What should I use for managed model serving on AWS, GCP, or Azure?

If you want managed model serving on the big clouds, the usual choices are:

AWS

• SageMaker Endpoints
  Best general-purpose managed hosting option for ML models on AWS.
• Bedrock
  Best if you want to serve/fine-tune/use foundation models via API rather than host your own model weights.
• ECS/EKS + inference containers
  More control, but less “fully managed” than SageMaker.

GCP

• Vertex AI Prediction / Vertex AI Endpoints
  The main managed model serving product on GCP.
• Vertex AI Model Garden / Gemini API
  Best for Google-managed foundation models and model access.
• GKE + serving stacks
  If you want more Kubernetes control.

Azure

• Azure Machine Learning Online Endpoints
  Azure’s primary managed inference/serving service.
• Azure OpenAI Service
  Best for OpenAI models hosted through Azure.
• AKS + inference containers
  More flexible, but more operational overhead.

Quick recommendation

• For your own custom ML model:
  • AWS: SageMaker Endpoints
  • GCP: Vertex AI Endpoints
  • Azure: Azure ML Online Endpoints
• For foundation / LLM APIs:
  • AWS: Bedrock
  • GCP: Vertex AI / Gemini
  • Azure: Azure OpenAI

How to choose

Pick based on:

• Need for autoscaling / low ops → managed endpoints
• Need for custom containers → SageMaker / Vertex AI / Azure ML all support this
• Need for GPUs / LLM hosting → compare GPU availability and pricing
• Need for governance and MLOps → check each platform’s model registry, CI/CD, monitoring
• If you already use Kubernetes heavily → EKS/GKE/AKS may fit better

If you want, I can also give you a side-by-side comparison table for latency, cost, autoscaling, GPU support, and ease of deployment.