Cast AI
Using AI to optimize Kubernetes clusters to cut cloud costs and boost performance
CAST AI is a Kubernetes automation platform that optimizes cloud costs, performance, and security for AWS, Azure, GCP, and hybrid environments. It uses machine learning to analyze and adjust Kubernetes clusters in real time, reducing costs by over 50% for many users. The platform supports managed Kubernetes services like EKS, AKS, and GKE, as well as self-managed setups like OpenShift. Its key features include Autoscaler for dynamic resource allocation, Spot Instance Automation for cost-efficient instance management, and Kvisor for real-time security scanning. CAST AI Anywhere extends optimization to on-premises and hybrid clusters.
The platform integrates seamlessly with major cloud providers, requiring no changes to existing tech stacks. Users can start with an agentless discovery process, connecting cloud accounts via a read-only script to analyze clusters. The dashboard provides detailed cost monitoring at cluster, namespace, and workload levels. Recent additions, like the AI Optimizer, enhance efficiency for AI workloads by selecting cost-effective large language models. Customer feedback highlights significant savings — Branch reported millions saved annually on AWS — and rapid onboarding, often delivering results within days.
CAST AI competes with CloudZero, Zesty, and Exostellar — which offer broader FinOps solutions but less Kubernetes-specific automation. CAST AI’s focus makes it ideal for container-heavy environments, though its reliance on Kubernetes expertise may challenge smaller teams. Pricing is usage-based, offering flexibility but requiring careful monitoring for unpredictable workloads. Recent X posts praise the platform’s cost savings and support, though some users note a learning curve.
The free plan includes unlimited cost monitoring and security checks, while paid plans unlock automation features like autoscaling and spot instance management. The platform’s SOC2 and SOC3 compliance ensures enterprise-grade security. A 2025 report noted only 10% CPU and 23% memory utilization in typical Kubernetes clusters, underscoring CAST AI’s value in reducing waste.
Start with the free plan to assess your cluster’s optimization potential. Use the agentless discovery to avoid upfront commitments, and leverage live chat support for setup guidance. Ensure your team has basic Kubernetes knowledge to maximize the platform’s benefits.
Homepage Screenshot 📸
Video Overview 🎬
What are the key features? ✨
- Autoscaler: Dynamically adjusts compute instances to match real-time demand, optimizing costs.
- Spot Instance Automation: Manages spot instance lifecycles, falling back to on-demand resources if needed.
- Kvisor: Scans Kubernetes clusters for vulnerabilities and misconfigurations in real time.
- AI Optimizer: Selects cost-efficient large language models for AI workloads via OpenAI-compatible APIs.
- CAST AI Anywhere: Optimizes resources for on-premises, hybrid, or non-major cloud Kubernetes setups.
Who is it for? 🤔
Examples of what you can use it for 💡
- DevOps Engineer: Uses Autoscaler to dynamically adjust resources, reducing cloud costs during low-demand periods.
- Cloud Architect: Leverages Spot Instance Automation to optimize costs while maintaining application reliability.
- Security Analyst: Employs Kvisor to identify and prioritize Kubernetes cluster vulnerabilities in real time.
- AI Developer: Utilizes AI Optimizer to select cost-efficient large language models for AI workloads.
- IT Manager: Implements CAST AI Anywhere to optimize on-premises Kubernetes clusters for hybrid setups.
Pros & Cons ⚖️
- Cuts cloud costs by over 50%.
- Automates Kubernetes optimization.
- Supports major cloud providers.
- Onboarding can be complex.
- Limited non-Kubernetes support.
FAQs 💬
Ready to try Cast AI?
Using AI to optimize Kubernetes clusters to cut cloud costs and boost performance
Visit Cast AI ↗Cast AI alternatives 🔗
-
Pump
Optimizes cloud costs using AI and group buying for startups
-
Antimetal
Optimizes AWS cloud costs using AI-driven analysis and automation
-
Azure AI Foundry
Cloud-based platform designed to facilitate the development, training, and deployment of AI models
-
Helicone
Manage, scale, and optimize Large Language Models (LLMs)
-
XenonStack
Enterprise-ready solution based on the use of your data with reliable outputs for business transformation
-
Arize
Monitors and evaluates AI models for performance and reliability in production
