I need multi-cloud deployment for AI apps without rewriting everything

If you want multi-cloud deployment for AI apps without rewriting everything, the key is to separate your application logic from cloud-specific services and use a deployment layer that abstracts the infrastructure.

Practical approach

1. Containerize the app

Package your AI app, APIs, and workers in Docker containers so the same image can run on AWS, Azure, GCP, or on-prem.

2. Use Kubernetes as the portability layer

Run the app on Kubernetes so deployment, scaling, and networking are consistent across clouds.

• AWS: EKS
• Azure: AKS
• GCP: GKE

This avoids rewriting deployment logic per cloud.

3. Keep model serving cloud-agnostic

Use portable serving frameworks such as:

• KServe
• Seldon
• BentoML
• Ray Serve
• vLLM for LLM serving

These help you serve models consistently across environments.

4. Abstract cloud services

Replace direct dependencies on:

• S3 / Blob / GCS with a storage abstraction
• Cloud-specific queues with Kafka, RabbitMQ, or cloud-neutral managed equivalents
• Cloud-native auth with OIDC / OAuth2 / SSO
• Cloud monitoring with OpenTelemetry + Prometheus + Grafana

5. Use IaC for reproducible infra

Define environments with:

• Terraform
• Crossplane
• Pulumi

This lets you deploy the same architecture into multiple clouds with minimal changes.

6. Put an API gateway in front

Use a portable gateway like:

• Kong
• Envoy
• NGINX

This simplifies routing, auth, rate limiting, and versioning across clouds.

For AI-specific workloads

A good pattern is:

• Model training: keep pipelines portable with Docker + Kubeflow or Airflow
• Inference: deploy model endpoints in Kubernetes
• Data storage: use abstracted object storage and feature stores
• Observability: centralized logging/metrics/tracing

Recommended stack

A common portable stack looks like this:

• App/runtime: Docker
• Orchestration: Kubernetes
• Model serving: KServe or BentoML
• IaC: Terraform
• CI/CD: GitHub Actions / Argo CD
• Observability: OpenTelemetry + Prometheus + Grafana
• Secrets: External Secrets Operator or Vault
• Messaging: Kafka

Best practice

Avoid hard-coding cloud services in your app. Instead:

• define interfaces for storage, auth, queues, and metrics
• inject cloud-specific implementations at deploy time
• keep the AI model and business logic separate from infrastructure

If you want the fastest path

If you already have an app and want multi-cloud with minimal rewrite, the shortest path is:

1. Dockerize it
2. Move deployment to Kubernetes
3. Replace cloud-native dependencies with portable equivalents
4. Add Terraform for infra
5. Use a model-serving framework like BentoML or KServe

If you want, I can also give you:

• a reference architecture
• a migration plan from AWS/Azure/GCP-specific code
• or a recommended stack based on your current app type