Cloud Cost Optimization

Optimize cloud cost by detecting and fixing resource allocation issues through intelligent policy-driven controls. This ensures Kubernetes and cloud resources are rightsized for better application utilization, performance and cost efficiency.

Rafay can automatically detect and clean up cloud environments that are no longer in use, and automatically enforce policies to ensure that resources provisioned for short term needs are freed after preset time-to-live (TTL) limits.

With Rafay, cloud teams can set schedule policies to ensure that persistent cloud resources only run when they are needed (for example, during weekdays). Policy limits can be set to restrict the number of environments teams can create simultaneously.

Improved sharing of Kubernetes clusters allows multiple applications to utilize the same cluster resources, reducing the need for separate clusters, deceasing software add-on licenses and lowering overall cloud costs by as much as 30%. Robust tools ensure isolation, compliance, and optimal performance in multi-tenant environments.

By utilizing GPU resources more efficiently with capabilities such as GPU virtualization and time-slicing, enterprises reduce the overall infrastructure cost of AI development, testing and serving in production.