Building a RAG app on a free-tier server: the memory-budget playbook

A free-tier hosting instance typically gives you 512 MB of memory. That number sounds tiny until you realise most of the "expensive" parts of an AI app are optional, not fundamental.

The biggest single cost: local embeddings

Loading `torch` plus `sentence-transformers` for local embedding generation alone needs roughly 500 MB — before your application code runs at all. Switching to a hosted embedding API removes that entire cost, at the price of a network call per embedding.

What actually fits

FAISS as a local vector store, a lightweight web framework, BM25 for keyword search, and hosted calls for embeddings and generation — that combination is how RAG.NextUpgrad runs in about 220 MB.

When to actually pay for compute

Once you need local embeddings for latency or cost-at-scale reasons, or you're running your own reranker model instead of a hosted one. Until then, the free tier teaches you more about your dependencies than a rented GPU ever will.