CEO
Rafay Systems
Rafay provides a centralized platform for managing the lifecycle of Kubernetes clusters across public and private clouds, enabling multi-cluster configuration and policy standardization. This solution reduces operational complexity and costs while enhancing developer productivity through automated self-service workflows and efficient resource management.
- Artificial Intelligence
- Developer Tools
- Enterprise Software
- Software Only
Updated 23 months ago
Funding
Raised to date
$33MRaised 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.
Funding rounds are not available yet.
Founders
Product
Problem
Managing Kubernetes clusters across diverse cloud environments introduces complexity in configuration, policy enforcement, and resource utilization. This complexity hinders developer productivity and increases operational overhead for platform teams.
Solution
Rafay provides a centralized platform for managing the lifecycle of Kubernetes clusters and GPU resources across public clouds, private data centers, and edge environments. The platform enables multi-cluster configuration and policy standardization, simplifying operations and enhancing governance. Rafay automates self-service workflows, allowing developers to access resources on-demand while ensuring compliance with security and financial requirements. By providing a unified console for managing CPU- and GPU-based workloads, Rafay increases efficiency and reduces cloud costs. The platform also supports the creation of preconfigured AI workspaces, enabling data scientists and developers to accelerate AI/ML initiatives.
Target Audience
Rafay is designed for platform teams, cloud architects, and I&O leaders who need centralized automation and governance of Kubernetes clusters, as well as enterprises and service providers looking to simplify AI and cloud-native infrastructure management.
Features
- Centralized management of Kubernetes clusters across multi-cloud and hybrid environments
- Automated self-service workflows for developers to access cloud resources
- Multi-cluster configuration and policy standardization
- Support for CPU- and GPU-based workloads
- GPU virtualization and resource allocation controls
- Integration with CI/CD pipelines (e.g., Jenkins, Terraform)
- Zero-trust Kubernetes security framework with RBAC policy management
- Unified monitoring plane for proactive performance optimization
- Support for AI/ML model deployment and management
- Automated workload distribution across Kubernetes clusters and cloud providers