Do I need a model deployment platform or can I just run FastAPI?

You can absolutely just run FastAPI — a model deployment platform is optional, not required.

Use FastAPI if:

• You want a simple API around your model
• Traffic is low to moderate
• You’re comfortable handling:
  • model loading
  • request/response validation
  • batching (if needed)
  • logging/monitoring
  • scaling
  • uptime/restarts

A common setup is:

• FastAPI for the API layer
• Uvicorn/Gunicorn for serving
• Docker for packaging
• A cloud VM or container service to run it

Use a model deployment platform if:

• You want autoscaling
• You need GPU management
• You care about rollouts / versioning / canary deploys
• You need monitoring, retries, health checks, observability
• You want to deploy models with less ops work

Examples:

• SageMaker
• Vertex AI
• Azure ML
• Kubernetes-based platforms
• BentoML / KServe / Ray Serve / Modal, etc.

Rule of thumb

• Prototype / MVP / low traffic → FastAPI is enough
• Production at scale or lots of operational needs → platform helps a lot

One important caveat

If you’re serving a model directly from FastAPI, make sure you:

• load the model once at startup, not per request
• avoid blocking the event loop with heavy inference
• consider using a worker process model if inference is CPU-heavy
• add timeouts, logging, and health checks

If you want, I can sketch a minimal FastAPI model-serving template or help you decide based on your expected traffic and model size.