# Cast AI Reviews **Vendor:** Cast AI **Category:** [Cloud Cost Management Tools](https://www.g2.com/categories/cloud-cost-management) **Average Rating:** 4.6/5.0 **Total Reviews:** 189 ## About Cast AI Cast AI is an automation platform for operating cloud-native and AI infrastructure at scale. It keeps applications fast and stable by continuously optimizing production systems and eliminating manual operations as environments scale. ## Cast AI Pros & Cons **What users like:** - Users value the **significant cost savings** offered by CAST AI, enjoying effortless optimization of Kubernetes environments. (43 reviews) - Users find CAST AI's **ease of use** impressive, simplifying management and enhancing visibility across workloads effectively. (43 reviews) - Users value CAST AI for its **effective cost management** , providing insights and features that optimize cluster expenses seamlessly. (42 reviews) - Users value CAST AI for its **cost reduction recommendations** and reliable automation for optimizing Kubernetes clusters. (39 reviews) - Users appreciate the **cost reduction capabilities** of CAST AI, valuing its automation and intelligent scaling for savings. (38 reviews) - Users value the **cost-saving capabilities** of CAST AI, achieving significant reductions in cloud expenses without losing performance. (38 reviews) - Automation (37 reviews) - Auto Scaling (37 reviews) - Easy Setup (35 reviews) - Users commend CAST AI for its **cost-effective solutions** , achieving up to 60% savings on cloud bills without sacrificing performance. (32 reviews) **What users dislike:** - Users find CAST AI **cost-prohibitive** compared to open source alternatives, raising concerns about its pricing structure. (12 reviews) - Users report **scaling issues** with Cast AI, experiencing improper resource allocations that hinder workload performance and control. (12 reviews) - Users express concerns about **pricing issues** , noting a lack of transparency and complexity in cost understanding. (10 reviews) - Users find the **UI navigation slow** and suggest improvements for better accessibility and visibility. (10 reviews) - Users find a **steep learning curve** with Cast AI, especially in understanding billing and monitoring performance effectively. (9 reviews) - Poor Documentation (9 reviews) - Difficulty in Usage (8 reviews) - Software Bugs (8 reviews) - Complexity (7 reviews) - Inadequate Monitoring (7 reviews) ## Cast AI Reviews ### 1. 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.) **Reviewed Date:** March 10, 2026 **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. ### 2. 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.) **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. ### 3. 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.) **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. ### 4. 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.) **Reviewed Date:** February 11, 2026 **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. ### 5. Centralized Kubernetes metrics and intuitive UI to optimize resources **Rating:** 4.0/5.0 stars **Reviewed by:** Fernando C. | Devops / Cloudops, Enterprise (> 1000 emp.) **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. ### 6. 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.) **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. ### 7. 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.) **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. ### 8. 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.) **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. ### 9. Great tool for K8 cost savings and cluster optimization **Rating:** 4.5/5.0 stars **Reviewed by:** Rushil S. | Lead Platform architect, Small-Business (50 or fewer emp.) **Reviewed Date:** February 11, 2026 **What do you like best about Cast AI?** It helps us optimize our K8 clusters and reduce costs. The UI is great and clearly shows how much we’ve saved so far, as well as what can still be improved within our cluster. The workload optimizer is also a really useful feature. **What do you dislike about Cast AI?** It’s hard to find logs for certain things, and it’s also hard to understand why something isn’t working when an issue comes up. For example, recently my scheduled rebalancing wasn’t working correctly, and even the support team couldn’t figure out why at first. After a lot of digging, we found it was because one machine was stuck in a weird state after a previous rebalancing. It wasn’t easy to track down what caused this, and it seemed like support wasn’t able to identify the issue right away either. **What problems is Cast AI solving and how is that benefiting you?** It helped us scale our cluster while also reducing costs by almost 50%. ### 10. Cost-Effective, Easy Setup **Rating:** 4.5/5.0 stars **Reviewed by:** Sodyam B. | DevOps Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** April 13, 2026 **What do you like best about Cast AI?** I use CAST AI for cost optimization, cost monitoring, and checking anomalies. The main thing I appreciate about CAST AI is its visibility in a common dashboard for cost monitoring and CPU and memory usage per pod. I love the workload autoscaler because it provides the right sizing of pods. It learns from the usage pattern over the last seven days of data, which helps us save resources. The autoscaler automatically rightsizes the pods based on the resource and limits provided, eliminating the need for manual tasks. It also manages the Replica count, HPA, and VPA intelligently. The classic console provides much ease of use. Setting up CAST AI was very easy, and with the mentioned steps, a cluster can be onboarded in no time. **What do you dislike about Cast AI?** Sometimes the cluster has to be reconciled to enable rebalancing. While it connects efficiently to AWS, Azure, and GCP, the integration with Oracle needs to be added. **What problems is Cast AI solving and how is that benefiting you?** I use CAST AI for cost optimization and monitoring, providing visibility in a common dashboard. It saves costs via workload autoscaling by right-sizing pods based on usage patterns, which eliminates manual tasks like managing replicas, HPA, and VPA. ### 11. Revolutionises Kubernetes Cost Management **Rating:** 5.0/5.0 stars **Reviewed by:** Narasimman A. | Mid-Market (51-1000 emp.) **Reviewed Date:** March 17, 2026 **What do you like best about Cast AI?** I use CAST AI to optimize Kubernetes infrastructure cost and resource utilization across both production and non-production environments. CAST AI helps us automate node scaling, workload right-sizing, and instance type selection, which is a huge help. I also appreciate the dashboard view, which makes it easy to see all namespace workloads and identify usage patterns to reduce resources. CAST AI effectively solves challenges related to Kubernetes cost management, scalability, and operations across multiple cloud provider clusters. The cost management is fantastic, as CAST AI chooses the best instance types based on workloads and automatically provisions the right size of nodes, CPUs, and memory, which saves costs. The initial setup was quite good, and it helped us learn more about cost optimization. Overall, I would definitely recommend CAST AI. **What do you dislike about Cast AI?** I would say CAST AI can improve by automatically picking the pod usage and changing the resources without any downtime of production workloads. Basically, right sizing is shown in the dashboard but we have to do it manually. It would be better if it could solve this automatically to spin up new pods and reduce the workloads. **What problems is Cast AI solving and how is that benefiting you?** I use CAST AI to optimize Kubernetes infrastructure costs and resource utilization. It automates node scaling, workload right-sizing, and selects cost-effective instance types, helping us manage multiple cloud provider clusters efficiently. ### 12. The 'Set-and-Forget' Engine for High-Performance Cluster Management **Rating:** 5.0/5.0 stars **Reviewed by:** Suresh S. | Senior Devops Engineer, Enterprise (> 1000 emp.) **Reviewed Date:** March 12, 2026 **What do you like best about Cast AI?** What I value most is the granular, real-time visibility and the "app-aware" engine that scales resources based on actual workload DNA rather than just generic metrics. The seamless integration with our existing CI/CD pipelines meant we saw performance improvements and massive cost reductions within hours of deployment. It has effectively bridged the gap between our DevOps and FinOps goals through one unified, automated control plane **What do you dislike about Cast AI?** While the automation is powerful, the "black box" nature of the decision logic can initially make it difficult to trust the system with mission-critical production workloads without extensive testing of the guardrails. We also found that the coordination between the Workload Autoscaler and Node Autoscaler could be tighter, as they sometimes operate independently rather than planning for future node utilization in perfect tandem. Additionally, the "percentage of savings" pricing model can feel like a "savings tax" as you scale, making it harder to predict long-term tool costs compared to a flat-tier subscription **What problems is Cast AI solving and how is that benefiting you?** Cast AI solves the persistent "Kubernetes waste" problem by automating rightsizing, bin-packing, and spot instance orchestration that are traditionally too complex to manage manually at scale. For me, this has replaced hours of tedious YAML tuning and "firefighting" during traffic spikes with a reliable, autonomous engine that keeps our clusters lean and high-performing. The biggest benefit is the reclaimed time; I can finally focus on high-impact architectural work instead of constantly babysitting node groups and cloud bills. ### 13. Transforming Kubernetes into a Self-Optimizing System through Automated Node Orchestration **Rating:** 5.0/5.0 stars **Reviewed by:** Saravanan M. | DevOps Engineer, Enterprise (> 1000 emp.) **Reviewed Date:** February 12, 2026 **What do you like best about Cast AI?** Cast AI automates node orchestration and right-sizing, using advanced bin-packing to ensure high cluster density and performance. It effectively manages spot instance lifecycles with automated fallback, significantly reducing compute overhead and manual infrastructure tuning **What do you dislike about Cast AI?** The initial configuration of IAM permissions and automation guardrails requires a careful setup phase before fully handing over infrastructure control **What problems is Cast AI solving and how is that benefiting you?** Cast AI eliminates over-provisioning and node fragmentation through aggressive, real-time bin-packing and automated rightsizing of CPU/memory requests. It automates the entire lifecycle of node provisioning and spot instance orchestration, including seamless fallbacks to on-demand instances during market interruptions. This transforms our Kubernetes clusters into a self-optimizing system, significantly reducing our cloud spend while freeing the team to focus on delivery pipelines rather than manual scaling policies ### 14. Automates Kubernetes and Cuts Costs Effectively **Rating:** 4.5/5.0 stars **Reviewed by:** Pramod P. | Enterprise (> 1000 emp.) **Reviewed Date:** February 26, 2026 **What do you like best about Cast AI?** I use CAST AI to automatically optimize the Kubernetes workload, which helps cut cloud costs without needing manual tuning. It eliminates the manual effort of managing autoscaling, node provisioning, and performance monitoring, allowing me to focus on building features instead of babysitting infrastructure. I particularly appreciate the completely automated Kubernetes optimization that actually works. I also experience massive cost savings with real-time analytics, and the real-time cost-saving feature lets me see where my money goes. The automated cost-saving means I don't have to manually tune the cluster. **What do you dislike about Cast AI?** I think the documentation and support guidance could be more consistent, particularly in areas like advanced autoscaling configurations. Clear, unified guidance with scenario-based examples and transparent troubleshooting notes would greatly enhance the onboarding experience. **What problems is Cast AI solving and how is that benefiting you?** I use CAST AI to automatically optimize Kubernetes workloads, cutting cloud costs without manual tuning. It eliminates the manual effort of managing autoscaling, node provisioning, and performance monitoring, allowing me to focus on features instead of infrastructure. ### 15. Revolutionized our HPC Workloads and Cost Optimization **Rating:** 4.5/5.0 stars **Reviewed by:** Louis B. | Enterprise (> 1000 emp.) **Reviewed Date:** February 24, 2026 **What do you like best about Cast AI?** I use CAST AI extensively to optimize the scaling of our HPC workloads on AWS EKS. The tech is great, providing both effective features and scalability. Cost optimization for Spot instances on AWS, a pretty tough problem, is solved amazingly well. There is no equivalent in AWS native feature or open-source components that come anywhere close to CAST AI's technology. The user and developer experience is smooth and fits well with the intended audience. Collaborating with the engineering team is fast and efficient, as they deliver fixes and features in record time. I also like the simple initial setup as onboarding through the helm chart requires limited involvement and the readonly mode allow to discover the products and insights without risks. **What do you dislike about Cast AI?** N/A **What problems is Cast AI solving and how is that benefiting you?** I use CAST AI to optimize the scaling of HPC workloads on AWS EKS, solving tough Spot instance cost problems effectively and allowing simultaneous multi-cluster scaling, unlike AWS native autoscaler and Karpenter. ### 16. CAST AI Full Autopilot That Actually Executes Changes in Real Time **Rating:** 5.0/5.0 stars **Reviewed by:** Sai Vishaal V. | Devops Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** February 23, 2026 **What do you like best about Cast AI?** What I like most is the Full Autopilot mode. Unlike other tools that just give you a list of recommendations that you have to implement manually, CAST AI actually executes the changes. It handles rightsizing, autoscaling, and spot instance management in real-time without our team having to intervene daily. **What do you dislike about Cast AI?** Honestly, the biggest hurdle was just the initial 'trust fall' with Autopilot. It’s a bit stressful giving an external tool the keys to your production environment to spin up or terminate nodes on its own. We had to spend a good chunk of time in a sandbox environment and go through several security reviews before the team felt comfortable letting it run on full-auto. Once that trust was built, it was fine, but that first week definitely had us on edge. **What problems is Cast AI solving and how is that benefiting you?** Our biggest issue was 'Cloud Waste.' Our engineers used to over-provision pods 'just in case,' and we were paying for massive amounts of idle CPU and RAM. CAST AI solved this by automating the bin-packing process. It has completely removed the manual guesswork from cluster management. We no longer have to spend hours every week manually adjusting instance types or scaling policies; the tool just picks the most cost-effective compute for the workload in real-time. ### 17. Autonomous K8s Optimization with Smooth Onboarding **Rating:** 4.5/5.0 stars **Reviewed by:** Rishabh A. **Reviewed Date:** February 14, 2026 **What do you like best about Cast AI?** I like CAST AI because it makes Kubernetes optimization truly autonomous. It handles the hardest and most time-consuming parts of K8s operation with features like fully autonomous optimization, which means zero manual tuning for us. The instant and reliable autoscaling is impressive, and I love the intelligent spot instance usage that offers massive savings without risk. The clear and actionable cost visibility is another standout feature. Our initial setup experience with CAST AI was smooth and straightforward, with fast and easy cluster onboarding that had no impact on our existing workloads. Plus, we received clear recommendations right after setup. **What do you dislike about Cast AI?** There are a few areas where the experience could be improved: some advanced features have a learning curve, the UI could be more streamlined in certain areas, recommendations could offer more context, and reporting could be more customizable. **What problems is Cast AI solving and how is that benefiting you?** I use CAST AI to automate Kubernetes cluster optimization, solve cloud cost challenges, eliminate manual resource tuning, and overprovisioning issues. It provides real-time rightsizing and makes autoscaling efficient, handling spot instance complexity without risk. ### 18. CAST AI Makes Kubernetes Cost Optimization Truly Automated and Reliable **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Automotive | Mid-Market (51-1000 emp.) **Reviewed Date:** February 13, 2026 **What do you like best about Cast AI?** what i like best about cast ai is how it turns kubernetes cost optimization from a manual, reactive task into an automated, intelligent system. instead of relying on static node groups or basic cluster autoscaler logic, cast ai continuously analyzes pod resource requests, actual utilization, bin-packing efficiency, and real-time spot and on-demand pricing. based on that data, it dynamically selects the most cost-effective instance types without manual intervention. a few things that stand out: workload-aware autoscaling that optimizes instance type selection, not just node count strong spot optimization with stability controls for production workloads clear visibility into over-provisioning and inefficiencies reduced operational overhead compared to manually tuning node groups in my experience using it in a production kubernetes environment, the biggest value is continuous optimization without compromising reliability. it feels less like a monitoring dashboard and more like an active control layer for infrastructure cost efficiency. **What do you dislike about Cast AI?** while cast ai delivers strong automation and cost optimization, there is a learning curve in fully understanding and trusting its automated decision-making, especially for teams used to managing infrastructure at a very granular level. in some cases, having deeper visibility into the exact reasoning behind instance selection or node replacement decisions would add even more confidence during audits or incident reviews. however, these are more about enhancing transparency and familiarity rather than limitations in capability, and with proper kubernetes configuration and governance, the platform performs reliably and efficiently. **What problems is Cast AI solving and how is that benefiting you?** cast ai is solving the core problem of kubernetes cost inefficiency and over-provisioned infrastructure by continuously optimizing compute selection, bin-packing, and spot utilization in real time. instead of relying on static node groups or periodic manual reviews, it automatically matches workloads to the most cost-effective instance types based on actual usage and market pricing. this has reduced waste from idle resources, improved cluster efficiency, and minimized the engineering time spent on manual tuning and capacity planning. the biggest benefit for me has been shifting from reactive cost control to continuous, automated optimization while maintaining production stability. ### 19. Effortless Cost Efficiency and Monitoring in Multi-Cloud Management **Rating:** 5.0/5.0 stars **Reviewed by:** Yogendra K. | Enterprise (> 1000 emp.) **Reviewed Date:** February 25, 2026 **What do you like best about Cast AI?** I use CAST AI to manage our infrastructure, especially various EKS and GKE clusters for right sizing. It helps me select cost-efficient instances automatically based on use cases, which avoids over and under-provisioning. I love that it monitors resource utilization and cost across multi-cloud platforms and takes action automatically without downtime. The ability to replace over-provisioned instances with the most cost-efficient ones in real-time, particularly across multiple cloud environments like AWS and GCP, is really beneficial for me. I also like the finance-friendly dashboard and continuous insights that highlight cost-saving opportunities. **What do you dislike about Cast AI?** For a beginner, it seems a little bit harder as a learning curve. **What problems is Cast AI solving and how is that benefiting you?** CAST AI helps manage our EKS, GKE clusters by right-sizing and automatically selecting cost-efficient instances, preventing over or under provisioning. It monitors resource use across multicloud platforms, ensuring uptime without downtime and providing continuous cost-saving insights via a finance-friendly dashboard. ### 20. Cast AI Automates Autoscaling and Instance Selection to Cut Costs Without Performance Loss **Rating:** 5.0/5.0 stars **Reviewed by:** Akhil S. | Head of Infrastructure and Security, Mid-Market (51-1000 emp.) **Reviewed Date:** February 11, 2026 **What do you like best about Cast AI?** Instead of teams spending hours right-sizing nodes, tweaking autoscalers, or guessing instance types, Cast AI continuously analyzes workload requirements and automatically: Selects the most cost-efficient instance types Optimizes cluster autoscaling in real time Uses spot instances intelligently to reduce costs Eliminates overprovisioning without impacting performance **What do you dislike about Cast AI?** Vendor dependency: Relying on automation to make core infrastructure decisions increases our operational dependence on a third party. **What problems is Cast AI solving and how is that benefiting you?** Cast AI solves the problem of overprovisioned Kubernetes clusters and high cloud costs by automatically selecting the most cost-efficient instances and optimizing scaling in real time. This benefits us by lowering infrastructure spend, improving resource utilization, reducing manual DevOps effort, and allowing us to scale confidently without constantly managing costs. ### 21. Automated GCP Savings with Safe, Predictable Kubernetes Optimization **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Computer Software | Mid-Market (51-1000 emp.) **Reviewed Date:** February 10, 2026 **What do you like best about Cast AI?** Cast AI has delivered real, automated cost reductions in GCP for our highly scalable communications API without compromising reliability or control. It continuously optimizes our Kubernetes workloads in a way that is safe, predictable, and well-aligned with production requirements. What I value most is that our SRE team stays firmly in control. Cast AI provides clear recommendations, enforces guardrails we define, and automates execution without becoming a black box. We’ve seen meaningful infrastructure savings with zero production impact and minimal ongoing effort. For a team operating at scale, that balance of automation, safety, and control is the biggest win. **What do you dislike about Cast AI?** The main gap is around higher-level visibility and reporting. While the optimization itself works well, turning the data into clear, executive-ready views of savings trends and attribution takes some extra effort. Additionally, more built-in explanation behind certain recommendations would help with onboarding and broader team understanding. Improving reporting and explainability would make an already strong platform even better. **What problems is Cast AI solving and how is that benefiting you?** Before Cast AI, optimizing GCP costs for our Kubernetes infrastructure required constant manual tuning by our SRE team, with real risk of over-correcting in a production environment that needs to scale reliably and predictably. With Cast AI, cost optimization is continuous and automated while staying within guardrails we control. Our infrastructure now right-sizes itself as workloads change, without sacrificing reliability or requiring ongoing hands-on effort from the team. The result has been sustained infrastructure cost reductions, faster scaling decisions, and significantly less operational overhead for SREs. That frees the team to focus on reliability and platform improvements instead of manual cost management. ### 22. A tool that changes the game **Rating:** 4.5/5.0 stars **Reviewed by:** Diego W. | Platform Engineering Tech Manager, Mid-Market (51-1000 emp.) **Reviewed Date:** February 12, 2026 **What do you like best about Cast AI?** I appreciate the range of diverse resources that the tool offers and, in addition to the constant updates focused on improving the tool, it is possible to notice the appreciation of those who work on this project and their commitment to excellence. The ability to identify how much each team's application running on the cluster costs is sensational. **What do you dislike about Cast AI?** Sometimes, its scaling is not that interesting. I don't know if this happens because of the calculations of Requests and Limits, but it often tries to spin up some nodes with absurd amounts of memory, which would make me spend much more than save. Therefore, it is necessary to impose several limits and not let it run loose; otherwise, the cost may end up increasing instead of decreasing. **What problems is Cast AI solving and how is that benefiting you?** Currently, I am using CAST AI to control the automatic scaling of my cluster, as well as the vertical scaling of my workloads, focusing on cost reduction and operation optimization. This is possible thanks to the artificial intelligence of CAST AI in allocating nodes with the best machine type for optimized operation, performance, and cost. Additionally, I also use CAST AI to turn on and off my non-productive clusters, focusing on cost savings. ### 23. CAST AI Automates Cloud Savings with Spot Instances—Like a 24/7 DevOps Pro **Rating:** 5.0/5.0 stars **Reviewed by:** aananthi m. | Lead engineer, Small-Business (50 or fewer emp.) **Reviewed Date:** February 12, 2026 **What do you like best about Cast AI?** I love how CAST AI doesn't just point out where I'm overspending, but actually goes in and automates the fixes so I don't have to manually tweak clusters all day. Its ability to handle Spot Instances is a total lifesaver because it keeps my apps running smoothly while cutting my cloud bill by more than half. Honestly, it’s like having a dedicated DevOps pro working 24/7 to make sure we’re never paying for more than we actually use. **What do you dislike about Cast AI?** One thing I find frustrating is that giving up that much control to an autonomous "black box" can be nerve-wracking, especially since it occasionally restarts workloads to move them and the reporting isn't always as granular as the native cloud billing I'm used to. **What problems is Cast AI solving and how is that benefiting you?** CAST AI is essentially solving the massive headache of manual Kubernetes scaling and "cloud waste" by using autonomous AI to rightsize my resources and manage Spot Instances in real-time, which basically means I get to slash my cloud bill by 50% or more without my DevOps team having to babysit the infrastructure 24/7. ### 24. AI-Powered Efficiency with Room for Notification Improvements **Rating:** 5.0/5.0 stars **Reviewed by:** Renan B. **Reviewed Date:** February 11, 2026 **What do you like best about Cast AI?** I appreciate the range of diverse features that CAST AI offers, and in addition to the constant updates focused on improving the tool, it's possible to perceive the appreciation of those working on this project and their commitment to excellence. I like how the tool revolves around AI, but it allows for fine-tuning of configurations to achieve excellence. The ability to allocate different machines to nodes within the same pool without requiring multiple configurations is particularly beneficial. The initial setup is super simple, basically running two or three commands on the cluster and all the configuration is ready. **What do you dislike about Cast AI?** I believe the notification aspect of CAST AI could be improved; it's very simple, which is good for usability, but bad because it makes it limited. It would be interesting to have the possibility of receiving error alerts and alerts when recovering from those errors, allowing us to actively monitor the tool's operation. **What problems is Cast AI solving and how is that benefiting you?** I use CAST AI for automatic scaling and optimal resource allocation, which saves costs and reduces team overload by eliminating manual cluster optimization. ### 25. Cost-Saving Automation with Robust Support **Rating:** 5.0/5.0 stars **Reviewed by:** Ritwik G. | Lead Software Engineer, Small-Business (50 or fewer emp.) **Reviewed Date:** February 11, 2026 **What do you like best about Cast AI?** I really like the way CAST AI is designed because it aligns well with our company culture. Their support is also very helpful, which I appreciate. I find the AI support integration features beneficial for addressing simple questions and debugging scenarios. The ease of enabling features like vertical auto scaling and scheduling rebalancing is quite user-friendly. I also value the early access feature that allows us to hibernate our development and staging clusters during off hours, which is very helpful for us. And one big feature that helped us a lot was the ability to allocate a part of workload in to spot machines. **What do you dislike about Cast AI?** There's definitely a learning curve to the platform, especially understanding the concepts and how it scales costs for you. Also, the billing aspect takes some time to understand. And some guarantees on the billing aspects (like if the cost reduction doesn't happen as expected) would reduce the initial inhibition of customers to adapt the system. For us personally another challenge was understanding how to measure cast AI's performance once we start using their system but also our own system starts growing. Actually there are indicators in the cast AI for this (like the cluster score, CPU & Memory over-provisioning percentage etc). Tracking these actually helps us understand if the cast AI's optimisations are as good as when we started. This could be documented as an indicator for cast AI's efficiency which will help the customers a lot. **What problems is Cast AI solving and how is that benefiting you?** We use CAST AI mainly for cost optimizations and dev ops. It's successfully brought down our costs and helped with automation to optimize for cost and stability. Features like vertical auto scaling, rebalancing, splitting workload in to on-demand & spot machines and hibernation of clusters during off hours are very helpful. ### 26. Effortless Cost Management and Automatic Scaling **Rating:** 4.5/5.0 stars **Reviewed by:** Clément A. **Reviewed Date:** February 11, 2026 **What do you like best about Cast AI?** I really like the simplicity of setting up CAST AI; it was super easy and took less than an hour. We just followed the docs, and within minutes, everything was running smoothly. One of the standout benefits for us has been the cost reduction. We've managed to reduce our costs by a huge 60% since using it. I appreciate how CAST AI ensures that pod limits and requests align with real usage thanks to rightsizing. Previously, managing node pools manually was a hassle. With CAST AI, it's fully automatic, so we don't have to worry about manually creating new node pools to match our resource needs anymore. **What do you dislike about Cast AI?** We had a small glitch at some point where the cluster scaled up because of a poorly configured workload but it didn't scale down automatically which end-up augmenting our costs a lot. We definitely need a way to have alerting when the daily price is more than x% of the previous days. **What problems is Cast AI solving and how is that benefiting you?** CAST AI reduces our infrastructure costs by efficiently using resources, automatically scaling on demand, and utilizing preemptible VMs. It's easy to set up, taking under an hour, and saves us 60% on costs. Previously, node pools were managed manually; now, everything is automated, freeing us from resource management. ### 27. Efficient Cost Optimization for Multi-Cloud **Rating:** 4.0/5.0 stars **Reviewed by:** Sumeet B. **Reviewed Date:** February 11, 2026 **What do you like best about Cast AI?** I like the valuable cost insights dashboard that helps us track and analyze our cluster expenses over time along with future cost predictions, making it easier to set and maintain our cost goals. The node recommendations by CAST AI ensure workloads run efficiently. If a node is over-provisioned, it suggests the right sized instances. I enjoy the integration with multiple clouds, including AWS, Azure, and GCP, as the onboarding experience with CAST AI has been extremely smooth. Additionally, I appreciate that many teams in our organization have switched to CAST AI because it is efficient and saves time for our team. **What do you dislike about Cast AI?** Sometimes the suggestions are too aggressive for nodes and it may lead to workload discrepancy. **What problems is Cast AI solving and how is that benefiting you?** I use CAST AI for easy k8s cost optimization, efficient infrastructure management, and smooth multi-cloud integration. It helps track expenses, predict costs, and optimize node provisioning, saving our team time and effort compared to our previous manual process. ### 28. Streamlined Node Management & Cost Optimization with CAST AI **Rating:** 4.5/5.0 stars **Reviewed by:** Aniket R. **Reviewed Date:** February 11, 2026 **What do you like best about Cast AI?** I use CAST AI primarily for node management and workload optimization within our Kubernetes clusters. I like its ability to automatically select the most cost-effective nodes for our workloads, taking a lot of guesswork out of infrastructure management. The intelligent instance selection feature dynamically chooses the optimal mix based on performance requirements and cost efficiency. Additionally, I appreciate its recommendations for right-sizing workloads, which help to save resources. The setup is also quick and easy, and onboarding clusters onto CAST AI is straightforward, with Terraform assisting in managing CAST AI resources. **What do you dislike about Cast AI?** One area that could be improved is dynamic workload right-sizing. While it's useful, it heavily relies on past actual usage to forecast and adjust resource requests and limits. It works, but in cases where traffic spikes unusually, it doesn't always adapt quickly enough. **What problems is Cast AI solving and how is that benefiting you?** I use CAST AI for node management and workload optimization in Kubernetes clusters. It helps with cluster capacity management, auto scales, and optimizes resource allocation to prevent overprovisioning and high costs. Its intelligent instance selection and dynamic recommendations improve cost efficiency and right-sizing. ### 29. Efficient EKS Optimization with Stellar Support **Rating:** 4.0/5.0 stars **Reviewed by:** Alex R. **Reviewed Date:** February 10, 2026 **What do you like best about Cast AI?** I like the UI and the ease with which I can operate an EKS cluster on CAST AI. For example, rebalancing one of our largest clusters back to an optimized state took just a few clicks. Even someone who's not familiar with coding and Terraform can easily accomplish this task. I also like their support, particularly how I can interact with them via Slack. They respond extremely quickly, dive deep into any issues, and are available to jump on a call and explain things thoroughly. Additionally, Cast AI's solution comes with their own autoscaler, which allowed us to start seeing savings immediately. Their support was very helpful during the setup process, assisting us in all aspects. **What do you dislike about Cast AI?** I think if there was an easier way to upgrade EKS clusters that would be a plus because currently after upgrading you still need to manually rebalance the nodes for all of them to get upgraded, where with EKS auto mode it's completely automated. **What problems is Cast AI solving and how is that benefiting you?** CAST AI optimizes my EKS cluster, reducing costs with easy scaling. I love the UI and quick adjustments, even for non-coders. It saves me money immediately compared to Sedai with less effort since it includes an autoscaler. ### 30. Streamlined Infrastructure with API Integration **Rating:** 4.5/5.0 stars **Reviewed by:** Naveen S. **Reviewed Date:** February 10, 2026 **What do you like best about Cast AI?** I like that CAST AI provides a single panel for visualizing and tracking performance, monitoring, and security. It gives us the visibility we need. Most functionalities are exposed via APIs, which enables us to automate security monitoring. I appreciate the easy and secure access to the dashboard and how it integrates with our internal security tools. This helps our lean team maintain compliance and security posture effectively. **What do you dislike about Cast AI?** While having APIs is the right step, those APIs provide only high-level information. The ability to provide granular details and expose wider API endpoints with more query parameters would be great. It took us some time as the APIs didn't provide all the information we needed. **What problems is Cast AI solving and how is that benefiting you?** CAST AI provides a single panel for visualizing and tracking performance, monitoring, and security, and offers APIs to automate security monitoring. It enables our lean team to maintain compliance and security through easy integration with internal tools. ### 31. Essential Tool for Kubernetes Efficiency and Cost Control **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User in Consumer Services | Enterprise (> 1000 emp.) **Reviewed Date:** February 10, 2026 **What do you like best about Cast AI?** What I value most is how Cast AI turns complex Kubernetes optimization into a seamless, automated process. It consistently delivers significant cost savings (often up to 50%) by intelligently handling Spot instances and right-sizing workloads in real-time. The automated autoscaling is much more responsive than native cloud tools, and the 'set and forget' nature of the platform has effectively removed the burden of manual cluster tuning from our DevOps team. **What do you dislike about Cast AI?** Initial Trust & Security Approval: Because Cast AI requires deep access to your cloud environment to perform automated actions (such as adding or deleting nodes), the initial security review—especially in larger organizations—can be lengthy and quite rigorous. Learning Curve for Advanced Policies: While the “read-only” setup is fast to get up and running, getting comfortable with the more advanced automation policies—and correctly fine-tuning the “Auto-Remediation” settings—takes time and a strong understanding of Kubernetes to avoid unexpected behavior.Cast AI solves the persistent issues of cloud waste and operational complexity. Before, we spent too much time manually adjusting resources and overpaying for idle capacity. Now, the platform benefits us by automatically selecting the most cost-effective nodes without compromising performance. This hasn't just lowered our monthly bill; it has given our engineers the peace of mind and the time to focus on building features rather than managing infrastructure. **What problems is Cast AI solving and how is that benefiting you?** Cast AI addresses the ongoing problem of cloud waste and the heavy, manual burden of cluster tuning. Previously, we struggled with overprovisioning and with the complexity of managing Spot instances safely. By automating real-time selection of the most cost-effective nodes, it has reduced our cloud bill significantly while still maintaining high availability. For us, the biggest benefit is the operational peace of mind: our DevOps team no longer spends hours on infrastructure rightsizing and can instead focus on shipping better code. ### 32. Cost Reduction and Efficient Automation **Rating:** 4.5/5.0 stars **Reviewed by:** Marcelo P. **Reviewed Date:** February 10, 2026 **What do you like best about Cast AI?** I really like the vulnerability visualization feature that CAST AI provides; it identifies vulnerabilities and shows the vulnerable images, highlighting their criticality. I also really appreciate all the information it provides and the possibility of automations, which is very important for the organization and greatly eases the daily workload. **What do you dislike about Cast AI?** One of the areas where CAST AI needs to evolve is in the matter of multiple organizations within the same company. Today, there is no centralized management, which makes daily operations quite difficult. If you have an environment that requires an organization, you need to create another one, which makes managing these tokens more difficult. Furthermore, there is no centralization in terms of a main organization and subsidiary organizations. This ends up multiplying the users, and they need to be managed manually, complicating the management of the tool. **What problems is Cast AI solving and how is that benefiting you?** I use CAST AI to optimize costs, improve the visualization of our Kubernetes-based environments, and automate tasks, which eases our daily workload. It solves cost issues in large environments, provides insights into container vulnerabilities, and aids in the predictability of operations. ### 33. Efficient Cost-Saving with Stellar Support **Rating:** 5.0/5.0 stars **Reviewed by:** Valerii V. **Reviewed Date:** February 10, 2026 **What do you like best about Cast AI?** I really like how CAST AI works and appreciate that it benefits us by saving costs. Workload rightsizing is a unique feature that saves a lot of resources and costs. It's easy to configure and very flexible. Support is responsive. CAST AI is way ahead of tools like Karpenter, which are partly used under the hood but don't compare to CAST AI's offerings. The user interface is nice and customizable, which makes it convenient to use. It works well with Terraform and GitOps tools like ArgoCD. Using API for configurations alongside these tools adds to the seamless experience. **What do you dislike about Cast AI?** One of our clients is on Azure and some of CAST AI's features seem to be made more for AWS and do not work well in the UI for Azure, but there are ways to make them work over API so it's a minor issue. **What problems is Cast AI solving and how is that benefiting you?** CAST AI saves costs, offers workload rightsizing, and is flexible and easy to configure. It has a nice UI, works well with Terraform and ArgoCD, and uses saving plans. Support is responsive, and it's a step ahead of tools like Karpenter. ### 34. CAST AI Automates Kubernetes Optimization with Measurable Cost Savings **Rating:** 4.0/5.0 stars **Reviewed by:** Sanjeev Kishore Y. | Director of Engineering Infrastructure , Security and Investigations, Mid-Market (51-1000 emp.) **Reviewed Date:** February 18, 2026 **What do you like best about Cast AI?** CAST AI is most helpful because it automates Kubernetes optimization at scale, and the upside is measurable cost savings, improved resource efficiency, and reduced operational overhead. **What do you dislike about Cast AI?** the downsides aren’t about capability — CAST AI does what it’s designed to do well — but around transparency, scope expectations, and fit. It works best when clusters are large, workloads are variable, and teams are comfortable embracing automation. **What problems is Cast AI solving and how is that benefiting you?** Castai solves: Inefficient resource allocation, Manual scaling complexity, Spot instance, risk management, Cloud cost unpredictability. And the benefit has been: Lower infrastructure costs Better workload stability Less operational overhead More time for the team to focus on platform improvements rather than infrastructure tuning ### 35. Great automation for K8S, but there is still a lot to improve. **Rating:** 2.5/5.0 stars **Reviewed by:** Yaroslav G. | DevOps Manager, Mid-Market (51-1000 emp.) **Reviewed Date:** February 11, 2026 **What do you like best about Cast AI?** Users of CAST AI appreciate the shift away from manual “firefighting” toward higher-level automation, especially features like Scalers and Performance. At the same time, some users place less value on the User Interface and Dashboards. **What do you dislike about Cast AI?** Lack of professionalism at the support level, lack of knowledge about how the components work and what they are responsible for, lack of logs, disorganized version deployment. No partner updates regarding changes to features and versions themselves. Changing feature definitions in new versions without updating the client. Full of bugs in Terraform's code. **What problems is Cast AI solving and how is that benefiting you?** It usually solves scale problems at a good level, not optimal at the moment. It provides a solution for cost optimization. ### 36. Powerful Cost Monitoring with Intuitive Interface **Rating:** 4.5/5.0 stars **Reviewed by:** Chandan T. **Reviewed Date:** February 11, 2026 **What do you like best about Cast AI?** I like how CAST AI calculates the cost on a daily basis and provides insightful financial granularity. This helps me catch spending spikes in real time and manage my services effectively to prevent bill shock. The new console UI is friendly and easy to use, which simplifies handling complex Kubernetes data. I love how it turns these complexities into a one-click executive dashboard for effortless FinOps management. Setting it up was very easy, even without much previous experience, making my transition to using CAST AI smooth and problem-free. **What do you dislike about Cast AI?** Sometimes it's hard to believe in the product and the trustworthiness of the data it provides. More cost-accurate calculations are needed. **What problems is Cast AI solving and how is that benefiting you?** I use CAST AI for cost monitoring and cluster configuration. It helps me track high or low costs, manage services, stop bill shock with daily insights, and simplify Kubernetes data into an easy dashboard for FinOps management. ### 37. Powerful Dashboard, Challenging Configuration **Rating:** 3.5/5.0 stars **Reviewed by:** Facundo B. | Mid-Market (51-1000 emp.) **Reviewed Date:** March 05, 2026 **What do you like best about Cast AI?** I like being able to see everything on the dashboards and make decisions right there with CAST AI. These dashboards are a very good tool for resource identification and optimization, allowing me to quickly visualize where I need to focus. **What do you dislike about Cast AI?** I feel a bit insecure about putting CAST AI in automatic mode because, although it is simple, it can affect production. There is no intuitive and quick way to ensure it, and I feel like I have to set many rules to prevent it from downscaling things it shouldn't for some reason. Also, the initial setup gave us quite a struggle because we use Terraform and had to deal with permission issues, among other things. **What problems is Cast AI solving and how is that benefiting you?** CAST AI helps me identify important improvement points in service optimization, and its dashboards present clear information for quick decisions in the company. ### 38. Total control and visibility: efficient optimization of costs, CPU, and memory with Cast AI **Rating:** 5.0/5.0 stars **Reviewed by:** Deivid P. | Gerente DevOps e Plataforma, Mid-Market (51-1000 emp.) **Reviewed Date:** February 11, 2026 **What do you like best about Cast AI?** With Cast AI, I can clearly identify the amount of CPUs and memory that my cluster is using, as well as the cost it is generating. Additionally, the tool allows for very efficient and secure resource optimization, providing more control and visibility over the environment's usage. **What do you dislike about Cast AI?** No disadvantages. Since we started using Cast AI in our environments, it has helped us a lot in cost optimization. **What problems is Cast AI solving and how is that benefiting you?** Cast AI has been helping us control and reduce the costs of our environments by offering the best cluster instances in terms of cost-effectiveness and automatically optimizing our workloads. ### 39. Unbeatable GKE automation, but needs deeper cost visibility **Rating:** 5.0/5.0 stars **Reviewed by:** Rafael Jose T. | Mid-Market (51-1000 emp.) **Reviewed Date:** March 27, 2024 **What do you like best about Cast AI?** The reliability of the node scaling continues to be a highlight; we have zero anxiety about node recreation even during high-traffic events. The 'set it and forget it' nature of the infra management is still the strongest selling point. Stability has been rock solid over the last year. **What do you dislike about Cast AI?** I still find the cost reporting to be the weak point compared to the GKE/GCP console. I need better visibility into specific node template costs and a clearer breakdown of infrastructure prices. Currently, reconciling Cast AI's reported figures with our actual GCP billing lacks the necessary granularity. We need more transparency on the total amounts for specific node groups to fully trust the cost data. **What problems is Cast AI solving and how is that benefiting you?** We have an issue to handle the nodes before using cast, with cast when we need more pods they handle everything. ### 40. Effortless Cost Optimization and Autoscaling **Rating:** 4.0/5.0 stars **Reviewed by:** Rishabh T. **Reviewed Date:** February 10, 2026 **What do you like best about Cast AI?** I like CAST AI for its workload autoscaling, which helps with optimization while maintaining availability. It has helped me achieve about 70% cost optimization with better bin packing, rebalancing, and pod mutation to maintain a diverse workload. I also appreciate having a single place to manage multiple clusters and daily costing. The initial setup was very easy and well-guided by the CAST AI team. **What do you dislike about Cast AI?** I think it could be better for handling StatefulSets in bin packing. It can handle better StatefulSets to make sure pods should handle state while eviction and reschedule in other nodes. **What problems is Cast AI solving and how is that benefiting you?** I use CAST AI for cost optimization and autoscaling in our K8s clusters. It solved issues with node choices, better autoscaling, bin packing, and minimizing sudden cost increases. It helped achieve 70% cost optimization and provides a single place to manage clusters and daily costs. ### 41. Automated Rightsizing That Cuts Cloud Spend Without Sacrificing Performance **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User in Logistics and Supply Chain | Mid-Market (51-1000 emp.) **Reviewed Date:** February 13, 2026 **What do you like best about Cast AI?** The automated workload optimization and rightsizing capabilities are the most effective features. The platform’s ability to analyze resource requirements in real-time and automatically adjust instances ensures that the infrastructure remains efficient without requiring constant manual intervention from the engineering team. This lead to a measurable reduction in cloud spend while maintaining cluster performance. **What do you dislike about Cast AI?** While the initial returns were simple to realize on lower environments, the transition to production required more effort. We found that iterative tuning was necessary to ensure there was no application degradation or performance impact. A more seamless "plug-and-play" experience for high-traffic production workloads would be a valuable improvement. **What problems is Cast AI solving and how is that benefiting you?** Cast AI addresses the challenge of managing complex cloud infrastructure costs and resource allocation. By automating the rightsizing of clusters and optimizing instance selection, it removes the manual burden of infrastructure tuning from our engineering teams. ### 42. Efficient Kubernetes Optimization with Evolving Features **Rating:** 5.0/5.0 stars **Reviewed by:** Gopi B. **Reviewed Date:** February 10, 2026 **What do you like best about Cast AI?** I like CAST AI because it's intuitive, reliable, and continuously evolving. Its reliability gives me confidence that optimization and automation actions won't disrupt workloads, which is critical in production. The platform evolves quickly with new features like Container Live Migration, which really adds value. The setup process was straightforward and well-guided, with initial onboarding and cluster connection being smooth. The UI and docs made it easy. I would rate them 10 for allowing us to save costs as well. **What do you dislike about Cast AI?** While we've had a very good overall experience with CAST AI, there are few areas that could be improved. Having more fine-grained controls and clear explanations would help. **What problems is Cast AI solving and how is that benefiting you?** I use CAST AI to optimize Kubernetes workloads efficiently, reducing operating overhead. It's reliable, evolves quickly with features like Container Live Migration, and saves costs, all without disrupting critical workloads. ### 43. Cast.AI Auto-Adjusts Resources to Keep Alerts Quiet and On-Call Nights Peaceful **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Financial Services | Enterprise (> 1000 emp.) **Reviewed Date:** February 11, 2026 **What do you like best about Cast AI?** The ability to immediately apply values to a manifest is my favorite feature. Being in a on-call rotation, this prevents unnecessary alerts from triggering in the middle of the night. Cast.AI will automatically adjust the resources, which clears the alert and I'm not woken up at night. **What do you dislike about Cast AI?** While Cast.AI automatically tells you how much you could to save BEFORE you turn it on. It doesn't track how much you've saved AFTER as you turn it on app by app. That would be a nice feature to have since the data is already there. **What problems is Cast AI solving and how is that benefiting you?** Rightsizing applications is a difficult and tedious task, but there could be a lot of wasted resources if you don't. Cast.AI lets us rightsize out applications to reduce that waste and in the end, save money. It basically pays for itself over time. ### 44. A very effective tool for reducing costs on Kubernetes and optimizing resources **Rating:** 5.0/5.0 stars **Reviewed by:** Raphaël D. | Lead Developer, Enterprise (> 1000 emp.) **Reviewed Date:** February 11, 2026 **What do you like best about Cast AI?** Easy installation and quick setup. I noticed a significant reduction in overprovisioning and cloud costs. Resource utilization is better, with fewer workloads lacking resources. **What do you dislike about Cast AI?** The learning module does not yet allow for the definition of business days. Consequently, weekend activity can disproportionately influence resource calculations. **What problems is Cast AI solving and how is that benefiting you?** It allows you to no longer have to perform a manual review of resources, which becomes humanly impossible on a large cluster. Bin packing helps reduce the number of nodes needed and, consequently, the bill. ### 45. Effortless Compute Management with Stellar Support **Rating:** 4.0/5.0 stars **Reviewed by:** Rajkumar S. | Senior devops Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** February 11, 2026 **What do you like best about Cast AI?** I find CAST AI easy to use and the dashboard is great as it provides a centralized view in one place. When we onboard a cluster, we can see cost trends, the node list, and spot on-demand options at a glance. I also appreciate that the CAST AI team is very supportive. They are ready to add custom features for specific needs like GPU and TPU support. Overall, I think it's a great team and a great product, and I'm very happy with it. **What do you dislike about Cast AI?** Some features, like GCP sole tenant, are very basic and not customizable. They could improve on their Terraform. **What problems is Cast AI solving and how is that benefiting you?** CAST AI optimizes compute allocation based on application usage and public cloud pricing. It provides a centralized dashboard showing cost trends and node lists, and the team supports custom features. ### 46. Efficient Resource Management with User-Friendly Interface **Rating:** 4.5/5.0 stars **Reviewed by:** Aakash M. | Mid-Market (51-1000 emp.) **Reviewed Date:** March 09, 2026 **What do you like best about Cast AI?** I use CAST AI for cost and resource monitoring, and it provides a great experience in reducing my costs significantly. I like that it tells about resource efficiency, making resources more efficient without wasting money. Even a non-tech person can use it easily, and it suggests the right resources for services or workload. It's pretty much easy to set up. **What do you dislike about Cast AI?** As an improvement, currently it has a lag of 1 hour which needs to come down to live as possible. **What problems is Cast AI solving and how is that benefiting you?** I use CAST AI for cost and resource monitoring. It helps reduce costs significantly by improving resource efficiency and suggesting the right resources for services. Even a non-tech person can use it easily, making it user-friendly. ### 47. Mostly Hands-Free, Tailored Results with Room to Grow in Cost Charts **Rating:** 4.5/5.0 stars **Reviewed by:** Piotr M. | DevSecOps Lead, Small-Business (50 or fewer emp.) **Reviewed Date:** February 10, 2026 **What do you like best about Cast AI?** It is mostly hands free and with little more work you can achieve tailored results. **What do you dislike about Cast AI?** Not a dislike but room to improvement, I would like to have some more dimensions in cost charts and also better visibility on k8s events related to cast **What problems is Cast AI solving and how is that benefiting you?** Aggressive autoscaling. We put heavy lift on AI processing pipelines that are heavy by design so Cast allow us to scale best suited machine from the pool for time that almost match the time required to run and it doesn't matter is it 5 or 500 instances ### 48. Flexible API with Responsive Engineering Support **Rating:** 5.0/5.0 stars **Reviewed by:** Eva K. | Senior Cloud Platform Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** February 11, 2026 **What do you like best about Cast AI?** What I have been involved most with is the API. So I like the flexibility it provides. We used it to build our cost dashboards that we need to for our internal cost monitoring Also we had chats with the engineering team for some of our requests and they were very responsive which made collaboration smooth and helped us get our results in time. **What do you dislike about Cast AI?** My primary interaction was with the API overall Im very happy with it, maybe some more examples in the documentation would be nice. **What problems is Cast AI solving and how is that benefiting you?** We use Cast AI cost exporting API to allocate costs at the workload level live. This level of detail its very important to us it gives us better cost visibility and enables us to take actions based on it. ### 49. Simple, Team-Friendly UI for Easy Right-Sizing **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User in Banking | Enterprise (> 1000 emp.) **Reviewed Date:** February 10, 2026 **What do you like best about Cast AI?** Cast AI has a simple UI that makes it easy for everyone on your team to understand what’s overprovisioned, along with suggested steps to take to right-size the resources. That simplicity is what I like best. **What do you dislike about Cast AI?** The Cast AI interface sometimes suggests optimisations that, if applied blindly, may not actually be optimal. I’ve noticed this particularly with resources that are only up for short durations (1–2 minutes) per run, but run frequently (multiple times a day), such as background jobs running on spot instances. **What problems is Cast AI solving and how is that benefiting you?** Cast AI is tackling one of the biggest challenges with cloud environments: cost optimisation. Over the past few months, we’ve saved more than 30% of our overall cloud costs by carefully validating Cast AI’s recommendations and then putting them into practice. ### 50. Stellar Customer-Centric Platform for K8s on AWS **Rating:** 5.0/5.0 stars **Reviewed by:** Hariharan G. | Principal data engineer **Reviewed Date:** February 12, 2026 **What do you like best about Cast AI?** CAST AI abstracts the nuisance of provisioning and scaling nodes and its UI provides a holistic view of the cluster status with usage metrics. I really appreciate the cluster rebalancer that optimizes workloads. The node template and node configuration offer superb flexibility for segregating workloads. What I love most is CAST AI's obsession with customer feedback. They connected with us to understand our requirement for using fractional GPU on templates and surprisingly delivered this feature in just two weeks. **What do you dislike about Cast AI?** I dont have any **What problems is Cast AI solving and how is that benefiting you?** I use CAST AI to manage k8s workloads on AWS EKS. It abstracts node provisioning and scaling, offers a holistic cluster view, and optimizes workloads with cluster rebalancer. It provides superb flexibility with node templates and configurations. ## Cast AI Discussions - [What is CAST AI used for?](https://www.g2.com/discussions/what-is-cast-ai-used-for) - 1 comment, 1 upvote - [View Cast AI pricing details and edition comparison](https://www.g2.com/products/cast-ai/reviews?section=pricing&secure%5Bexpires_at%5D=2026-05-13+08%3A34%3A46+-0500&secure%5Bsession_id%5D=e02e4308-a844-404e-96e2-8a3da0cf482e&secure%5Btoken%5D=005f2d1827bb0ea4b00fc25ba99902ed124d156fe4184466ffb3ccb4fc6c7638&format=llm_user) ## Cast AI Integrations - [Amazon EC2](https://www.g2.com/products/amazon-ec2/reviews) - [Amazon Elastic Kubernetes Service (Amazon EKS)](https://www.g2.com/products/amazon-elastic-kubernetes-service-amazon-eks/reviews) - [Argo CD](https://www.g2.com/products/argo-cd/reviews) - [Azure](https://www.g2.com/products/hopem-azure/reviews) - [Datadog](https://www.g2.com/products/datadog/reviews) - [Devtron](https://www.g2.com/products/devtron/reviews) - [Google Cloud](https://www.g2.com/products/google-cloud/reviews) - [Google Kubernetes Engine (GKE)](https://www.g2.com/products/google-kubernetes-engine-gke/reviews) - [Grafana Labs](https://www.g2.com/products/grafana-labs/reviews) - [IBM Terraform (formerly HashiCorp Terraform)](https://www.g2.com/products/ibm-terraform-formerly-hashicorp-terraform/reviews) - [Jira](https://www.g2.com/products/jira/reviews) - [Kubernetes](https://www.g2.com/products/kubernetes/reviews) - [Microsoft Azure](https://www.g2.com/products/microsoft-microsoft-azure/reviews) - [Prometheus](https://www.g2.com/products/prometheus/reviews) - [Pulumi](https://www.g2.com/products/pulumi/reviews) - [Slack](https://www.g2.com/products/slack/reviews) - [Slack Connector for Jira](https://www.g2.com/products/slack-connector-for-jira/reviews) ## Cast AI Features **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 - Cloud Resource Management - Cloud Backup and Recovery **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 **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) - [Datadog](https://www.g2.com/products/datadog/reviews) - 4.4/5.0 (689 reviews) - [Amazon CloudWatch](https://www.g2.com/products/amazon-cloudwatch/reviews) - 4.3/5.0 (360 reviews)