CAST AI
Kubernetes automation platform that optimizes cost and performance with autoscaling, rightsizing, Spot automation, and monitoring.
🛡️ AgentReady threat assessment
MAESTRO 7-layer threat model + OWASP AIVSS risk score for CAST AI, derived from its capabilities.
These scores are auto-generated from public information (the agent's own listing, docs, and repository) using the canonical OWASP AIVSS formula and the MAESTRO framework — an estimate for guidance, not a penetration test, audit, or certification. See the scoring methodology — every score is re-derived by the same automated method as an agent's public evidence changes.
Overview
CAST AI is an application performance automation platform for Kubernetes that helps teams reduce cloud spend and improve workload performance through automated optimization. It provides Kubernetes cost and performance monitoring, then adds automation such as intelligent autoscaling, rightsizing, bin packing, and Spot Instance orchestration with fallback to maintain reliability. CAST AI supports major managed Kubernetes services (EKS, GKE, AKS) and other environments, and offers usage-based plans with a free tier for monitoring plus paid tiers for automated optimization features.
Key features and capabilities
- kubernetes
- autoscaling
- cost optimization
- rightsizing
- bin packing
- spot instances
- finops
- resource allocation
- cluster optimization
- cost monitoring
Use cases
- Reducing Kubernetes cloud costs via automated rightsizing, bin packing, and resource allocation optimization.
- Autoscaling clusters and workloads with Spot Instance automation and fallback to on-demand capacity.
- Monitoring Kubernetes cost and performance with real-time dashboards and retention.
- Improving DevOps productivity by automating optimization decisions that are otherwise manual and ongoing.