Cast AI is a Kubernetes optimization platform focused on reliability, performance, and cost control. By turning Kubernetes workloads, infrastructure, cost, and SLO signals into safe automated actions, it helps cloud-native teams achieve automated operations.
Purpose
Cast AI is designed to address complexity in Kubernetes environments, such as constantly changing CPU, memory, replica count, latency, and cost requirements for workloads, while tools such as Karpenter, HPA, VPA, Spot, requests/limits, and node pools still require platform teams to manually integrate recommendations, tickets, dashboards, and YAML changes. Cast AI closes the loop between Kubernetes signals and reliable automated operations, helping platform, SRE, and FinOps teams automate workload sizing, GPU and AI infrastructure optimization, and cost control without sacrificing reliability.
Key capabilities
- Application performance and Kubernetes automation: Turn workload, infrastructure, cost, and SLO signals into safe automated actions, including Pod sizing, node scaling, GPU and Spot optimization, and issue remediation without manual tuning.
- Self-healing operations: Use agentic Runbooks to remediate drift, image issues, policy violations, and operational failures, with approval workflows ensuring every change is approved before going live.
- Enterprise-grade security: Provide Kubernetes cost and performance intelligence, with visibility into actual, requested, and provisioned usage by cluster, namespace, workload, team, CPU, memory, and GPU.
- Workload sizing: Automatically adjust CPU, memory, requests, limits, and replica counts based on actual workload behavior, reducing over-provisioning without affecting applications.
- Infrastructure automation: Provision the right compute resources, improve bin packing, predict Spot interruptions, and extend Karpenter with workload-aware decisions.
- Predictive models: Advanced predictive models trained on thousands of clusters and millions of real workloads analyze the "DNA" of application demand, going beyond "if-then" logic.
- Application-aware reliability: Predict Spot interruptions up to 30 minutes in advance, gracefully migrating workloads before users perceive slowdowns.
- Precise sizing: Adjust CPU and memory at the millicore level, preventing resource starvation and "noisy neighbor" issues.
- Intelligent workload placement: Instantly match each Pod to the best instance type, ensuring high-demand AI and data workloads run on the best hardware.
How to use
- Connect a cluster: Connect to EKS, AKS, GKE, or an on-premises cluster within minutes, with no changes required to infrastructure.
- Start in read-only mode: The platform first observes actual workload behavior (rather than static configuration) in read-only mode and identifies optimization opportunities.
- Automated operations: Cast AI automatically scales, sizes, and rebalances based on real-time signals (rather than scheduled tasks).
- Approval workflows: Use agentic Runbooks to remediate operational and security issues, but every change requires user approval before it is released.
Use cases
- Platform, SRE, and FinOps teams want to automate Kubernetes workload sizing, GPU and AI infrastructure optimization, and cost control.
- Enterprises need to reduce cloud costs without affecting reliability.
- Teams using EKS, AKS, GKE, or on-premises Kubernetes clusters.
- Scenarios that require predicting Spot interruptions and gracefully migrating workloads.
Limitations
The materials do not provide detailed information about pricing, specific billing models, supported platform languages, minimum cluster size, or specific technical limitations.
