[Python] Best Practice for Seeding Data in FastAPI Integration Tests: API Endpoint vs. Direct...

I am writing integration tests for my Python web application built with FastAPI and SQLAlchemy, and I need to decide on the best pattern for setting up the database state before my tests run.

For example, to test an endpoint that retrieves a "DAO" (GET /api/v1/daos/config/{slug}), I first need to ensure a specific DAO exists in the test database. I've considered two main approaches, and I'm unsure which is more maintainable and robust in the long run.

1. 
   Seeding Data via a Dedicated Test API Endpoint (My current method)
   
   In this pattern, I create a special, non-production API endpoint (e.g., POST /_dev/daos) whose only job is to set up test data. My test helper functions call this endpoint.
   
   
2. 
   Seeding Data by Direct Database Insertion
   
   In this pattern, the test setup connects directly to the test database (using a pytest fixture) and inserts the data.
   

Given these two distinct patterns, what is the recommended approach for integration testing in a modern Python/FastAPI application? Is the speed and simplicity of direct DB access worth the risk of bypassing application logic? Or is the end-to-end assurance of the API seeding method the superior, more robust pattern, despite its slowness?

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