How do I host a model in my own cloud account instead of a vendor account?

To host a model in your own cloud account instead of a vendor-managed account, you generally want to bring the model runtime to your infrastructure rather than calling a third-party hosted endpoint.

Common approaches

1. Self-host an open-weight model

If you can use a model whose weights you’re allowed to run yourself:

• Pick a model: Llama, Mistral, Qwen, Gemma, etc.
• Provision compute in your cloud account:
  • AWS: EC2 / EKS / SageMaker
  • GCP: Compute Engine / GKE / Vertex AI custom deployment
  • Azure: VM / AKS / Azure ML
• Run an inference server:
  • vLLM
  • Hugging Face TGI (Text Generation Inference)
  • TensorRT-LLM
  • Ollama for simpler setups
• Put it behind your own API gateway / load balancer
• Secure it with IAM, VPC, auth, logging, and autoscaling

This gives you full control over networking, data, and costs.

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2. Bring your own model to a managed platform in your account

If you want less ops work, use a managed service but deploy it inside your own account:

• AWS SageMaker endpoints in your AWS account
• Vertex AI custom prediction in your GCP project
• Azure ML Online Endpoints in your Azure subscription

You still control the account, permissions, and billing, but the platform handles more of the orchestration.

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3. Run a model container on Kubernetes

If you already have Kubernetes:

• Package the model server in a container
• Deploy it to EKS/GKE/AKS
• Store model weights in object storage (S3/GCS/Blob)
• Mount or download on startup
• Expose via internal service or ingress

This is a strong option if you want portability and control.

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High-level steps

1. Choose a model that allows self-hosting.
2. Estimate hardware needs:
   • Small models: CPU or single GPU
   • Larger models: one or more GPUs, high memory
3. Select deployment target in your cloud account.
4. Containerize the inference server.
5. Store weights securely in your own object storage.
6. Set up networking:
   • private subnets if internal-only
   • load balancer / API gateway if external
7. Add auth and observability:
   • IAM, OAuth, JWT, mTLS
   • logs, metrics, tracing
8. Scale and autosize based on latency and throughput.

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Example architectures

Simple

Client → API Gateway → VM running vLLM → model weights in S3

More production-ready

Client → Load Balancer → Kubernetes service → GPU inference pods → S3/GCS/Blob storage

Managed in your account

Client → SageMaker endpoint in your AWS account → model container + weights in your S3 bucket

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Important considerations

• Licensing: some models are not allowed for commercial self-hosting or redistribution.
• GPU availability: not all cloud regions have the GPUs you want.
• Latency: place the model close to users.
• Cost: GPU instances can be expensive when idle.
• Security: ensure prompts, outputs, and fine-tuning data are handled according to your policies.
• Scaling: use autoscaling or serverless options where possible.

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If you tell me your cloud provider

I can give you a concrete setup for:

• AWS
• GCP
• Azure
• Kubernetes
• Docker on a single VM

If you want, I can also provide a step-by-step deployment example for one of these.