A developer built the same RAG system twice: first in FastAPI over weeks, then in Ruby on Rails during a 48-hour hackathon. The core AI logic—using OpenAI embeddings, pgvector, and GPT-4—was identical in both. The stark difference was development speed, with Rails taking just 24 hours to ship a working demo.

The author attributes Rails' velocity to mature tooling like Sidekiq for background jobs and ActiveRecord for database management. These handled retries, session management, and deployment configuration automatically, letting him focus on features rather than infrastructure glue. FastAPI required manual setup for async workers and database sessions, consuming time on configuration.

This hands-on comparison suggests that for product engineers, a mature framework like Rails can accelerate AI feature integration without a full rewrite. Python remains ideal for ML experimentation, but a hybrid approach—using Python microservices only for specialized ML tasks—may be optimal for existing Rails applications.