Kubernetes Service
Overview
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.
Conclusion
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.