerful but i would prefer even more control over exactly when certain optimization changes are applied cast ai offer immediate and deferred application modes but more granular control can still be useful **What problems is Cast AI solving and how is that benefiting you?** cast ai is helping me reduce kubernetes Claud waste caused by over provisioning and underutilized resources its automated right sizing and scaling help me use infrastructure more efficiently and reduce cloud costs ### 15. Revolutionized Our Kubernetes Optimization **Rating:** 5.0/5.0 stars **Reviewed by:** Dhruv V. | DevOps Engineer, Mid-Market (51-1000 emp.) **Validated Reviewer:** Validated through a business email account **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** July 08, 2026 **What do you like best about Cast AI?** I like CAST AI for its automated optimization, which has made a significant impact on managing multiple client clusters efficiently. Its ability to analyze resource usage and optimize node provisioning helps minimize infrastructure costs and reduces cloud waste. The automation feature enables rightsizing of workloads, maintaining efficiency at scale, and identifying underutilized compute resources. The initial setup process was smooth, making the transition from manual Kubernetes optimization seamless. **What do you dislike about Cast AI?** I think more granular client level reporting, better forecasting capabilities, and enhanced workload recommendations can be improved in the platform. Also, the initial setup process was smooth, but if you are running with multi-client with multi-environment, it will be a learning curve for new users. **What problems is Cast AI solving and how is that benefiting you?** I use CAST AI to optimize Kubernetes workloads, minimize infrastructure cost, and reduce cloud waste. It rightsizes workloads, automates node optimization, and saves effort in managing multiple client clusters, improving efficiency at scale. ### 16. Cast AI Removes Operational Effort with Smart, Automated Optimization **Rating:** 4.5/5.0 stars **Reviewed by:** Tony J. | cloud administrator, Mid-Market (51-1000 emp.) **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** This review contains authentic analysis and has been reviewed by our team **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** August 25, 2026 **What do you like best about Cast AI?** I like how much operational effort it removes from the team once the policies are configured many optimization tasks happen automatically **What do you dislike about Cast AI?** cast ai is a powerful but i think the user experience could be improved by reporting and pricing information more straightforward **What problems is Cast AI solving and how is that benefiting you?** one of the biggest benefits is that i no longer have to constantly monitor and manually adjust resources allocations cast ai handles much of that optimization automatically which save engineering time and helps control costs ### 17. Automatic Workload Rightsizing That Optimizes CPU and Memory Effortlessly **Rating:** 4.0/5.0 stars **Reviewed by:** William S. | Cloud administrator, Mid-Market (51-1000 emp.) **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** This review contains authentic analysis and has been reviewed by our team **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** August 24, 2026 **What do you like best about Cast AI?** I really like the automatic workload rightsizing cast ai adjust cpu and memory resources base on actual usage which helps avoid over provisioning **What do you dislike about Cast AI?** there is a learning curve when configuring the more advanced optimization policies it take some time to understand how the different settings affect workloads **What problems is Cast AI solving and how is that benefiting you?** cast ai help me control unpredictable cloud costs by automatically adjusting compute capacity to match workload demand this give me better resource utilization and makes my infrastructure spending more predictable ### 18. Hands-Off Node Sizing and Smarter Spot Handling with cast AI **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User in Computer Software | Small-Business (50 or fewer emp.) This reviewer's identity has been verified by our review moderation team. They have asked not to show their name, job title, or picture. **Validated Reviewer:** Validated through Google One Tap using a business email account **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** August 26, 2026 **What do you like best about Cast AI?** I have stopped thinking about node sizing. Before this I was doing capacity planning manually and every time a team launched something big I'd be adding nodes at odd hours. Now it just handles it in the background. Setup was quicker than I expected we ran it read-only first to see what it would do before letting it act, which made it easy to get buy-in from the rest of the team. No changes to our clusters. The spot handling is the other big one. It moves workloads before the interruption hits instead of after, so we get spot pricing without the usual anxiety about it. Cost visibility was a nice surprise too. Seeing actual vs requested usage per namespace showed us how much we were overprovisioning, which was more than I wanted to admit. And I also like the UI of cast AI and easy to integrate. **What do you dislike about Cast AI?** The pricing model took some getting used to. It's tied to what you save, which makes sense in principle, but it makes forecasting your own spend harder than a flat per-cluster number would. Getting the policies tuned right took longer than the initial setup did. The connect part is genuinely minutes, but figuring out the right constraints for our workloads — what can move, what can't, which node types to allow — was a few weeks of back and forth. The agentic runbooks are promising but still feel early. The approval workflow is the right call, though in practice it means someone still has to sit and review each action, so it's less hands-off than the "self-healing" framing suggests. I'd want a longer track record before letting it act unattended on anything production-critical. Same story with the GPU side. It's clearly where the roadmap energy is going, but it's noticeably less mature than the core autoscaling — which has years of clusters behind it. If GPU optimization is your main reason for evaluating, go in with expectations set. I'd also like more visibility into why the engine made a specific decision. When it picks an instance type or evicts a pod, I can see what happened but not always the reasoning, which is awkward when someone asks me to explain it — and that gets more uncomfortable the more autonomous the actions become. **What problems is Cast AI solving and how is that benefiting you?** Two things mainly — overprovisioning and the manual work around it. We were sizing clusters for peak and paying for it the other 90% of the time. Nobody wanted to trim requests because the downside of getting it wrong is a production incident, so everyone padded their numbers and we ate the cost. Cast AI rightsizes based on what workloads actually consume rather than what teams guessed at, so we recovered that headroom without anyone having to argue about YAML in a PR review. The bigger win for me personally is time. I used to do capacity planning on a schedule and get pulled in whenever a team was launching something. That mostly stopped. Node provisioning, bin packing, spot handling — it runs in the background and I check on it instead of driving it. Spot is the third one. We wanted spot pricing but couldn't stomach the interruption risk on anything user-facing. Because it predicts interruptions ahead of time and drains workloads before the node goes, we're running a lot more on spot than we would have on our own. Net effect: our cloud bill came down meaningfully, and my team spends time on platform work instead of babysitting infrastructure. ### 19. Cast ai Balances Cost and Performance with Reliable Workload Optimization **Rating:** 4.0/5.0 stars **Reviewed by:** Olivia Agnes O. | cloud architect, Mid-Market (51-1000 emp.) **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** This review contains authentic analysis and has been reviewed by our team **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** September 01, 2026 **What do you like best about Cast AI?** Cast ai does a great job balancing cost and performance it automatically optimizes workloads while helping maintain the resources applications need to run reliably **What do you dislike about Cast AI?** there is not much i dislike but the platform can feel complex initially better onboarding and simpler configuration would make it easier **What problems is Cast AI solving and how is that benefiting you?** cast ai addresses cloud cost management resource optimization and kubernetes complexity the main benefit is less manual work better resource utilization and lower cost ### 20. Cost Optimization Without Sacrificing Performance **Rating:** 4.5/5.0 stars **Reviewed by:** Mike Z. | system administrator, Mid-Market (51-1000 emp.) **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** This review contains authentic analysis and has been reviewed by our team **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** August 28, 2026 **What do you like best about Cast AI?** What stands out most is the combination of cost optimization and performance. Cast AI helps reduce unnecessary costs while keeping workloads properly resourced. **What do you dislike about Cast AI?** The documentation and onboarding could be clearer. Some configuration details can take extra time to work through the project. **What problems is Cast AI solving and how is that benefiting you?** I was dealing with overprovisioned workloads and unused capacity. Cast AI continuously analyzed resource usage and adjusted CPU allocation, which helps eliminate waste while maintaining application performance. ### 21. Cast AI Keeps Workloads Continuously Optimized **Rating:** 4.0/5.0 stars **Reviewed by:** Luke C. | infrastructure administrator, Mid-Market (51-1000 emp.) **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** This review contains authentic analysis and has been reviewed by our team **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** August 26, 2026 **What do you like best about Cast AI?** I like that cast ai continuously optimizes workloads rather than relying on occasional manual reviews it makes infrastructure management more proactive **What do you dislike about Cast AI?** the pricing could be more transparent and easier to understand especially for the teams **What problems is Cast AI solving and how is that benefiting you?** my biggest challenge was balancing performance with cloud costs cast ai help me find that balance by continuously adjusting resources based on actual workload requirements so i don’t have to rely on excessive over provisioning ### 22. Solid tool for cutting cloud costs and reducing infra toil **Rating:** 4.5/5.0 stars **Reviewed by:** Rahul Abishek K. | Senior DevOps Engineer, Mid-Market (51-1000 emp.) **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** Validated through a business email account **Source: Seller invite:** Invitation from a seller or affiliate. This reviewer was not provided any incentive by G2 for completing this review. **Reviewed Date:** March 10, 2026 At G2, we prefer fresh reviews and we like to follow up with reviewers. They may not have updated their review text, but have updated their review. **What do you like best about Cast AI?** The automation is genuinely impressive - once Cast AI is connected to our clusters, it handles the scaling decisions that used to eat up hours of our engineers' time each week. The cost savings kicked in pretty quickly after setup, and the visibility into where our cloud spend is going has been really useful. We had a multi-cluster setup and Cast AI handled it better than I expected. The recommendations are solid and the UI makes it easy to see what's happening without digging through logs. **What do you dislike about Cast AI?** The initial setup and onboarding documentation could be a bit clearer - there were a few gotchas around IAM permissions that took us longer to figure out than it should have. The alerting options feel a bit limited compared to what we're used to with other tools. Nothing that's been a dealbreaker, but there's room to improve on those fronts. **What problems is Cast AI solving and how is that benefiting you?** We were over-provisioning across our Kubernetes clusters and had no real visibility into where the waste was coming from. Cast AI helped us right-size workloads automatically and brought down our cloud bill noticeably within the first month. The auto-scaling also means our team isn't getting paged for manual interventions nearly as often, which has been a big quality-of-life improvement for the on-call engineers. ### 23. Lock and Bolt Infrastructure: Fire-and-Forget Cloud Savings for K8s **Rating:** 4.5/5.0 stars **Reviewed by:** Ajay B. | DevOps Engineer, Enterprise (> 1000 emp.) **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** Validated through a business email account **Source: Seller invite:** Invitation from a seller or affiliate. This reviewer was not provided any incentive by G2 for completing this review. **Reviewed Date:** March 10, 2026 **What do you like best about Cast AI?** The automated rebalancing and Spot Instance management are game-changers. Unlike other FinOps tools that just give you a list of suggestions to fix manually, CAST AI actually executes the changes in real-time. The Autoscaler is incredibly aggressive (in a good way) at bin-packing pods, which allowed us to shrink our cluster footprint significantly without any downtime. Also, their Spot fallback mechanism gives us the confidence to run production workloads on Spot instances because we know it will move them to On-Demand instantly if capacity drops. **What do you dislike about Cast AI?** While the onboarding is fast, there is a slight learning curve when it comes to fine-tuning policies for very complex stateful workloads. I also noticed that the Workload and Node autoscalers sometimes feel like they are operating on two different planes—it would be great to see even tighter coordination between the two so that resource requests and node provisioning are perfectly synced 100% of the time. Lastly, the pricing can feel a bit steep for very small, static clusters where there isn't much to optimize. **What problems is Cast AI solving and how is that benefiting you?** We were facing massive cloud waste (roughly 40%) due to over-provisioning and 'shadow' Kubernetes spending. CAST AI solved this by automating our rightsizing. Benefit 1: We reduced our AWS/GCP bill by nearly 50% within the first two months. Benefit 2: Our DevOps team no longer spends hours every week 'hand-tuning' instance types or manually handling Spot interruptions. It has effectively shifted our team from 'infrastructure babysitting' to actual feature development. ### 24. All-in-One Kubernetes Cost Optimization and Resource Automation **Rating:** 4.0/5.0 stars **Reviewed by:** Original . | System administrator, Mid-Market (51-1000 emp.) **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** This review contains authentic analysis and has been reviewed by our team **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** August 27, 2026 **What do you like best about Cast AI?** The biggest advantage for me is having Kubernetes optimization cost management and resource automation in one platform instead of managing several separate tools **What do you dislike about Cast AI?** the pricing could be more transparent and forecasting capabilities for breaking costs down across different projects **What problems is Cast AI solving and how is that benefiting you?** cast ai helped me turn kubernetes cost optimization from a manual ongoing tasks into a more automated process and help me scale infrastructure according to actual demand ### 25. Excellent Autoscaling That Adapts to Real-Time Demand **Rating:** 4.5/5.0 stars **Reviewed by:** Tony F. | it administrator, Mid-Market (51-1000 emp.) **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** This review contains authentic analysis and has been reviewed by our team **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** August 26, 2026 **What do you like best about Cast AI?** The autoscaling capabilities are excellent resources can scale according to real time demand so I don’t have to manually manage capacity **What do you dislike about Cast AI?** find tuning cast ai for complex state full workloads can require more hand on configuration and pricing information more straightforward **What problems is Cast AI solving and how is that benefiting you?** cast ai solves the challenge of efficiently managing kubernetes at scale automated and workload optimization have reduced the amount of manual turning to my team ### 26. Excellent Auto Scaling That Adapts to Real-Time Demand **Rating:** 4.5/5.0 stars **Reviewed by:** White M. | IT administrator, Mid-Market (51-1000 emp.) **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** This review contains authentic analysis and has been reviewed by our team **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** August 25, 2026 **What do you like best about Cast AI?** The auto scaling capabilities are excellent resources can scale according to real time demand so I don’t have manually manage capacity **What do you dislike about Cast AI?** i would like more granular reporting forecasting capabilities particularly for breaking cost down across different projects **What problems is Cast AI solving and how is that benefiting you?** i were dealing with overprovisioned workloads and unused capacity cast ai continuously analyzes resources usage and adjust cpu and memory allocations which helps eliminate waste while maintaining application performance ### 27. CastAI Automation Cut Wasted Compute and Improved Cost Transparency **Rating:** 4.0/5.0 stars **Reviewed by:** Vatsal D. | Devops Engineer, Mid-Market (51-1000 emp.) **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** Validated through Google One Tap using a business email account **Source: Seller invite:** Invitation from a seller or affiliate. This reviewer was not provided any incentive by G2 for completing this review. **Reviewed Date:** February 26, 2026 **What do you like best about Cast AI?** What stood out to me most was the automation. Once it was set up, CastAI continuously analyzed our workloads and adjusted resources in real time. We saw noticeable reductions in wasted compute, especially around underutilized nodes. The platform’s ability to automatically leverage Spot instances without compromising stability was a big win for us. It handled the complexity in the background, which gave our team more time to focus on product work instead of infrastructure tuning. The visibility into costs has also been valuable. Being able to break down spending by cluster and workload helped us understand exactly where our cloud budget was going. That transparency made it much easier to have productive conversations internally about optimization and accountability. **What do you dislike about Cast AI?** I seldom observed wrongful recommendations applied to some workloads where CastAI applied resources higher than the maximum available capacity on our EKS cluster which lead to some services staying in pending state without any way to control it. **What problems is Cast AI solving and how is that benefiting you?** Before implementing CastAI, managing our Kubernetes infrastructure costs felt like a constant balancing act. We were either overprovisioning to stay safe or spending too much time manually tweaking node sizes and autoscaling rules. After integrating CastAI, much of that manual effort disappeared. CastAI continuously analyzed our workloads and adjusted resources in real time, and we saw noticeable reductions in wasted compute—especially on underutilized nodes. The platform’s ability to automatically leverage Spot instances without compromising stability was a big win for us. It handled the complexity in the background, which gave our team more time to focus on product work instead of infrastructure tuning. The added visibility into costs has also been valuable. Being able to break down spending by cluster and workload helped us understand exactly where our cloud budget was going. That transparency made it much easier to have productive internal conversations about optimization and accountability. ### 28. Enhancing Cluster Visibility and Reducing Costs with CAST AI **Rating:** 5.0/5.0 stars **Reviewed by:** Prashant P. | Lead Data Engineer, Mid-Market (51-1000 emp.) **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** Validated through Google One Tap using a business email account **Source: Seller invite:** Invitation from a seller or affiliate. This reviewer was not provided any incentive by G2 for completing this review. **Reviewed Date:** February 11, 2026 At G2, we prefer fresh reviews and we like to follow up with reviewers. They may not have updated their review text, but have updated their review. **What do you like best about Cast AI?** I’m genuinely impressed with the way CAST AI presents its user interface. The layout feels clean, intuitive, and thoughtfully designed, which makes it incredibly easy to navigate and understand without needing extensive documentation or onboarding. This intuitive experience allows me to make data‑driven decisions with confidence and quickly follow through with corrective actions whenever necessary. Since adopting CAST AI, I’ve seen an almost 80% reduction in the manual effort previously required for continuous monitoring. Tasks that once demanded constant attention have now become streamlined and largely automated. One feature I especially appreciate is the clear visibility into cost analytics. CAST AI distinctly highlights the actual cost versus the optimized effective cost, making it simple to understand the financial impact of its automation. The platform also provides transparent insights into savings achieved through right‑sizing and resource allocation based on real usage patterns. This level of clarity significantly helps me with planning, forecasting, and overall execution. Additionally, the initial setup process was remarkably quick and hassle‑free, allowing me to start leveraging its capabilities almost immediately. **What do you dislike about Cast AI?** I’ve noticed that during the initial pod initialization, CAST AI doesn’t really catch up with the metrics, Following are details Key Observations About Pod Initialization Metrics in CAST AI Initial pod‑startup metrics are not fully captured During the very first phase of pod initialization, CAST AI appears to miss short‑lived spikes in resource demand. This leads to incomplete or inaccurate metric collection for that specific window. Short bursts of CPU requirements go unreported If a pod briefly requires a full 1 core at startup—even for a fraction of a second—CAST AI currently does not record this spike. As a result, the platform overlooks an important requirement needed for successful initialization. Reported CPU utilization does not reflect real startup needs When the pod’s average CPU usage settles around, say, 300 millicores, CAST AI reports only that average. It does not reflect that the pod initially needed 1 full core to boot successfully. This leads to misleading CPU insights Since CAST AI displays only the averaged metrics, it suggests that the pod’s CPU requirement is consistently low. However, operationally the pod still cannot start without that initial 1‑core burst. Practical implication: startup failures despite “adequate” reported CPU Even though the dashboard may show that 300 millicores is sufficient, the absence of a guaranteed 1‑core burst at initialization can cause pod startup delays or failures—none of which the current reporting highlights. Overall effect on capacity planning and rightsizing This gap in visibility can cause confusion during rightsizing exercises, as CAST AI does not reflect the full picture. Teams might allocate too little CPU based on averaged metrics, unaware of the critical startup requirement. **What problems is Cast AI solving and how is that benefiting you?** I use CAST AI extensively for end‑to‑end cluster management, including monitoring, analyzing resource utilization, and optimizing both cost and performance. The platform has significantly streamlined my operations by automating many of the routine oversight tasks that previously required continuous manual effort. In fact, it has reduced my manual monitoring workload by nearly 80%, allowing me to focus more on strategic improvements rather than day‑to‑day checks. The intuitive and thoughtfully designed UI plays a major role in this efficiency. It presents complex metrics and optimization insights in a clear, easy‑to‑interpret manner, enabling me to make informed, data‑driven decisions with confidence. Additionally, CAST AI highlights cost savings transparently—showing both actual and optimized spending—which makes it much easier to track financial impact and justify optimization initiatives. Overall, CAST AI has become an essential part of my workflow for maintaining efficient, cost‑effective, and high‑performing Kubernetes environments. ### 29. Centralized Kubernetes metrics and intuitive UI to optimize resources **Rating:** 4.0/5.0 stars **Reviewed by:** Fernando C. | Devops / Cloudops, Enterprise (> 1000 emp.) **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** Validated through Google One Tap using a business email account **Source: Seller invite:** Invitation from a seller or affiliate. This reviewer was not provided any incentive by G2 for completing this review. **AI Translated:** This review has been translated from Spanish; Castilian using AI. **Reviewed Date:** February 23, 2026 **What do you like best about Cast AI?** The centralization of Kubernetes metrics in an intuitive user interface, along with the configuration of nodes and workload autoscalers, facilitates resource optimization. **What do you dislike about Cast AI?** What complicates the use of the tool for us a bit is the installation through Helm, since we deploy it with Terraform using manifests. In that context, some components, such as the evictor, cause us issues when managing them without the user interface. **What problems is Cast AI solving and how is that benefiting you?** Cast AI helps us solve problems of overprovisioning and low efficiency in our Kubernetes infrastructure, as it automatically optimizes resource usage and selects more suitable instances according to actual demand. This mainly translates into a reduction in cloud costs, along with improved performance and greater application stability. Additionally, by automating optimization tasks that previously required manual intervention, it reduces the operational burden on the team and allows us to focus on other priorities. ### 30. Cast AI cloud computing **Rating:** 4.0/5.0 stars **Reviewed by:** CA Kushagra G. | Audit Experienced Associate, Mid-Market (51-1000 emp.) **Validated Reviewer:** Validated through LinkedIn **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** August 18, 2026 **What do you like best about Cast AI?** The best part is that this software adjusts CPU memory allocations and manages application load on the CPU. It helps to maximize efficiency and minimize cost and helps to tackle interruptions during work. **What do you dislike about Cast AI?** Sometimes, the cost of this feels a bit high, since the benefit-to-cost ratio doesn’t seem that strong. Also, its benefits feel somewhat limited because, at times, it can’t handle a heavy load due to certain applications. **What problems is Cast AI solving and how is that benefiting you?** It doesn't involve specialized infrastructure resulting in avoiding additional cost.It also helps to reduce downtime during heavy traffic thus saves time and avoid tensions during urgent deliverables. ### 31. Beautiful Scaling Mapping and Read-Write View for Zero-Downtime Strategies with Less Cost **Rating:** 5.0/5.0 stars **Reviewed by:** Chirag S. | Senior DevOps Engineer, Mid-Market (51-1000 emp.) **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** Validated through Google using a business email account **Source: Seller invite:** Invitation from a seller or affiliate. This reviewer was not provided any incentive by G2 for completing this review. **Reviewed Date:** February 12, 2026 **What do you like best about Cast AI?** 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. **What do you dislike about Cast AI?** 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. **What problems is Cast AI 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. ### 32. Smarter Kubernetes Optimization with Real Cost Impact **Rating:** 5.0/5.0 stars **Reviewed by:** Oded S. | SVP of R&D, Mid-Market (51-1000 emp.) **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** Validated through Google using a business email account **Source: Seller invite:** Invitation from a seller or affiliate. This reviewer was not provided any incentive by G2 for completing this review. **Reviewed Date:** February 11, 2026 **What do you like best about Cast AI?** What I like best about Cast AI is how effectively it combines cost optimization with operational simplicity. It continuously analyzes our Kubernetes workloads and automatically right-sizes nodes, scales clusters, and leverages spot instances without requiring constant manual tuning from our DevOps team. The visibility into resource utilization and savings is clear and actionable, which makes it easier to justify infrastructure decisions internally. Beyond the cost savings, the real value is the time saved and the confidence that the cluster is always running in an optimized state without daily intervention. **What do you dislike about Cast AI?** One downside is that some of the more advanced configuration and optimization features require a deeper understanding of Kubernetes and cloud infrastructure to fully leverage. While the basics are easy to set up, fine tuning policies and understanding the impact of certain automation decisions can take time. In addition, more granular cost reporting and forecasting capabilities would be helpful for organizations that need detailed financial breakdowns across teams or projects. **What problems is Cast AI solving and how is that benefiting you?** Cast AI is solving the problem of inefficient Kubernetes resource utilization and unpredictable cloud costs. Before using it, we were overprovisioning to avoid performance risks, which resulted in wasted spend and constant manual monitoring. Cast AI automates cluster scaling, right sizing, and spot instance management, which reduces overprovisioning while maintaining reliability. This directly benefits us by lowering infrastructure costs, improving resource efficiency, and freeing our engineering team from repetitive operational tasks so they can focus on higher value initiatives. ### 33. Cast AI Delivers Fast Kubernetes Cost Savings with Smart Automation **Rating:** 4.5/5.0 stars **Reviewed by:** Aswath P. | Senior Devops Engineer, Computer & Network Security, Mid-Market (51-1000 emp.) **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** Validated through Google One Tap using a business email account **Source: Organic:** Organic review. This review was written entirely without invitation or incentive from G2, a seller, or an affiliate. **Reviewed Date:** February 11, 2026 **What do you like best about Cast AI?** Its ability to automatically optimize Kubernetes costs without sacrificing performance stands out. The automation around workload rightsizing and intelligent autoscaling saves a significant amount of time and greatly reduces manual effort. I also appreciate the clear visibility into cluster performance and cost metrics, which makes it easier to make informed decisions and stay on top of usage. Overall, the platform is user-friendly, integrates smoothly with existing cloud environments, and delivers measurable cost savings quickly. setup was guided by the support team and we are frequenlty using this to create nodegroups etc **What do you dislike about Cast AI?** One downside of Cast AI is that the initial setup and fine-tuning can take some time, particularly in more complex Kubernetes environments. Although the automation is powerful, it can take a while to fully understand and configure all of the optimization features, and there may be a learning curve for teams that are new to Kubernetes cost management. In addition, having deeper customization options and more detailed reporting in certain areas would make the platform even stronger overall. **What problems is Cast AI solving and how is that benefiting you?** Cast AI solves cloud cost waste and infrastructure management pain. It continuously optimizes resource usage, autoscaling, and spot instance management, reducing unnecessary spending. This means you spend less time manually tuning clusters and more time on real work, while keeping performance and reliability high. The automation also improves operational efficiency and frees up DevOps capacity for higher-value tasks. ### 34. Cost Savings and Automated Cloud Optimization Made Easy **Rating:** 4.0/5.0 stars **Reviewed by:** Diteboho L. | Technical Administrator, Small-Business (50 or fewer emp.) **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** Validated through a business email account **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** August 27, 2026 **What do you like best about Cast AI?** Cost savings and automated cloud optimization. Works well with Slack, Grafana/Prometheus, Terraform, Helm, and Jira. **What do you dislike about Cast AI?** The initial setup can be complex, and some features take time to understand. **What problems is Cast AI solving and how is that benefiting you?** Reducing cloud costs & complexity via automation. Benefits: lower bills, less manual ops, faster scaling, and better resource efficiency. ### 35. Great tool for K8 cost savings and cluster optimization **Rating:** 4.5/5.0 stars **Reviewed by:** Rushil S. | Lead Platform architect, Small-Business (50 or fewer emp.) **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** Validated through a business email account **Source: Seller invite:** Invitation from a seller or affiliate. This reviewer was not provided any incentive by G2 for completing this review. **Reviewed Date:** February 11, 2026 **What do you like best about Cast AI?** It helps us optimize our K8 clusters and reduce costs. The UI is great and clearly shows how much we’ve saved so far, as well as what can still be improved within our cluster. The workload optimizer is also a really useful feature. **What do you dislike about Cast AI?** It’s hard to find logs for certain things, and it’s also hard to understand why something isn’t working when an issue comes up. For example, recently my scheduled rebalancing wasn’t working correctly, and even the support team couldn’t figure out why at first. After a lot of digging, we found it was because one machine was stuck in a weird state after a previous rebalancing. It wasn’t easy to track down what caused this, and it seemed like support wasn’t able to identify the issue right away either. **What problems is Cast AI solving and how is that benefiting you?** It helped us scale our cluster while also reducing costs by almost 50%. ### 36. Cast AI executes for you: automatic optimization of Kubernetes without manual adjustments **Rating:** 5.0/5.0 stars **Reviewed by:** Luis Eduardo U. | Gestor de Capitales, Small-Business (50 or fewer emp.) **Validated Reviewer:** Validated through LinkedIn **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **AI Translated:** This review has been translated from Spanish; Castilian using AI. **Reviewed Date:** August 07, 2026 At G2, we prefer fresh reviews and we like to follow up with reviewers. They may not have updated their review text, but have updated their review. **What do you like best about Cast AI?** 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 Cast AI?** I am concerned about the costs when scaling my systems. **What problems is Cast AI 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. ### 37. Cost-Effective, Easy Setup **Rating:** 4.5/5.0 stars **Reviewed by:** Sodyam B. | DevOps Engineer, Mid-Market (51-1000 emp.) **Validated Reviewer:** Validated through LinkedIn **Source: G2 invite on behalf of seller:** Invitation from G2 on behalf of a seller or affiliate. This reviewer was not provided any incentive by G2 for completing this review. **Reviewed Date:** April 13, 2026 **What do you like best about Cast AI?** I use CAST AI for cost optimization, cost monitoring, and checking anomalies. The main thing I appreciate about CAST AI is its visibility in a common dashboard for cost monitoring and CPU and memory usage per pod. I love the workload autoscaler because it provides the right sizing of pods. It learns from the usage pattern over the last seven days of data, which helps us save resources. The autoscaler automatically rightsizes the pods based on the resource and limits provided, eliminating the need for manual tasks. It also manages the Replica count, HPA, and VPA intelligently. The classic console provides much ease of use. Setting up CAST AI was very easy, and with the mentioned steps, a cluster can be onboarded in no time. **What do you dislike about Cast AI?** Sometimes the cluster has to be reconciled to enable rebalancing. While it connects efficiently to AWS, Azure, and GCP, the integration with Oracle needs to be added. **What problems is Cast AI solving and how is that benefiting you?** I use CAST AI for cost optimization and monitoring, providing visibility in a common dashboard. It saves costs via workload autoscaling by right-sizing pods based on usage patterns, which eliminates manual tasks like managing replicas, HPA, and VPA. ### 38. Revolutionises Kubernetes Cost Management **Rating:** 5.0/5.0 stars **Reviewed by:** Narasimman A. | Mid-Market (51-1000 emp.) **Validated Reviewer:** Validated through Google using a business email account **Source: Seller invite:** Invitation from a seller or affiliate. This reviewer was not provided any incentive by G2 for completing this review. **Reviewed Date:** March 17, 2026 **What do you like best about Cast AI?** I use CAST AI to optimize Kubernetes infrastructure cost and resource utilization across both production and non-production environments. CAST AI helps us automate node scaling, workload right-sizing, and instance type selection, which is a huge help. I also appreciate the dashboard view, which makes it easy to see all namespace workloads and identify usage patterns to reduce resources. CAST AI effectively solves challenges related to Kubernetes cost management, scalability, and operations across multiple cloud provider clusters. The cost management is fantastic, as CAST AI chooses the best instance types based on workloads and automatically provisions the right size of nodes, CPUs, and memory, which saves costs. The initial setup was quite good, and it helped us learn more about cost optimization. Overall, I would definitely recommend CAST AI. **What do you dislike about Cast AI?** I would say CAST AI can improve by automatically picking the pod usage and changing the resources without any downtime of production workloads. Basically, right sizing is shown in the dashboard but we have to do it manually. It would be better if it could solve this automatically to spin up new pods and reduce the workloads. **What problems is Cast AI solving and how is that benefiting you?** I use CAST AI to optimize Kubernetes infrastructure costs and resource utilization. It automates node scaling, workload right-sizing, and selects cost-effective instance types, helping us manage multiple cloud provider clusters efficiently. ### 39. Auto-Rightsizing That Optimizes Workloads Effortlessly **Rating:** 4.0/5.0 stars **Reviewed by:** Real M. | Cloud engineer, Mid-Market (51-1000 emp.) **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** This review contains authentic analysis and has been reviewed by our team **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** August 31, 2026 **What do you like best about Cast AI?** Its ability to automatically rightsize workloads based on real usage **What do you dislike about Cast AI?** some advanced features have a leaning curve **What problems is Cast AI solving and how is that benefiting you?** automating resource scaling so i only use what my workloads actually need ### 40. The 'Set-and-Forget' Engine for High-Performance Cluster Management **Rating:** 5.0/5.0 stars **Reviewed by:** Suresh S. | Senior Devops Engineer, Enterprise (> 1000 emp.) **Validated Reviewer:** Validated through a business email account **Source: Seller invite:** Invitation from a seller or affiliate. This reviewer was not provided any incentive by G2 for completing this review. **Reviewed Date:** March 12, 2026 **What do you like best about Cast AI?** What I value most is the granular, real-time visibility and the "app-aware" engine that scales resources based on actual workload DNA rather than just generic metrics. The seamless integration with our existing CI/CD pipelines meant we saw performance improvements and massive cost reductions within hours of deployment. It has effectively bridged the gap between our DevOps and FinOps goals through one unified, automated control plane **What do you dislike about Cast AI?** While the automation is powerful, the "black box" nature of the decision logic can initially make it difficult to trust the system with mission-critical production workloads without extensive testing of the guardrails. We also found that the coordination between the Workload Autoscaler and Node Autoscaler could be tighter, as they sometimes operate independently rather than planning for future node utilization in perfect tandem. Additionally, the "percentage of savings" pricing model can feel like a "savings tax" as you scale, making it harder to predict long-term tool costs compared to a flat-tier subscription **What problems is Cast AI solving and how is that benefiting you?** Cast AI solves the persistent "Kubernetes waste" problem by automating rightsizing, bin-packing, and spot instance orchestration that are traditionally too complex to manage manually at scale. For me, this has replaced hours of tedious YAML tuning and "firefighting" during traffic spikes with a reliable, autonomous engine that keeps our clusters lean and high-performing. The biggest benefit is the reclaimed time; I can finally focus on high-impact architectural work instead of constantly babysitting node groups and cloud bills. ### 41. Auto Scaling That Cuts Cloud Waste **Rating:** 4.5/5.0 stars **Reviewed by:** Lastborn . | cloud infrastructure engineer, Mid-Market (51-1000 emp.) **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** This review contains authentic analysis and has been reviewed by our team **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** August 31, 2026 **What do you like best about Cast AI?** The auto scaling features that help reduce wasted cloud resources **What do you dislike about Cast AI?** i would like more control over automated optimization decisions **What problems is Cast AI solving and how is that benefiting you?** improving kubernetes resource utilization and reducing wasted capacity ### 42. Cast AI Cut Our Kubernetes Cloud Spend by 50% with Seamless Autopilot Scaling **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Financial Services | Mid-Market (51-1000 emp.) This reviewer's identity has been verified by our review moderation team. They have asked not to show their name, job title, or picture. **Validated Reviewer:** Validated through a business email account **Source: Seller invite:** Invitation from a seller or affiliate. This reviewer was not provided any incentive by G2 for completing this review. **Reviewed Date:** May 19, 2026 **What do you like best about Cast AI?** Cast AI is an outstanding Kubernetes cost optimization platform that has genuinely transformed how we manage our cloud infrastructure. The automated cost optimization is incredibly effective, reducing our cloud spend by over 50% without any manual effort. The AI-driven right-sizing of workloads is spot on, and the autopilot feature handles scaling seamlessly. The UI is intuitive and clean, making it easy to navigate and understand resource usage at a glance. Integration with our existing cloud providers (AWS, GCP, Azure) was smooth and took only minutes. The real-time cost visibility and recommendations are actionable and easy to implement. The support team is world-class and always responsive. **What do you dislike about Cast AI?** Honestly, it is very hard to find anything to dislike about Cast AI. The product is so comprehensive that there is very little room for improvement. If I had to nitpick, I would say that the initial setup documentation could have a few more visual guides, but the support team more than compensates for this. Everything else — from onboarding to daily use — has been a pleasure. The platform keeps getting better with every update, and the team is clearly listening to user feedback and continuously improving the product. **What problems is Cast AI solving and how is that benefiting you?** Before Cast AI, we struggled with unpredictable cloud costs and over-provisioned Kubernetes clusters that were wasting significant resources. Cast AI solved this completely. It automatically right-sizes our nodes, eliminates wasted capacity, and has reduced our monthly cloud bill by more than 50%. We no longer need to manually tune resource requests and limits — Cast AI handles it all intelligently. The ROI has been remarkable: within the first month, we recouped the cost of the subscription many times over. Our engineering team now spends less time on infrastructure optimization and more time building features, which has accelerated our product development considerably. ### 43. Multiple Models at Once, But Newest Options Aren’t Always Included **Rating:** 3.5/5.0 stars **Reviewed by:** Verified User in Manufacturing | Small-Business (50 or fewer emp.) This reviewer's identity has been verified by our review moderation team. They have asked not to show their name, job title, or picture. **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** Validated through LinkedIn **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** August 25, 2026 **What do you like best about Cast AI?** Having multiple models available at once is really convenient. There’s no need to swap tabs or screens, which makes everything feel smoother and more efficient. So far, it compares favorably. Still working on full user engagement which will determine ultimate value gained. **What do you dislike about Cast AI?** The newest models aren’t necessarily included. **What problems is Cast AI solving and how is that benefiting you?** I like its simplicity and speed, and how quickly I can switch between multiple models. ### 44. Transforming Kubernetes into a Self-Optimizing System through Automated Node Orchestration **Rating:** 5.0/5.0 stars **Reviewed by:** Saravanan M. | DevOps Engineer, Enterprise (> 1000 emp.) **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** Validated through a business email account **Source: Seller invite:** Invitation from a seller or affiliate. This reviewer was not provided any incentive by G2 for completing this review. **Reviewed Date:** February 12, 2026 **What do you like best about Cast AI?** Cast AI automates node orchestration and right-sizing, using advanced bin-packing to ensure high cluster density and performance. It effectively manages spot instance lifecycles with automated fallback, significantly reducing compute overhead and manual infrastructure tuning **What do you dislike about Cast AI?** The initial configuration of IAM permissions and automation guardrails requires a careful setup phase before fully handing over infrastructure control **What problems is Cast AI solving and how is that benefiting you?** Cast AI eliminates over-provisioning and node fragmentation through aggressive, real-time bin-packing and automated rightsizing of CPU/memory requests. It automates the entire lifecycle of node provisioning and spot instance orchestration, including seamless fallbacks to on-demand instances during market interruptions. This transforms our Kubernetes clusters into a self-optimizing system, significantly reducing our cloud spend while freeing the team to focus on delivery pipelines rather than manual scaling policies ### 45. Automates Kubernetes and Cuts Costs Effectively **Rating:** 4.5/5.0 stars **Reviewed by:** Pramod P. | Enterprise (> 1000 emp.) **Validated Reviewer:** Validated through a business email account **Source: Seller invite:** Invitation from a seller or affiliate. This reviewer was not provided any incentive by G2 for completing this review. **Reviewed Date:** February 26, 2026 **What do you like best about Cast AI?** I use CAST AI to automatically optimize the Kubernetes workload, which helps cut cloud costs without needing manual tuning. It eliminates the manual effort of managing autoscaling, node provisioning, and performance monitoring, allowing me to focus on building features instead of babysitting infrastructure. I particularly appreciate the completely automated Kubernetes optimization that actually works. I also experience massive cost savings with real-time analytics, and the real-time cost-saving feature lets me see where my money goes. The automated cost-saving means I don't have to manually tune the cluster. **What do you dislike about Cast AI?** I think the documentation and support guidance could be more consistent, particularly in areas like advanced autoscaling configurations. Clear, unified guidance with scenario-based examples and transparent troubleshooting notes would greatly enhance the onboarding experience. **What problems is Cast AI solving and how is that benefiting you?** I use CAST AI to automatically optimize Kubernetes workloads, cutting cloud costs without manual tuning. It eliminates the manual effort of managing autoscaling, node provisioning, and performance monitoring, allowing me to focus on features instead of infrastructure. ### 46. Revolutionized our HPC Workloads and Cost Optimization **Rating:** 4.5/5.0 stars **Reviewed by:** Louis B. | Enterprise (> 1000 emp.) **Validated Reviewer:** Validated through Google using a business email account **Source: Seller invite:** Invitation from a seller or affiliate. This reviewer was not provided any incentive by G2 for completing this review. **Reviewed Date:** February 24, 2026 **What do you like best about Cast AI?** I use CAST AI extensively to optimize the scaling of our HPC workloads on AWS EKS. The tech is great, providing both effective features and scalability. Cost optimization for Spot instances on AWS, a pretty tough problem, is solved amazingly well. There is no equivalent in AWS native feature or open-source components that come anywhere close to CAST AI's technology. The user and developer experience is smooth and fits well with the intended audience. Collaborating with the engineering team is fast and efficient, as they deliver fixes and features in record time. I also like the simple initial setup as onboarding through the helm chart requires limited involvement and the readonly mode allow to discover the products and insights without risks. **What do you dislike about Cast AI?** N/A **What problems is Cast AI solving and how is that benefiting you?** I use CAST AI to optimize the scaling of HPC workloads on AWS EKS, solving tough Spot instance cost problems effectively and allowing simultaneous multi-cluster scaling, unlike AWS native autoscaler and Karpenter. ### 47. CAST AI Full Autopilot That Actually Executes Changes in Real Time **Rating:** 5.0/5.0 stars **Reviewed by:** Sai Vishaal V. | Devops Engineer, Mid-Market (51-1000 emp.) **Validated Reviewer:** Validated through a business email account **Source: Seller invite:** Invitation from a seller or affiliate. This reviewer was not provided any incentive by G2 for completing this review. **Reviewed Date:** February 23, 2026 **What do you like best about Cast AI?** What I like most is the Full Autopilot mode. Unlike other tools that just give you a list of recommendations that you have to implement manually, CAST AI actually executes the changes. It handles rightsizing, autoscaling, and spot instance management in real-time without our team having to intervene daily. **What do you dislike about Cast AI?** Honestly, the biggest hurdle was just the initial 'trust fall' with Autopilot. It’s a bit stressful giving an external tool the keys to your production environment to spin up or terminate nodes on its own. We had to spend a good chunk of time in a sandbox environment and go through several security reviews before the team felt comfortable letting it run on full-auto. Once that trust was built, it was fine, but that first week definitely had us on edge. **What problems is Cast AI solving and how is that benefiting you?** Our biggest issue was 'Cloud Waste.' Our engineers used to over-provision pods 'just in case,' and we were paying for massive amounts of idle CPU and RAM. CAST AI solved this by automating the bin-packing process. It has completely removed the manual guesswork from cluster management. We no longer have to spend hours every week manually adjusting instance types or scaling policies; the tool just picks the most cost-effective compute for the workload in real-time. ### 48. Autonomous K8s Optimization with Smooth Onboarding **Rating:** 4.5/5.0 stars **Reviewed by:** Rishabh A. **Validated Reviewer:** This review contains authentic analysis and has been reviewed by our team **Source: Seller invite:** Invitation from a seller or affiliate. This reviewer was not provided any incentive by G2 for completing this review. **Reviewed Date:** February 14, 2026 **What do you like best about Cast AI?** 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 Cast AI?** 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 Cast AI 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. ### 49. CAST AI Makes Kubernetes Cost Optimization Truly Automated and Reliable **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Automotive | Mid-Market (51-1000 emp.) This reviewer's identity has been verified by our review moderation team. They have asked not to show their name, job title, or picture. **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** Validated through Google One Tap using a business email account **Source: Seller invite:** Invitation from a seller or affiliate. This reviewer was not provided any incentive by G2 for completing this review. **Reviewed Date:** February 13, 2026 **What do you like best about Cast AI?** 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 Cast AI?** 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 Cast AI 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. ### 50. Effortless Cost Efficiency and Monitoring in Multi-Cloud Management **Rating:** 5.0/5.0 stars **Reviewed by:** Yogendra K. | Enterprise (> 1000 emp.) **Validated Reviewer:** Validated through Google using a business email account **Source: Seller invite:** Invitation from a seller or affiliate. This reviewer was not provided any incentive by G2 for completing this review. **Reviewed Date:** February 25, 2026 **What do you like best about Cast AI?** I use CAST AI to manage our infrastructure, especially various EKS and GKE clusters for right sizing. It helps me select cost-efficient instances automatically based on use cases, which avoids over and under-provisioning. I love that it monitors resource utilization and cost across multi-cloud platforms and takes action automatically without downtime. The ability to replace over-provisioned instances with the most cost-efficient ones in real-time, particularly across multiple cloud environments like AWS and GCP, is really beneficial for me. I also like the finance-friendly dashboard and continuous insights that highlight cost-saving opportunities. **What do you dislike about Cast AI?** For a beginner, it seems a little bit harder as a learning curve. **What problems is Cast AI solving and how is that benefiting you?** CAST AI helps manage our EKS, GKE clusters by right-sizing and automatically selecting cost-efficient instances, preventing over or under provisioning. It monitors resource use across multicloud platforms, ensuring uptime without downtime and providing continuous cost-saving insights via a finance-friendly dashboard. ## Cast AI Discussions - [What is CAST AI used for?](https://www.g2.com/discussions/what-is-cast-ai-used-for) - 1 comment, 1 upvote - [View Cast AI pricing details and edition comparison](https://www.g2.com/products/cast-ai/reviews?source=search§ion=pricing&secure%5Bexpires_at%5D=2026-09-07+22%3A10%3A51+-0500&secure%5Bsession_id%5D=ee5cc5a2-4a0a-426f-a244-e3aafecb940b&secure%5Btoken%5D=4850c2eee23766ebd8f75493ef9701ff0faa09a0652c15947859cc455dd3792a&format=llm_user) ## Cast AI Features **Additional Functionality** - Tagging - Natural Language Processing - Data Extraction - Multi-Language - Predictive Analytics - Drag & Drop - Speech Recognition - Reporting/Analytics - Data Storage Management - Virtual Personal Assistant (VPA) - AI Copilot - Customer Segmentation - Collaboration Tools - Data Import/Export - Generative AI - For eCommerce - Role-Based Permissions - Customizable Branding - Search/Filter - Monitoring - Document Management - API - Data Visualization - Trend Analysis - Machine Learning - Access Controls/Permissions - Alerts/Escalation - Performance Metrics - Real-Time Data - Third-Party Integrations - Mobile App - Multiple Data Sources - For Sales Teams/Organizations - Sentiment Analysis - Activity Dashboard - Chatbot - Workflow Automation **Additional Functionality** - Specialized or Emerging Capabilities - Distinct AI Functionality - Niche Application **Operations** - Scheduling - Automation - Multi-Cloud Management - Usage Monitoring **Functionality** - Cloud Consolidation - Cloud Orchestration - Cloud Optimization **Automated resource scaling** - Automatic resource discovery - Smart scaling **Cost Optimization** - Spend Forecasting and Optimization - Recommendations - Spend Tracking **Management** - Cloud Cost Analytics - Cloud Security - Security Management - Cloud Backup and Recovery - Capacity Analytics - Compliance Management - Configuration Management - User Management - Workflow Management - Application Management - Cost Management - Data Storage Management - Change Management - Disaster Recovery - Multi-Cloud Management - Service Level Agreement (SLA) Management - Audit Management - Patch Management - Inventory Management **Scaling strategies** - Pre-defined optimization strategies - Predictive scaling **Administration** - Reporting - Dashboards and Visualizations - Compliance **Visualization** - Unified scaling - Dashboard **Agentic AI - Cloud Management Platforms** - Autonomous Task Execution - Cross-system Integration - Decision Making - Data Integration - Third-Party Integrations **Additional Functionality** - Performance Analysis - Service Catalog - Activity Tracking - Anomaly Detection - Real-Time Monitoring - Data Visualization - Alerts/Notifications - Multitenancy - Generative AI - Self Service Portal - Network Monitoring - API - Reporting & Statistics - Data Import/Export - AI Copilot - Search/Filter - Access Controls/Permissions - Database Support - Cloud Encryption - Data Migration - Event Logs - Monitoring **Agentic AI - Cloud Cost Management** - Autonomous Task Execution - Proactive Assistance - Decision Making ## Top Cast AI Alternatives - [IBM Turbonomic](https://www.g2.com/products/ibm-turbonomic/reviews) - 4.4/5.0 (287 reviews) - [Flexera One](https://www.g2.com/products/flexera-one/reviews) - 4.4/5.0 (173 reviews) - [Amazon CloudWatch](https://www.g2.com/products/amazon-cloudwatch/reviews) - 4.3/5.0 (364 reviews)