CAST-AI

CAST AI Kubernetes auto-optimization Spot ML.

Definition

CAST AI features: (1) Cluster Autoscaler: intelligent autoscaling EKS + AKS + GKE node groups based on pending pods + node utilization, optimal instance type selection mixed Spot + On-Demand + Reserved Instances, automatic bin-packing. (2) Spot Instances Optimization: up to 90% cost reduction via aggressive Spot adoption with automatic interruption handling, Spot diversification across instance types/zones reduces interruption risk. (3) Workload Autoscaler: automatically adjusts HorizontalPodAutoscaler + VerticalPodAutoscaler parameters based ML usage patterns. (4) Container Right Sizing: analyzes actual container CPU + memory usage vs requests, recommends right-sized requests, automatically applies via Mutating Webhook. (5) Commitment Manager: automated RI + Savings Plans recommendations + purchases via AWS Cost Optimization Hub integration. (6) Multi-cluster Cost Reporting + savings dashboard. Pricing: CAST AI takes percentage of savings achieved (typically 5-25% of $ saved, no upfront cost), aligned with customer savings. Customers: Salesforce, Twitter, Akamai, T-Mobile, etc.

Origin

CAST AI founded 2019 ; Series A $10M 2020 ; Series B $20M 2022 ; ~1500 customers 2024 ; ~$1B collective optimization 2024.

Example in context

Akamai Technologies Kubernetes Linode Akamai cloud platform deploys CAST AI on 50+ production clusters, achieves 65% cost reduction via automated Spot Instances optimization + container right-sizing, ~$15M annual savings vs static configuration baseline.

Related terms

• Kubecost / OpenCost — alternative monitoring.