Building an autonomous AI agent is easy. Keeping it alive 24/7 for four months on a ₹0 budget is the real engineering problem

.My first version taught me that the hard way. One LLM, no fallback. Every rate limit or outage froze the entire pipeline. Image generation was worse: I went through several free tiers before Cloudflare, and Hugging Face's free inference simply wasn't dependable enough for a system that never sleeps.

So I rebuilt it as XeL Studio, an open-source, self-driving newsroom. Every 30 minutes it finds tech news, writes the article, generates a cover image, and publishes. Once a day it cleans its own database. No human in the loop.

Here's how I designed around free-tier limits instead of fighting them:

Two LLMs, one pipeline Groq and Cerebras run behind a fallback router. If one hits a rate limit or goes down, the other takes over. That single change fixed most of v1's failures.

Vercel for the frontend, GitHub Actions for the heavy lifting The Next.js + TypeScript frontend is hosted on Vercel. But serverless timeouts kill long jobs, so AI generation and Python scraping run on GitHub Actions as a scheduled backend.

Discovery via Searchwala News discovery runs on Searchwala, another open-source project of mine.

Media at ₹0 Cloudflare generates the cover images. Cloudinary stores and delivers them.

Honestly, this is my favourite part of building: taking free tiers that are weak on their own and combining them into something that holds up in production.

Both XeL Studio and Searchwala are open-source. Links in the first comment.

So, would you trust a free-tier stack like this in production, or is it a ticking time bomb?

Website link: https://xel-studio.vercel.app/ GitHub link: https://github.com/SandeepAi369/xel-studio