Managing Kubernetes Clusters in Nexastack
This guide provides a step-by-step walkthrough for adding and onboarding a new Kubernetes cluster in the Nexastack platform. Following these steps ensures your cluster is configured correctly and ready for deploying AI models efficiently and securely.
What is a Kubernetes Cluster?
A Kubernetes cluster is a collection of nodes (servers) that work together to run containerized applications. Each cluster typically has:
- Master Node(s): Responsible for managing the cluster, scheduling workloads, and maintaining cluster state.
- Worker Nodes: Run the applications (pods) and handle computational tasks.
- Networking Components: Manage communication between nodes and services.
- Storage Resources: Persistent volumes for applications that require data storage.
On Nexastack, clusters provide the underlying infrastructure required to:
- Deploy and scale AI models reliably.
- Monitor resource usage and application performance.
- Ensure high availability and fault tolerance for workloads.
- Provide a controlled environment for multiple teams to collaborate on shared projects.
Kubernetes clusters make it easier to orchestrate AI model deployments across multiple nodes, ensuring that applications remain scalable, resilient, and easy to manage.
Why Onboard a Cluster?
Onboarding a cluster in Nexastack allows your team to:
- Deploy AI Models at Scale: Run multiple models or large workloads without overloading a single server.
- Monitor Resources Efficiently: Use Prometheus and other metrics to track CPU, memory, and cluster health.
- Secure and Manage Access: Configure ingress, authentication, and roles to control who can deploy and manage workloads.
- Collaborate Across Teams: Enable multiple teams to use the same infrastructure without conflicts.
- Automate Management Tasks: Reduce manual maintenance by leveraging Kubernetes’ automated scheduling, scaling, and updates.
Additionally, onboarding clusters ensures that your AI applications are production-ready, highly available, and can easily integrate with other Nexastack platform services such as data pipelines, monitoring dashboards, and model evaluation tools.
Clusters provide a centralized, scalable, and secure environment for managing AI workloads, giving teams flexibility and control while maintaining operational efficiency.
Types of Clusters Supported by Nexastack
Nexastack supports several cluster types, allowing teams to choose the one that fits their infrastructure and operational requirements:
- On-Premises Clusters: Hosted on your organization’s infrastructure. You have full control over the hardware, network, and security settings. Ideal for teams that require complete autonomy.
- Managed by Nexastack: Fully managed clusters where Nexastack handles setup, maintenance, scaling, and monitoring. Best for teams that want a turnkey solution without operational overhead.
- Cloud Clusters: Hosted by cloud providers for easy scalability and flexibility. Useful for teams that need rapid provisioning and elastic resources.
Select a cluster type based on your team’s needs:
- On-Premises: Maximum control and compliance.
- Managed by Nexastack: Minimal operational effort.
- Cloud: High scalability and flexibility.