Applied AI•10 min read•Oct 15, 2026
Building a RAG Pipeline Without the Enterprise Price Tag
You don't need $500/mo in LLM infrastructure to build great AI search. Here's the lean stack we use: pgvector, Supabase, and the OpenAI API.
Retrieval-Augmented Generation (RAG) is transforming how web apps query knowledge bases, document repositories, and proprietary internal data.
Enterprise AI vendors will try to sell you expensive hosted vector suites with five-figure annual contracts. You don't need that.
Our Lean RAG Stack:
• Database: Supabase with the pgvector extension enabled (included in free/pro tier).
• Embeddings: OpenAI text-embedding-3-small (extremely cheap, sub-cent pricing for thousands of documents).
• Pipeline: Chunking raw documents into 500-token blocks with metadata tags, storing vectors in a Postgres table.
• Querying: Cosine similarity search using simple SQL functions, passing top 3 matches to Claude 3.5 or GPT-4o.
Result: An AI assistant that knows your business domain inside and out, costing less than $20/month in total operating infrastructure.
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