CAST AI is an Autonomous Kubernetes platform that automates cloud cost optimization by continuously monitoring and adjusting resource allocation, achieving up to 50% savings on cloud expenses. The platform enhances application performance and security while eliminating downtime through features like automated scaling and provisioning.
Funding
$86.8M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
VIFounders
Product
Problem
Managing Kubernetes infrastructure across cloud providers like AWS, Azure, and GCP can lead to significant cloud costs due to over-provisioning, inefficient resource allocation, and the complexities of manual optimization. Organizations struggle to maintain optimal configurations, resulting in wasted resources and increased operational overhead.
Solution
CAST AI provides an autonomous Kubernetes platform that automates cloud cost optimization, enhances application performance, and improves DevOps productivity. The platform continuously monitors Kubernetes clusters and dynamically adjusts resource allocation in real-time, leveraging features like automated scaling, provisioning, and bin packing to reduce cloud expenses. By automatically selecting the best fitting node types and optimizing the utilization of resources, CAST AI enables organizations to achieve substantial cost savings without sacrificing uptime or performance. The platform also offers features for Kubernetes cost monitoring and container security, providing a comprehensive solution for managing and optimizing Kubernetes deployments.
Target Audience
CAST AI targets FinOps and DevOps teams, SREs, and platform engineers who manage Kubernetes infrastructure and are responsible for cloud cost optimization, application performance, and security.
Features
- Automated scaling and provisioning to continuously optimize resource allocation
- Real-time Kubernetes cost monitoring with detailed breakdowns by cluster, workloads, and labels
- Automated remediation for container and Kubernetes security vulnerabilities
- AI Enabler to optimize Large Language Model (LLM) deployments for GenAI applications
- Support for multiple cloud providers, including AWS, Azure, and GCP
- Integration with Committed Use Discounts (CUDs) to maximize cost savings
- Dynamic node type selection to ensure optimal workload placement
- Bin packing to compress workloads and eliminate unnecessary nodes
- Automated node provisioning to simplify cluster management