CAST AI
CAST AI automates Kubernetes optimization to reduce cloud costs by 50%.
CAST AI is a machine learning-powered platform that automatically optimizes Kubernetes clusters and cloud infrastructure in real time, reducing cloud costs by 50% or more while improving application performance and security. Unlike competitors that merely monitor and recommend fixes, CAST AI actively implements optimizations across AWS, Azure, GCP, and on-premises environments. The platform uses advanced ML algorithms to continuously analyze workload behavior, automate resource allocation, and eliminate waste through intelligent bin packing and pod scheduling.
Problem solved
Engineering teams waste compute resources and overspend on cloud infrastructure due to inefficient resource allocation, manual scaling, and lack of real-time optimization across multi-cloud environments.
Target customer
Engineering and DevOps teams at mid-market to enterprise companies running Kubernetes workloads on AWS, Azure, GCP, or on-premises infrastructure with significant cloud spend.
Founders
Y
Yuri Frayman
Founder & CEO
Serial entrepreneur with 20+ years of experience, built 5 successful ventures generating $750M+ in exits. Previously co-founded Viewdle (acquired by Google) and Zenedge (acquired by Oracle in 2018).
L
Laurent Gil
President & Co-Founder
Co-founder of Viewdle (ML startup acquired by Google) and Zenedge (cybersecurity platform acquired by Oracle in 2018).
L
Leon Kuperman
CTO & Co-Founder
Co-founder of Viewdle and Zenedge, bringing deep technical expertise in machine learning and infrastructure optimization.
Funding history
Seed
Unknown
2019-2020
Led by Unknown
· Unknown
Series A
Unknown
Unknown
Led by Cota Capital
· Unknown
Series B
$35M
Unknown
Led by Vintage Investment Partners
· Creandum, Uncorrelated Ventures
Series C
$108M
April 30, 2025
Led by G2 Venture Partners, SoftBank Vision Fund 2
· Aglaé Ventures (Bernard Arnault), Hedosophia, Cota Capital, Vintage Investment Partners, Creandum, Uncorrelated Ventures
Total raised:
$272M
Industries
Pricing
Usage-based pricing tied to compute consumption and cloud savings. Free plan available with $0/month entry. Growth plan at $1,000/month plus $5/CPU/month. Enterprise plans are custom. CAST AI often takes a percentage of the cloud cost savings delivered.
Notable customers
Akamai, BMW, FICO, Hugging Face, NielsenIQ, Swisscom, project44, Yotpo, OpenX, Branch.io, ShareChat
Integrations
AWS, Microsoft Azure, Google Cloud Platform, Red Hat OpenShift, Oracle Cloud, IBM Cloud, Linode, on-premises Kubernetes clusters
Tech stack
Lodash (JavaScript libraries)
Swiper (JavaScript libraries)
jQuery Migrate (JavaScript libraries)
jQuery (JavaScript libraries)
core-js (JavaScript libraries)
HubSpot Chat (Live chat)
Prism (UI frameworks)
Open Graph
WordPress (Blogs)
Linkedin Insight Tag (Analytics)
HubSpot Analytics (Analytics)
Matomo Analytics (Analytics)
Leadfeeder (Analytics)
Google Analytics (Analytics)
Facebook Pixel (Analytics)
HSTS (Security)
Google Font API (Font scripts)
Gutenberg (Editors)
WP Rocket (Caching)
PHP (Programming languages)
Google Workspace (Email)
cdnjs (CDN)
Cloudflare (CDN)
HubSpot (Marketing automation)
MySQL (Analytics)
Reddit Ads (Advertising)
Twitter Ads (Advertising)
Microsoft Advertising (Advertising)
Google Tag Manager (Tag managers)
Yoast SEO Premium (SEO)
Yoast SEO (SEO)
Google Optimize (A/B Testing)
The Events Calendar (WordPress plugins)
HubSpot WordPress plugin (WordPress plugins)
Website
Competitors
Spot.io (Spot Intelligence)
Focuses primarily on spot instance automation but lacks CAST AI's comprehensive multi-cloud functionality and universal metrics across different cloud providers.
ScaleOps
Competitor in the Kubernetes optimization space but with narrower scope compared to CAST AI's end-to-end automation and multi-cloud support.
Vantage
Financial software focused on cloud cost visibility and management rather than automated real-time optimization across infrastructure.
Why this matters: CAST AI represents a new category of AI-powered infrastructure optimization that moves beyond visibility and recommendations to active automation. With $272M in funding backed by SoftBank Vision Fund 2 and Bernard Arnault's investment firm, and marquee customers like Akamai and BMW, CAST AI is establishing the standard for intelligent, automated Kubernetes cost optimization in the multi-cloud era.
Best for: DevOps and engineering teams managing Kubernetes clusters across multiple clouds who want to reduce cloud costs significantly without manual intervention or constant firefighting.
Use cases
Cost Reduction for High-Scale Infrastructure
A company like Akamai with massive infrastructure footprint uses CAST AI to automatically optimize resource allocation and spot instance usage, achieving 40-70% cost savings depending on workload type. The automation also frees engineering time previously spent on manual optimization.
Multi-Cloud Cluster Optimization
Enterprise organizations running Kubernetes across AWS, Azure, and GCP use CAST AI's unified platform to optimize workload distribution across all clouds simultaneously, ensuring cost-efficient resource allocation without cloud lock-in or manual intervention.
Performance and Cost Balance
Teams monitoring SLOs (error rates, latency, OOM kills) use CAST AI to automatically scale and allocate resources before users notice degradation. CAST AI acts proactively on performance signals while keeping costs minimal as a byproduct.
Spot Instance Optimization
Companies like Yotpo reduced spot instance costs by 30% using CAST AI's machine learning to predict interruptions and automatically migrate workloads, improving both cost and reliability.
Alternatives
Spot.io
Choose Spot.io if you need specialized spot instance optimization but don't require comprehensive multi-cloud automation and unified metrics across providers.
Kubecost
Choose Kubecost for detailed cost visibility and analytics; choose CAST AI if you want active automated optimization that reduces costs without manual intervention.
CloudHealth (VMware)
Choose CloudHealth for broader cloud governance and compliance; choose CAST AI for specialized real-time Kubernetes optimization and cost reduction.
FAQ
What does CAST AI do? +
CAST AI is a machine learning platform that automatically optimizes Kubernetes clusters in real time, reducing cloud costs by 50% or more while improving performance. It actively implements optimizations like resource allocation, pod scheduling, and workload distribution across AWS, Azure, GCP, and on-premises environments, eliminating the need for manual cluster management.
How much does CAST AI cost? +
CAST AI uses usage-based pricing tied to actual compute consumption. A free plan is available at $0/month, a Growth plan starts at $1,000/month plus $5/CPU/month, and Enterprise plans are custom. CAST AI often charges a percentage of the cloud savings it delivers, so you only pay more when you save more.
What are alternatives to CAST AI? +
Spot.io specializes in spot instance optimization but with less comprehensive multi-cloud support. Kubecost provides detailed cost visibility and analytics. CloudHealth offers broader cloud governance. Choose CAST AI if you want active real-time automation rather than monitoring and recommendations.
Who uses CAST AI? +
Target customers are engineering and DevOps teams at mid-market to enterprise companies running Kubernetes. Notable customers include Akamai, BMW, FICO, Hugging Face, NielsenIQ, Swisscom, project44, and Yotpo, which reported 40-70% cost savings.
How does CAST AI compare to Spot.io? +
While Spot.io focuses on spot instance automation using machine learning, CAST AI provides comprehensive multi-cloud functionality with universal metrics that work seamlessly across AWS, Azure, GCP, and on-premises. CAST AI actively implements optimizations across the entire cluster rather than just recommending changes.
Can CAST AI work with on-premises Kubernetes? +
Yes, CAST AI extends its automation and cost optimization capabilities beyond the three major cloud providers to on-premises Kubernetes clusters and other cloud providers like Red Hat OpenShift, Oracle Cloud, IBM Cloud, and Linode.
What kind of cost savings can we expect? +
Savings vary by workload and usage patterns. Akamai achieved 40-70% depending on workload, project44 saw 50% cost reduction in their initial cluster, and Yotpo reduced spot instance costs by 30%. Average reported savings are 50% or more on cloud spend.
Tags
Kubernetes optimization
cloud cost reduction
machine learning
multi-cloud
DevOps automation
infrastructure optimization
FinOps