𝗛𝗼𝘄 𝗜 𝗕𝘂𝗶𝗹𝘁 𝗮 𝗠𝘂𝗹𝘁𝗶𝗹𝗶𝗻𝗴𝘂𝗮𝗹 𝗥𝗔𝗚 𝗦𝘆𝘀𝘁𝗲𝗺 𝗳𝗼𝗿 𝗨𝗻𝗱𝗲𝗿 $𝟬.𝟬𝟬𝟬𝟱 𝗽𝗲𝗿 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻

Most RAG stacks rely on paid embeddings, paid vector DBs, and GPT‑4 for everything. It works, but it’s expensive and over‑engineered for many real‑world use cases. I wanted something different: a production‑grade multilingual RAG system that costs pennies to run. Here’s the architecture. 𝗦𝗲𝗹𝗳‑𝗵𝗼𝘀𝘁𝗲𝗱 𝗺𝘂𝗹𝘁𝗶𝗹𝗶𝗻𝗴𝘂𝗮𝗹 𝗲𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴𝘀 (𝗰𝗼𝘀𝘁: $𝟬 𝗽𝗲𝗿 𝗾𝘂𝗲𝗿𝘆 - 𝗻𝗼 𝗽𝗲𝗿‑𝘂𝘀𝗮𝗴𝗲 𝗰𝗵𝗮𝗿𝗴𝗲𝘀) A sentence‑transformers model running locally: 50-100ms per embedding Zero marginal cost Smaller vectors → faster search Works offline Caching eliminates repeated work This removes all embedding‑related spend.