> For the complete documentation index, see [llms.txt](https://docs.kaisar.io/kaisar-network/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.kaisar.io/kaisar-network/kaisar-architecture/products/kaisar-cloud-end-users/kaisar-ray-cluster.md).

# Kaisar Ray Cluster

Kaisar Ray Cluster is a all-in-one solution that simplifies the management and scaling of [Ray](https://docs.ray.io/en/latest/ray-overview/index.html) clusters. A Ray Cluster is a distributed computing system designed for executing large-scale tasks such as AI and machine learning across multiple machines. 

Kaisar Ray cluster will deploy Ray Clusters on Kaisar DePIN network leveraging the combination of both. 

**Features and Benefits:** 

*  Kaisar Ray cluster Automates the setup, configuration, and maintenance of Ray clusters distributed across worker nodes on Kaisar DePIN.
*  Ray cluster automatically scales compute resources up or down based on real-time demand and predefined policies. 
* Ray Cluster includes built-in integration with Prometheus and Grafana, providing comprehensive visibility into cluster performance. 
* Ray cluster are designed with high availability in mind, ensuring that the cluster remains operational even in the event of hardware or software failures. 
* Ray Cluster offers customizable scaling strategies that can be tailored to meet specific workload requirements and performance targets. 

#### Technical Specifications

* Monitoring Tools: Integrated with Prometheus and Grafana.
* Scaling Policies: Flexible on Demand.