CAST AI
Overview
Kubernetes automation platform that optimizes cost and performance with autoscaling, rightsizing, Spot automation, and monitoring.
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.
AI Agent Store research
What the evidence says about CAST AI
CAST AI is best understood as a workflow automation platform. Kubernetes optimization platform for reliability and performance. CAST AI uses SLO signals to take guardrailed actions in production.
Last reviewed July 30, 2026
Verified capabilities
- Workflow automation
Kubernetes optimization platform for reliability and performance. CAST AI uses SLO signals to take guardrailed actions in production.[1]
Where it fits best
- Reducing Kubernetes cloud costs via automated rightsizing, bin packing, and resource allocation optimization.[1]
Sources and research method (1)
We record only claims tied to public sources checked by our team or listing workflow. Counts above are derived directly from this profile, not a subjective rating.
- Kubernetes Optimization Platform for Performance - Cast AIOfficial site · checked 2026-07-30
Autonomy level
84%
Comparisons
Custom Comparisons
Some of the use cases of CAST AI:
- 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.
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Popularity level: 81%
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