--- title: Cast AI Reviews meta_title: 'Cast AI Reviews 2026: Details, Pricing, & Features | G2' meta_description: Filter 193 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: 4.6 review_count: 193 scale: '5' date_modified: '2026-07-17' 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:** 4.6/5.0 **Total Reviews:** 193 ## 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 **cost management capabilities** of CAST AI, effectively reducing cluster costs and avoiding interruptions. (53 reviews) - Users value the **cost-saving benefits** of CAST AI, significantly reducing cloud expenses while optimizing Kubernetes management. (53 reviews) - Users value the **ease of use** of CAST AI, finding it straightforward to set up and operate effectively. (50 reviews) - Users highlight the **significant cost reduction** achieved with CAST AI through autonomous optimization and intelligent resource management. (49 reviews) - Users value the **cost-effectiveness** of CAST AI for managing clusters and optimizing expenses effortlessly. (48 reviews) - Auto Scaling (41 reviews) - Users value the **cost-saving recommendations** from CAST AI, enhancing optimization and automation for Kubernetes clusters. (40 reviews) - Automation (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 face **scaling issues** with CAST AI when dealing with bursty workloads, affecting performance and budget management. (13 reviews) - Users find the **pricing high** , especially for small clusters, despite the tool paying for itself initially. (12 reviews) - Users face a **substantial learning curve** with advanced features, requiring time to understand and utilize effectively. (11 reviews) - Users report **poor documentation** leading to confusion and contradictory information from support, complicating their experience. (10 reviews) - Users find **pricing issues** with CAST AI, highlighting a need for improved clarity and transparency in cost reporting. (10 reviews) - Users find the **UI navigation to be slow** , suggesting simplification and better accessibility for an improved experience. (10 reviews) - Users express concerns about **integration issues** affecting cost accuracy and workload optimization in Cast AI. (9 reviews) - Difficulty in Usage (8 reviews) - Software Bugs (8 reviews) - Complexity (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. ## 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/cast-ai-review-13076801?section=pricing&secure%5Bexpires_at%5D=2026-07-18+15%3A39%3A20+-0500&secure%5Bsession_id%5D=4844adf7-2838-4f07-8b4a-d4bb2275d280&secure%5Btoken%5D=63bb21151cc62e7044895529fab64524abdd7b3206a756080e4f652f7eb85dd8&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) - [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) - [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 (288 reviews) - [Amazon CloudWatch](https://www.g2.com/products/amazon-cloudwatch/reviews) - 4.3/5.0 (363 reviews) - [Datadog](https://www.g2.com/products/datadog/reviews) - 4.4/5.0 (714 reviews)