AI-powered Kubernetes optimization platform that automatically rightsizes workloads, manages spot instances, and self-heals clusters. Delivers 40-70% cloud cost savings with zero manual intervention.
Cast AI automatically optimizes Kubernetes clusters through AI-powered rightsizing, spot instance management, and self-healing automation. Delivers 40-70% cost savings with zero manual intervention.
Cast AI eliminates the gap between Kubernetes cost monitoring and actual optimization. While tools like Kubecost show you over-provisioned resources and recommend changes, Cast AI automatically implements those optimizations in real-time.
The platform's AI engine analyzes workload patterns at millicore precision and continuously adjusts CPU/memory requests to match actual usage. This goes beyond basic threshold-based autoscaling to predictive resource management trained on data from 2,100+ organizations.
Spot instance management is where Cast AI truly differentiates itself. The platform predicts AWS/GCP/Azure spot interruptions up to 30 minutes before they occur and seamlessly pre-migrates workloads. This enables companies like Yotpo to run 70%+ of production workloads on spot instances with zero downtime—something previously too risky without predictive capabilities.
The self-healing automation (drift remediation, automatic OOM recovery, container image updates, policy enforcement) handles operational issues before they impact users. All automated changes flow through configurable approval workflows with SLO-based guardrails monitoring error rates, latency, and resource exhaustion.
Cast AI starts in read-only mode, providing detailed savings analysis and recommendations before you enable any automated changes. This risk-free evaluation period lets you verify projected savings are realistic for your specific workloads before granting write permissions.
With $170M+ in funding (including a $108M Series D in 2025), Cast AI has established itself as the dominant automated Kubernetes optimization platform. Their 2025 benchmark report analyzing 2,100+ organizations found average clusters waste 63% of compute spend—the exact problem Cast AI was built to solve.
The trade-off: Usage-based pricing means you pay a percentage of managed compute (typically 15-25% of realized savings). For massive deployments, this percentage can reduce net savings, and you must grant significant cluster permissions for full automation.This beats hiring a dedicated FinOps engineer ($150K/year) plus the time cost of manual optimization. Cast AI's automation scales across unlimited clusters while human optimization doesn't.
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Cast AI is the definitive choice for teams wanting Kubernetes cost optimization to happen automatically, not just appear in dashboards. The read-only onboarding eliminates risk, spot instance management is genuinely differentiated, and enterprise results (40-70% savings) are well-documented across 2,100+ organizations. The trade-off is usage-based pricing and Enterprise-gated advanced features. Best for teams spending $10K+/month who want hands-off optimization that pays for itself.
AI analyzes actual resource consumption patterns and automatically adjusts CPU/memory requests at millicore precision. Continuous optimization prevents both over-provisioning waste and under-provisioning performance issues.
Use Case:
A 200-workload production cluster automatically reduces compute costs by 50% by matching resource allocations to real usage patterns instead of developer estimates, saving $15K/month without performance degradation.
Predictive engine forecasts spot interruptions up to 30 minutes ahead and pre-migrates workloads before AWS/GCP/Azure reclaims capacity. Maintains 99.9%+ availability while maximizing spot savings.
Use Case:
Production e-commerce platform runs 70% of workloads on spot instances during Black Friday with zero downtime, saving $25K/month vs on-demand pricing through automated interruption handling.
AI agents automatically remediate configuration drift, recover from OOM kills, update container images, and enforce security policies. All changes flow through approval workflows with rollback capabilities.
Use Case:
Automatically detects and recovers from a memory-starved pod by rightsizing its allocation and restarting it in under 2 minutes, preventing customer-facing outages before the on-call engineer is notified.
Real-time workload placement across AWS EKS, Azure AKS, and Google GKE based on pricing, availability, and performance metrics. Single control plane manages optimization policies across all providers.
Use Case:
Financial services firm saves additional 20% by automatically shifting dev/test workloads to the cheapest cloud provider while keeping production on their primary provider, reducing multi-cloud spend by $8K/month.
Monitors error rates, latency percentiles, and resource saturation to ensure optimizations never compromise service quality. Automatically backs out changes that impact SLO compliance.
Use Case:
API service maintains 99.95% uptime while reducing infrastructure costs by 65% through SLO-constrained rightsizing that prevents optimization changes when error rates approach thresholds.
Granular cost allocation by cluster, namespace, service, and team with anomaly detection and savings forecasting. Shows exact ROI of each optimization action taken.
Use Case:
Platform team identifies that a single microservice is consuming 40% of cluster budget due to misconfigured resource requests, automatically optimizes it, and tracks the $12K/month savings back to specific code deployments.
$0
Usage-based (billed against managed compute)
Custom
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