Developers Can Build Zero-Cost Semantic Search Without LLMs Using FastAPI and Qdrant

A technical guide published on DEV Community outlines how to build a production-grade semantic search engine without relying on large language models or paid AI APIs. The proposed architecture uses FastAPI, Qdrant in-memory vector storage, and Hugging Face's free serverless inference API to generate 384-dimensional text embeddings. Unlike keyword-based search, the semantic approach converts queries into vectors, allowing it to match conceptually similar content even when exact words differ. The entire stack runs on a serverless, Jamstack infrastructure hosted on GitHub Pages, designed to cost nothing at scale and return results in single-digit milliseconds. The guide argues that for the majority of real-world search use cases, direct context-aware document retrieval is more practical and efficient than generative AI responses.