Cast AI
CAST AI is an autonomous Kubernetes optimization platform that continuously right-sizes workloads, consolidates underused nodes, and provisions capacity from existing node templates to cut cloud costs by up to 50%. The platform starts in a read-only mode, then automates changes within user-defined policies, opening pull requests for code-level fixes. It supports AWS, Azure, GCP, and Oracle Cloud, and is trusted by over 2,100 companies.
- Developer Tools
- AI Agents
- Software Only
- Artificial Intelligence
- Enterprise Software
Funding
Raised to date
$153MRaised 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.
across 2 rounds
$108M · Undisclosed
Announced Apr 2025
Disclosed cumulative: $143M
Founders
Product
Problem
Managing Kubernetes infrastructure across cloud providers like AWS, Azure, and GCP often leads to significant cloud cost overruns due to over-provisioning, inefficient resource allocation, and the manual effort required to continuously tune cluster configurations. Teams frequently pay for idle capacity and spend excessive engineering time on infrastructure upkeep instead of feature development.
Solution
CAST AI provides an autonomous Kubernetes platform that continuously monitors clusters and automatically optimizes resource allocation in real time. The platform rightsizes workload CPU and memory requests based on observed usage, bin-packs pods onto fewer, better-fitting nodes, and moves eligible workloads to spot instances to reduce expenses. It provisions nodes from existing node templates and consolidates underused nodes, all within policies set by the user. When a fix belongs in code, such as Helm values or Terraform, CAST AI opens a pull request for the team to review and merge. The platform also offers GPU optimization, storage autoscaling, and database query caching to further reduce infrastructure waste.
Target Audience
Primary users are 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 workload rightsizing that sets CPU and memory requests from observed usage patterns
- Bin packing to compress workloads onto fewer, better-fitting nodes and eliminate unnecessary capacity
- Node provisioning from user-defined node templates and consolidation of underused nodes
- Agentic workflows that open pull requests for infrastructure fixes in code (e.g., Helm values, Terraform)
- Read-only agent with least-privilege access, pre-egress scrubbing of sensitive environment variables, and ISO 27001/SOC 2 Type II certification
- Support for AWS, Azure, GCP, and Oracle Cloud, plus on-premises environments (OpenShift, Tanzu, kOps, bare metal)
- GPU sharing and cross-cloud GPU access (OMNI Compute) to source capacity from other clouds and regions
- Storage Autoscaler that grows volumes before they fill and tunes IOPS to demand
- Database Optimizer that finds slow queries, caches reads, and pools connections
- OpsPilot natural-language assistant for querying pod, cost, and health data