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.

𝗽𝗴𝘃𝗲𝗰𝘁𝗼𝗿 𝗶𝗻𝘀𝘁𝗲𝗮𝗱 𝗼𝗳 𝗣𝗶𝗻𝗲𝗰𝗼𝗻𝗲/𝗤𝗱𝗿𝗮𝗻𝘁 (𝗰𝗼𝘀𝘁: $𝟬)
Everything lives inside Postgres:

• vectors
• metadata
• filtering
• HNSW similarity search

For <100K vectors, queries land in 50–200ms.
One database → one deployment → no sync issues.

𝗚𝗣𝗧‑𝟰𝗼‑𝗺𝗶𝗻𝗶 𝗳𝗼𝗿 𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 (~$𝟬.𝟬𝟬𝟬𝟮𝟳–$𝟬.𝟬𝟬𝟬𝟰𝟱)
Optimized RAG queries typically use:

• ~600 input tokens
• ~300 output tokens

At GPT‑4o‑mini pricing, that’s roughly:
~$0.00027 per question.
Even with larger contexts (1000 input + 500 output), cost stays under:
~$0.0005 per question.

𝗔 𝗺𝗶𝗻𝗶𝗺𝗮𝗹, 𝗰𝗼𝘀𝘁‑𝗲𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝘁 𝗱𝗲𝘀𝗶𝗴𝗻

• FastAPI backend
• Postgres + pgvector
• Lightweight cache
• Static frontend

No GPUs.
No microservices.
No expensive managed services.

𝗪𝗵𝘆 𝗶𝘁 𝘄𝗼𝗿𝗸𝘀

• One unified system → fewer failure modes
• Caching turns repeated queries into 100ms lookups
• Multilingual embeddings remove translation cost + latency
• GPT‑4o‑mini is the perfect balance of speed, quality, and cost

𝗧𝗵𝗲 𝗯𝗼𝘁𝘁𝗼𝗺 𝗹𝗶𝗻𝗲
You don’t need Pinecone, GPT‑4, GPUs, or a complex microservices architecture.

You need:
pgvector + sentence‑transformers + GPT‑4o‑mini + a caching layer.

That’s how you get to ~$0.0003 per question.

𝗪𝗿𝗶𝘁𝘁𝗲𝗻 𝗯𝘆 𝗥𝗶𝗰𝗮𝗿𝗱𝗼 𝗦𝗮𝘂𝗺𝗲𝘁𝗵
𝗦𝗲𝗻𝗶𝗼𝗿 𝗙𝗿𝗼𝗻𝘁‑𝗘𝗻𝗱 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿 | 𝗥𝗲𝗮𝗹‑𝗧𝗶𝗺𝗲 𝗨𝗜 𝗦𝗽𝗲𝗰𝗶𝗮𝗹𝗶𝘀𝘁