Do I need a managed endpoint to serve a fine-tuned model?

Not always.

You do not inherently need a managed endpoint to serve a fine-tuned model, but whether you should use one depends on your deployment setup:

You can serve it without a managed endpoint if you:

• have your own infrastructure
• want to run the model on a VM, Kubernetes, or on-prem
• are comfortable handling:
  • model loading
  • scaling
  • authentication
  • monitoring
  • autoscaling
  • uptime/retries

A managed endpoint is useful if you want:

• simpler deployment
• automatic scaling
• built-in monitoring/logging
• easier auth and access control
• less ops overhead

In practice

• For experimentation or small internal use: a managed endpoint is often easiest.
• For production with custom infra needs: self-hosting is totally fine.
• For serverless / API-based hosting platforms: many platforms let you deploy a fine-tuned model behind a managed endpoint, but that’s a convenience, not a requirement.

If you tell me which platform you’re using (OpenAI, Azure, AWS SageMaker, Hugging Face, etc.), I can give the exact answer for that environment.