Managed Kubernetes

Lightning AI supports fully managed Kubernetes clusters, so you can keep running your existing workloads - while gaining visibility, reliability, and control designed for AI/ML teams.

Whether you're running model inference servers, batch jobs, or custom microservices, Lightning AI gives you the Kubernetes foundation you’re used to - with built-in support for team-scale collaboration and modern AI workflows.

Why Kubernetes?

Kubernetes is widely used across the industry for container orchestration and is a natural choice for:

• Running model inference as microservices
• Deploying REST APIs and web applications
• Managing autoscaling, service discovery, and fault tolerance

If your team already has a Kubernetes-based workflow, Lightning AI ensures you can continue using it without changes - and without managing the underlying infrastructure.

Our opensouce works with K8s

All Lightning AI open-source tools can be containerized and deployed to Kubernetes. You can run training, inference, or hybrid workloads as custom Kubernetes jobs and services.

Lightning AI also supports native integration with tools like:

• Helm charts
• Argo Workflows
• KubeFlow
• Any custom Kubernetes operators

You’re not locked in - and you don’t have to rebuild anything.

Lightning AI enhances Kubernetes

Lightning AI makes Kubernetes easy to work with for teams and real use-cases:

• Managed GPU infrastructure with full autoscaling and node health monitoring
• Observability across pods, nodes, and services (UI + APIs)
• Team management (RBAC, audit logs, usage tracking)
• Integrated storage and secrets management
• Support for hybrid SLURM + Kubernetes deployments

Same workflow

Use the same workflow and tools you're used to

kubectl apply -f inference-deployment.yaml