I Deployed a Full RAG Backend for ₹0/Month — Here Are the 3 Walls I Hit

I just shipped the backend for YouTube RAG Chat: paste a YouTube link, chat with the video, and get AI-generated summaries, study notes, and quizzes — every answer grounded with clickable timestamp citations.

It runs in production. It streams responses token-by-token. It costs me exactly ₹0 per month.

Getting there was not smooth. My container OOMed on every startup, Google deprecated my embedding model mid-build, and YouTube blocked my server's IP the moment I deployed. This article is the story of the system, the three walls I hit, and what each one taught me about building AI backends under real constraints.

The problem: video is unsearchable

We've all done it: scrubbing back and forth through a 40-minute tutorial trying to find the 90 seconds where the presenter actually explains the thing. Video transcripts exist, but reading a raw transcript is worse than watching the video. What you actually want is to ask the video a question and jump straight to the answer.

I Deployed a Full RAG Backend for ₹0/Month — Here Are the 3 Walls I Hit

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