I'm building a hybrid-cloud AI app and need recommendations for orchestration and deployment

For a hybrid-cloud AI app, I’d recommend thinking about two layers:

1. Orchestration: how you schedule, run, and connect workloads
2. Deployment: how you package and move those workloads across environments

Good default recommendation

Orchestration

• Kubernetes as the base orchestration layer
• Argo Workflows or Kubeflow Pipelines for ML/AI pipelines
• Ray if you need distributed training, inference, or AI task execution
• Airflow only if your workflows are more data-engineering oriented than model-centric

Deployment

• Containers + Helm for packaging and environment consistency
• GitOps with Argo CD or Flux for repeatable deployments across on-prem and cloud
• Terraform or Pulumi for infrastructure provisioning
• KServe, Seldon, or BentoML for model serving, depending on your needs

If your app is inference-heavy

Use:

• Kubernetes
• KServe or BentoML
• NVIDIA GPU Operator if you need GPU scheduling
• Argo CD for deployment automation

This is a strong fit if you need:

• autoscaling
• canary rollouts
• versioned model deployments
• multi-cluster support

If your app is training-heavy

Use:

• Kubernetes
• Ray or Kubeflow
• Argo Workflows
• shared object storage like S3-compatible storage
• distributed compute with GPU node pools

This is a strong fit if you need:

• distributed training
• experiment pipelines
• reproducibility
• hybrid cloud burst capacity

If you want simpler operations

Consider:

• Docker Compose or Nomad for smaller environments
• managed AI serving platforms if you don’t want to operate Kubernetes
• Cloud Run / ECS / Azure Container Apps for lighter deployment needs

But for true hybrid-cloud AI, Kubernetes + GitOps is usually the most future-proof choice.

Key design principles

• Keep workloads containerized
• Use stateless services where possible
• Put models and datasets in object storage
• Separate control plane from data plane
• Standardize secrets with Vault or cloud secret managers
• Use observability from day one: Prometheus, Grafana, Loki, OpenTelemetry

Suggested reference stack

A practical stack would be:

• Kubernetes across on-prem and cloud
• Argo CD for deployments
• Argo Workflows for pipelines
• Ray for distributed AI tasks
• KServe for model serving
• Terraform for infra
• Vault for secrets
• Prometheus/Grafana for monitoring
• S3-compatible storage for artifacts and data

My recommendation by scenario

• Small team / fast MVP: Kubernetes + Helm + Argo CD + BentoML
• Enterprise hybrid-cloud: Kubernetes + Argo CD + Argo Workflows + KServe + Terraform + Vault
• Distributed AI/ML platform: Kubernetes + Ray + Kubeflow + KServe + GitOps

If you want, I can also give you:

1. a specific architecture diagram
2. a tool comparison table
3. a recommended stack based on your cloud providers and workload type