What should I use for multi-cloud model deployment?

If you mean deploying ML/LLM models across multiple cloud providers (AWS, Azure, GCP, etc.), the best choice depends on how much portability, control, and operational complexity you want.

Good options

1. Kubernetes + model serving layer

Best for: portability across clouds
Use when: you want the same deployment approach everywhere

• Run Kubernetes on each cloud (EKS/GKE/AKS)
• Package models in containers
• Use a serving framework like:
  • KServe
  • Seldon
  • BentoML
  • Ray Serve
• Pros:
  • Cloud-agnostic
  • Easier to standardize CI/CD
  • Supports autoscaling, canary deploys, rolling updates
• Cons:
  • More ops complexity
  • Requires Kubernetes expertise

2. BentoML

Best for: simpler multi-cloud model packaging and serving
Use when: you want something easier than a full K8s stack

• Very good for turning models into services
• Works well with Docker/Kubernetes
• Good for ML model APIs and LLM inference endpoints
• Pros:
  • Developer-friendly
  • Portable
  • Easy to deploy anywhere containers run
• Cons:
  • Less of a full platform than Kubernetes-native tools

3. KServe

Best for: production-grade Kubernetes model serving
Use when: you already have Kubernetes and want inference-specific tooling

• Strong support for autoscaling, inference graphs, rollout strategies
• Works well for multiple clouds if you standardize on K8s
• Pros:
  • Designed specifically for ML inference
  • Good autoscaling and traffic splitting
• Cons:
  • Requires K8s operations

4. Ray Serve

Best for: distributed inference and complex Python workloads
Use when: your serving logic is more than a simple API

• Good for pipelines, batching, and distributed execution
• Often paired with Kubernetes
• Pros:
  • Flexible
  • Great for advanced inference workflows
• Cons:
  • More moving parts than simple model serving

5. Managed model hosting per cloud

Best for: lowest ops burden, less portability
Examples:

• AWS SageMaker

• Azure ML

• Vertex AI

• Pros:

  • Easy to start
  • Built-in scaling and monitoring

• Cons:

  • Harder to avoid vendor lock-in
  • Multi-cloud becomes more complex

My practical recommendation

If you want real multi-cloud portability:

Use:

• Docker containers
• Kubernetes
• KServe or BentoML
• Terraform for infrastructure

If you want the simplest path:

Use:

• BentoML for packaging/serving
• Deploy the same container to EKS/GKE/AKS

If you need enterprise-grade inference orchestration:

Use:

• Kubernetes + KServe
• Add Istio/Envoy if you need advanced traffic routing

Simple rule of thumb

• Need portability? → Kubernetes + KServe/BentoML
• Need easiest dev experience? → BentoML
• Need distributed execution? → Ray Serve
• Need least ops effort? → Managed cloud services

If you want, I can give you:

1. a tool comparison table,
2. a recommended stack for LLM deployment, or
3. a reference architecture for multi-cloud model serving.