What is the purpose of Qdrant?

Qdrant exists to make similarity search over large collections of vector embeddings both fast and practical in production, specifically by treating metadata filtering as a first-class part of the search itself rather than a separate step applied before or after.

Many applications generating embeddings — from text, images, or user behavior — also need to narrow results by structured attributes at the same time: "find documents similar to this one, but only from this tenant" or "find similar products, but only ones currently in stock." Qdrant's purpose is answering that combined query efficiently, rather than forcing an application to run a broad vector search and filter the results afterward in application code.

It's designed specifically for the vector search workload rather than as a general-purpose database: it intentionally omits features like full SQL support or generic document modeling in favor of optimizing the storage layout, indexing, and query execution path specifically for high-dimensional similarity search at scale.