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Kubernetes Service


The Kubernetes Service provided by Ori is akin to other Serverless Kubernetes services but comes with enhanced capabilities and much fewer restrictions. It offers a pay-per-use model, making it an economical choice for businesses of all sizes.

Key Features

  • GPU Support: Integrated GPU resources make it ideal for compute-intensive applications, particularly in ML and AI.
  • Serverless Managed Cluster: The Kubernetes environment is fully managed by Ori, removing the overhead of managing clusters and node resources.
  • Kubectl Access: Users have access to Kubectl, allowing them to schedule pods in their own namespaces within a serverless Kubernetes environment.
  • Familiar Experience: Offers a user experience similar to traditional Kubernetes, providing familiarity for those accustomed to Kubectl.
  • Cost-Efficient: Pay-per-use model ensures users only pay for the resources they consume, optimizing cost efficiency.
  • Scalability: Dynamically scales based on workload demands, ensuring optimal resource utilization.

Currently, we are offering the following GPU types:

  • NVIDIA H100: ideal for inference/training of large models and best performance
  • NVIDIA L4: great for graphics and for machine learning tasks such as training and inference at a great price point
  • NVIDIA L40S: designed for high-performance computing, AI workloads, and advanced graphics rendering in DC and Enterprise environments.

Use Cases

  • Ideal for applications that require rapid scaling.
  • Suitable for ML/AI workloads that need GPU resources.
  • Perfect for developers who prefer a Kubernetes environment without the complexity of cluster management.


Ori Kubernetes Services offer powerful solutions for different Kubernetes use cases, whether it’s the ease and efficiency of a Serverless Kubernetes environment or the tailored control of GPU-enhanced Kubernetes clusters. Users can choose the service that best fits their operational needs, ensuring they have the right tools to deploy, manage, and scale their applications effectively in a Kubernetes environment.