--- title: Cast AI Reviews meta_title: 'Cast AI Reviews 2026: Details, Pricing, & Features | G2' meta_description: Filter 248 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: 248 scale: '5' date_modified: '2026-09-28' 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:** 248 **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 Kubernetes and GPU infrastructure. It scales, rightsizes, and migrates workloads automatically, and sources GPU capacity across clouds and regions through OMNI. Every optimization is verified: lower costs, stronger performance and reliability, and less manual toil. Cast AI reached unicorn status in January 2026 and is trusted by BMW, Cisco, FICO, HuggingFace, and Swisscom. ## 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. Excellent Live Migration for Seamless Kubernetes Workload Optimization **Rating:** 3.5/5.0 stars **Reviewed by:** Narendar S. | IT Infrastructure 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:** 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:** September 24, 2026 **What do you like best about Cast AI?** CAST AI provides a powerful approach to Kubernetes infrastructure optimization and workload management. One of the features I appreciate most is its dynamic compute provisioning capability, which helps automatically adjust Kubernetes compute capacity according to workload requirements. Another major strength is its workload autoscaling capability, particularly its integration with HPA and VPA. Automated workload rightsizing, infrastructure optimization, workload placement, and resource-utilization capabilities. These features help platform teams improve cluster efficiency and manage Kubernetes infrastructure more effectively. Overall, CAST AI is a valuable platform for organizations looking to automate Kubernetes infrastructure operations, optimize compute resources, and improve workload resource management **What do you dislike about Cast AI?** While Cast AI's workload rebalancing capability is useful for optimizing cluster utilization, we have encountered a few issues during rebalancing activities. The support team was responsive and helped resolve the problems but product felt as costly as it charges per core **What problems is Cast AI solving and how is that benefiting you?** CAST AI helps us address the challenges of managing Kubernetes compute capacity, node utilization, and workload resource allocation. Its dynamic compute provisioning reduces manual infrastructure-management effort, most useful feature what helped us is live migration. ### 2. CastAI Massively Reduced Our Cluster Costs with Intuitive, Powerful Controls **Rating:** 4.5/5.0 stars **Reviewed by:** Chirag B. | Machine Learning 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:** September 27, 2026 **What do you like best about Cast AI?** CastAI is an amazing tool which we use also in production which has reduced our cluster costs massively. The UI is very intuitive helps us realize potential gains as well, and of course, the amount of hooks and controls given by CastAI are great to just tune workloads as per our needs and risk appetite. It also has a great bot that helps us analyze the events and decisions taken. Overall, it has been a great experience using it. **What do you dislike about Cast AI?** UI is a bit buggy sometimes, and also some of the settings configured via Terraform do not reflect in the UI. Job-like workloads are a bit tricky to be managed by CastAI. **What problems is Cast AI solving and how is that benefiting you?** For us, it majorly reduces our idle costs. But also we use it for job-like workloads and resize them for optimal usage of node capacity. So far, the performance has been great! ### 3. Cast AI Delivers Major Cloud Cost Savings with Smooth Entra ID Integration **Rating:** 4.5/5.0 stars **Reviewed by:** Moulick A. | Senior 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 LinkedIn **Source: Seller invite:** Invitation from a seller or affiliate. This reviewer was not provided any incentive by G2 for completing this review. **Reviewed Date:** September 24, 2026 **What do you like best about Cast AI?** Pretty useful, and I don’t have to chase every team to reduce CPU and memory usage for their services. The UX is nice, and it integrates well with Entra ID. Cast AI can also improve the performance of your services if it detects they’re underscaled. The application, operator, and pods running in the cluster are also very efficient at what they do. It is directly saving us money, so the cost of Cast AI is returned by the cloud cost savings many times over. The support offered by Cast AI is also pretty quick and helpful. **What do you dislike about Cast AI?** Cast AI is an application that adjusts CPU and other application resources, and it can sometimes downscale services too aggressively. If your workloads are complicated, you’ll likely need to spend some time fine-tuning the scaling policies to get the results you want. **What problems is Cast AI solving and how is that benefiting you?** Saving money directly. ### 4. 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. ### 5. CastAI Scales Seamlessly, Cuts Costs, and Delivers Excellent Support **Rating:** 4.0/5.0 stars **Reviewed by:** Ruevon B. | DevOps Engineer, Insurance, 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 **Source: Seller invite:** Invitation from a seller or affiliate. This reviewer was not provided any incentive by G2 for completing this review. **Reviewed Date:** September 25, 2026 **What do you like best about Cast AI?** It does a fantastic job scaling our environments while keeping costs down. The UI is easy to learn, and incorporating CastAI into our environments was not too difficult. The dedicated Teams channel we use to message support has been excellent, and it consistently helps us get fast, reliable responses. **What do you dislike about Cast AI?** I don’t really have anything to dislike about it. It effectively replaces 3–4 separate products that would otherwise be freeware and therefore subject to changes we might not want, or it would force us to rely on a much more expensive product instead. **What problems is Cast AI solving and how is that benefiting you?** It replaces the need for us to manually define what our applications require in terms of memory and CPU. It also helps with node management and ensures we have a fresh set of apps in the lower environments every day. ### 6. 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. ### 7. 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. ### 8. 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 , Logistics and Supply Chain, 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 ### 9. 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. ### 10. 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. ### 11. Promising Optimization Platform, Excellent Team Support **Rating:** 4.0/5.0 stars **Reviewed by:** Sairam D. | Small-Business (50 or fewer 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:** September 26, 2026 **What do you like best about Cast AI?** I find CAST AI really impressive for its ability to help with infra optimization, resource utilization, CPU and GPU efficiency, and cost visibility. The team at CAST AI is fantastic; they're always available for discussions and to provide more solutions. I appreciate their openness to understand what we need and how they can help. Kimchi, the multi-model platform from CAST AI, assists in day-to-day activities smoothly. The initial setup was very easy, which was a big plus for me as well. **What do you dislike about Cast AI?** I think it would be beneficial if there were more models available with Kimchi. In my organization, different teams work with various tasks, so instead of having the same model for everyone, it would be helpful to have specific models tailored for different teams, making it easier for them to work on their respective projects. **What problems is Cast AI solving and how is that benefiting you?** CAST AI helps in infra optimization, CPU utilization, managing AI workloads, and provides a new perspective on resource costs. ### 12. A Simple UI That Makes Right-Sizing Easier for the Team **Rating:** 5.0/5.0 stars **Reviewed by:** Shubham G. | Java Developer, Information Technology and Services, Small-Business (50 or fewer emp.) **Validated Reviewer:** Validated through LinkedIn **Source: Seller invite:** Invitation from a seller or affiliate. This reviewer was not provided any incentive by G2 for completing this review. **Reviewed Date:** September 24, 2026 **What do you like best about Cast AI?** What I like most about Cast AI is how easy it is to get a quick understanding of what’s actually happening with our resources. I don’t have to dig through a lot of dashboards or metrics to figure out where we’re overprovisioned. The UI clearly highlights the areas where we can right-size and gives practical suggestions on what to change. It’s been especially useful when I need to explain resource optimization to other team members who don’t work with Kubernetes every day. **What do you dislike about Cast AI?** One thing I’ve noticed is that some of the optimization recommendations need a bit of context before applying them. For example, I’ve seen recommendations for background jobs that only run for 1–2 minutes per execution but run several times a day on spot instances. In one case, the recommendation suggested reducing the resources, but the actual savings would have been only a few dollars per month, while the potential impact on job execution time wasn’t worth the trade-off. I usually look at the workload pattern, frequency, and actual cost impact before applying these recommendations rather than following them blindly. **What problems is Cast AI solving and how is that benefiting you?** Cast AI is helping us address one of the biggest challenges in running cloud infrastructure: keeping costs under control. Over the past few months, we’ve used its recommendations to identify areas where we were overprovisioned and then validated those recommendations against our actual workload patterns before making changes. Across the workloads we’ve optimized so far, we’ve seen our overall cloud spend come down by more than 30%. The important part for us has been that we didn’t apply every recommendation blindly—we reviewed the potential savings and workload impact first, then implemented the changes that made sense. ### 13. CAST AI Slashed Our Kubernetes Cloud Spend with Powerful Automation **Rating:** 5.0/5.0 stars **Reviewed by:** Rajiv d. | Manager – Information Security, Enterprise (> 1000 emp.) **Validated Reviewer:** Validated through LinkedIn **Source: Seller invite:** Invitation from a seller or affiliate. This reviewer was not provided any incentive by G2 for completing this review. **Reviewed Date:** September 24, 2026 **What do you like best about Cast AI?** What I like best about CAST AI is its powerful automated cloud cost optimization. It completely removes the manual guesswork from managing Kubernetes clusters by dynamically scaling and rightsizing resources in real-time. Features like automated spot instance management and instant rebalancing have significantly dropped our cloud spend without compromising performance or stability. **What do you dislike about Cast AI?** While the automation is excellent, navigating the advanced configuration options can initially feel overwhelming. The initial setup requires a solid understanding of Kubernetes, and it takes some time to fully grasp how the different autoscaling policies interact with unique workload architectures. Additional contextual tooltips inside the dashboard would be highly beneficial. **What problems is Cast AI solving and how is that benefiting you?** Our workloads have highly fluctuating traffic patterns, making it incredibly difficult to scale efficiently. We often over-provisioned resources to ensure high availability, which led to terrible cost ROI, or under-provisioned, which risked application performance. The Solution & Benefit: CAST AI solved this by dynamically matching our compute resources to our exact application demands in real-time. Their clean, intuitive user interface gives us crystal-clear visibility into our cluster metrics. Now, we maintain flawless application performance during traffic spikes while confidently keeping our baseline costs as lean as possible. ### 14. 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. **AI Translated:** This review has been translated from English using AI. **Reviewed Date:** April 13, 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 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. ### 15. Easy UI, Smooth Cast AI Integration, and Outstanding Support **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User in Apparel & Fashion | Enterprise (> 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 added to their profile **Source: Seller invite:** Invitation from a seller or affiliate. This reviewer was not provided any incentive by G2 for completing this review. **Reviewed Date:** September 25, 2026 **What do you like best about Cast AI?** It does exactly what they promise. The UI is easy to understand, and integrating with Cast AI has been a breeze. We’re not only saving money on compute, but also saving developers time, since they no longer have to worry as much about how many resources to allocate to their application, Cast AI takes care of it. The support and onboarding team have been phenomenal as well; they’ve helped us every step of the way. **What do you dislike about Cast AI?** We had some issues with PersistentVolume, as Cast AI would create node types which didn't support our PersistentVolume. I think Cast AI should detect this automatically and don't create nodes which doesn't support the PersistentVolume we are using. **What problems is Cast AI solving and how is that benefiting you?** We no longer have to analyse the workloads and find the correct node type or adjust our workflows to have the correct resources set. ### 16. 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. ### 17. 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. ### 18. 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. ### 19. 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. ### 20. 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 ### 21. 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. ### 22. 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. ### 23. Great for Database Optimization, Smooth Operation Post-Setup **Rating:** 4.0/5.0 stars **Reviewed by:** sreenivas p. | MySql Database Administrator, Enterprise (> 1000 emp.) **Validated Reviewer:** Validated through LinkedIn **Source: Seller invite:** Invitation from a seller or affiliate. This reviewer was not provided any incentive by G2 for completing this review. **Reviewed Date:** September 25, 2026 **What do you like best about Cast AI?** I mostly liked the database-related cluster nodes and their monitoring in CAST AI. It's been helpful for cost optimization from a database perspective and finding unused filesystem storage and metrics. The platform works fine, though there was some difficulty with the initial setup, but it's smooth now. We're planning to set up a Patroni cluster related to PostgreSQL, and it works well with K8 clusters. **What do you dislike about Cast AI?** I found that setting up CAST AI initially was a bit challenging, though it is working smoothly now. Also, there are some aspects related to cluster maintenance models that might need improvement. **What problems is Cast AI solving and how is that benefiting you?** CAST AI helps with cost optimization by identifying unused file storage and analyzing database cluster nodes and CPU metrics, enhancing my platform's efficiency. ### 24. Impressive Kubernetes Automation with Smart Autoscaling and Spot Instance Management **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User in Computer Software | Enterprise (> 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 LinkedIn **Source: Seller invite:** Invitation from a seller or affiliate. This reviewer was not provided any incentive by G2 for completing this review. **Reviewed Date:** September 24, 2026 **What do you like best about Cast AI?** The degree of automation in Kubernetes infrastructure management is incredibly impressive. The real-time autoscaler dynamically shifts and provisions the exact instance sizes needed based on actual pod demand. Additionally, the Spot instance management is a lifesaver—it accurately predicts interruptions and smoothly transitions workloads to On-Demand instances without causing application downtime **What do you dislike about Cast AI?** The pricing and billing models can be a bit complex to parse out initially, and the pricing scales up quickly as your cluster environments grow larger **What problems is Cast AI solving and how is that benefiting you?** It completely removes the manual burden of tweaking node group configurations and reviewing rightsizing recommendations. We've turned Kubernetes cost optimization from a tedious, ongoing manual chore into an autopilot process, resulting in immediate cloud bill reductions of around 30% ### 25. Intuitive Dashboards and Clear Cost Insights for Kubernetes Optimization **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User in Computer Networking | 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 LinkedIn **Source: Seller invite:** Invitation from a seller or affiliate. This reviewer was not provided any incentive by G2 for completing this review. **Reviewed Date:** September 28, 2026 **What do you like best about Cast AI?** The interface is intuitive and easy to navigate, with clear dashboards that provide visibility into cluster health, resource consumption, and optimization opportunities. Cost and usage insights are presented in a way that is accessible to both platform engineers and management stakeholders. Some advanced settings require familiarity with Kubernetes concepts, but the overall user experience is well designed. **What do you dislike about Cast AI?** While CAST AI delivers strong cost optimization and automation capabilities, some advanced features have a learning curve and require a good understanding of Kubernetes concepts to be used effectively. Greater visibility into how certain optimization recommendations are calculated would improve transparency and user confidence. **What problems is Cast AI solving and how is that benefiting you?** CAST AI is solving two major challenges for us: Kubernetes cost optimization and operational complexity and improves infrastructure efficiency without negatively impacting workload stability. Regarding Integrations, CAST AI connected smoothly with our Kubernetes and cloud environments, allowing us to begin identifying optimization opportunities quickly without significant changes to existing processes or tooling. The Support / Onboarding experience was very positive. The team provided responsive guidance throughout deployment and adoption, helping accelerate time-to-value and ensuring best practices were implemented from the beginning. ### 26. Effortlessly Optimizes Kubernetes Costs **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User | Enterprise (> 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: Organic:** Organic review. This review was written entirely without invitation or incentive from G2, a seller, or an affiliate. **Reviewed Date:** September 25, 2026 **What do you like best about Cast AI?** I really like how CAST AI "just runs" once we got the settings right, with the autoscaler and spot handling doing their thing without me needing to babysit it. It's one of the best things for an infrastructure tool. I find the bin packing feature especially valuable because it consolidates workloads onto fewer nodes and chooses instance types I wouldn't have thought to try. The handling of spot instances and the cost breakdown by namespace are also incredibly helpful, even though the latter sounds small. Additionally, the initial setup was quite easy as it involved installing an agent and connecting in read-only mode. **What do you dislike about Cast AI?** CAST AI has a bit of a learning curve. Node templates and constraints are really powerful, but you need to really understand your own workloads before turning everything on. The UI can be kind of busy too. They keep adding modules, and sometimes I end up hunting for a setting or asking support what it actually does. **What problems is Cast AI solving and how is that benefiting you?** I use CAST AI for Kubernetes cost optimization, managing overprovisioned clusters. It minimizes overspending by dynamically fitting workloads to resources, reducing manual adjustments, thanks to features like bin packing and spot instance handling. ### 27. Efficient Cost Management but Setup Needs Improvement **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User | Enterprise (> 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 LinkedIn **Source: Seller invite:** Invitation from a seller or affiliate. This reviewer was not provided any incentive by G2 for completing this review. **Reviewed Date:** September 25, 2026 **What do you like best about Cast AI?** I like CAST AI's solution itself. It seems to be working pretty well based on what I've seen so far. The UI for managing the clusters is pretty easy to understand, making it clear how CAST AI works and how it's contributing. I also like the overview of analyzing the cost in the UI. CAST AI reduces our effort in maintaining the infrastructure, especially in terms of FinOps, without having to delegate that to the engineers. We don't need to change the applications or notify the developers, while still saving money and ensuring the workflows run as expected. CAST AI helps us make sure we are not overexpanding resources that we are not using, which is really valuable. **What do you dislike about Cast AI?** The outscaling policies in CAST AI aren't as user-friendly as I'd like. It's a bit tricky and sometimes when we test these policies, we end up causing incidents because the scaling for applications can be too aggressive, leading to issues. The policy could definitely be improved. Additionally, I find that the recommendations aren't too helpful. I wish there were more insights into how we could better utilize the features CAST AI provides without having to do everything manually. Setting things up within the clusters and configuring everything is not as easy as it should be, which makes the adoption process harder. **What problems is Cast AI solving and how is that benefiting you?** CAST AI helped us rightsize resources, reducing our AWS cloud costs and ensuring we only use necessary resources for workloads. It simplifies infrastructure maintenance, enabling us to scale effectively without altering applications or notifying developers, thus saving money and ensuring smooth workflow operations. ### 28. 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 ### 29. 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. ### 30. 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 ### 31. 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 ### 32. 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. ### 33. CAST AI: Stunning Savings and Live Visualization **Rating:** 4.5/5.0 stars **Reviewed by:** Alain T. | Product Owner | Ingénieur DevOps, Small-Business (50 or fewer emp.) **Validated Reviewer:** Validated through LinkedIn **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 French using AI. **Reviewed Date:** September 24, 2026 **What do you like best about Cast AI?** I love the interface that allows me to visualize our Kubernetes clusters, and it's adaptable to all our Kubernetes clusters. It's particularly handy to have the live visualization of the savings we make. Additionally, CAST AI can be coupled with Carpenter, which is really useful. **What do you dislike about Cast AI?** I find that CAST AI could have more compatibility with other services, like OVH Cloud. This would make integration with different environments smoother and more efficient. **What problems is Cast AI solving and how is that benefiting you?** CAST AI solves the problem of saving on our Kubernetes clusters and offers an interface to visualize our savings in real-time. ### 34. 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 ### 35. 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. ### 36. 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 ### 37. 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. ### 38. 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. ### 39. 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 ### 40. 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 ### 41. 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 ### 42. 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. ### 43. Best-in-Class Search Results and Beginner-Friendly Experience **Rating:** 4.5/5.0 stars **Reviewed by:** Rohan B. | Sr Data Operations Analyst, 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:** September 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?** This provides the best results based on search query and is useful for beginners as well. **What do you dislike about Cast AI?** It takes some time to provide results, and the user interface could be improved. **What problems is Cast AI solving and how is that benefiting you?** I attended a webinar on Cast AI, and the numbers are eye-opening. For example, Wio Bank saved a staggering 70% on its cloud spend by optimizing its Kubernetes clusters. This isn’t about guesswork—it’s about smart automation. Cast AI intelligently right-sizes nodes, automates pod placement, and leverages a mix of Spot and Reserved Instances to drive down costs. ### 44. 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. ### 45. 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. ### 46. Automatic Resource Request and Limit Management Made Easy **Rating:** 5.0/5.0 stars **Reviewed by:** Fernando S. | DevOps Engineer, 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 added to their profile **Source: Seller invite:** Invitation from a seller or affiliate. This reviewer was not provided any incentive by G2 for completing this review. **Reviewed Date:** September 24, 2026 **What do you like best about Cast AI?** I like that I can automatically manage the requests and limits for my resources. **What do you dislike about Cast AI?** At the moment, I haven’t found anything that I would consider a negative point. **What problems is Cast AI solving and how is that benefiting you?** The main problem is the possibility to reduce the costs of my EKS nodes. ### 47. 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. ### 48. 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. ### 49. 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. ### 50. 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. ## Cast AI Discussions - [What is CAST AI used for?](https://www.g2.com/discussions/what-is-cast-ai-used-for) - 2 comments, 2 upvotes - [View Cast AI pricing details and edition comparison](https://www.g2.com/products/cast-ai/reviews?qs=pros-and-cons§ion=pricing&secure%5Bexpires_at%5D=2026-09-28+11%3A29%3A44+-0500&secure%5Bsession_id%5D=aa82c693-8862-46d2-9e49-18be9d8123ec&secure%5Btoken%5D=fc2f3df957f4fd7aa716240f4719b76829cda494b8912a47b165a8e8aa1f9024&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) - 4.3/5.0 (175 reviews) - [Amazon CloudWatch](https://www.g2.com/products/amazon-cloudwatch/reviews) - 4.3/5.0 (364 reviews)