Glossary · AI & Agents
Qdrant
Overview
What Qdrant Is and How It Works
Qdrant is an open-source vector database written in Rust that stores data as high-dimensional vectors, or embeddings, alongside structured metadata called payloads. It uses an HNSW index to find the most similar vectors to a query in milliseconds, even across millions of records. You can run it self-hosted with Docker or use Qdrant Cloud, and connect through REST or gRPC APIs.
Why It Matters for RAG and Search
Large language models do not know your private data. Qdrant bridges that gap: you convert documents into embeddings, store them, then retrieve the most relevant chunks to feed a model at query time. This is the retrieval step in retrieval-augmented generation. Payload filtering lets you combine semantic similarity with exact conditions, like tenant, date, or category, so results stay accurate and scoped.
How We Use Qdrant in Client Projects
When we build knowledge assistants and RAG systems, Qdrant is a common choice for the vector store because it self-hosts cleanly, scales, and filters on metadata. Our team pairs it with document processing and n8n automation to keep the index fresh as content changes. As a full-service, in-house team in Houston, we handle embeddings, retrieval tuning, and the surrounding app so the assistant answers from trusted sources.
Where we use it
Related Zen in Tech services
How our team puts Qdrant to work in real projects.
FAQ
Qdrant — common questions
Is Qdrant free?
Qdrant is free and open-source under the Apache 2.0 license, so you can self-host it at no license cost. Qdrant Cloud, the managed option, is paid and priced by cluster size, with a free tier for testing.
Qdrant vs Pinecone: what's the difference?
Qdrant is open-source and can be self-hosted for full data control, while Pinecone is a fully managed, closed-source service. Both do fast vector search; Qdrant suits teams that want to own their infrastructure or avoid vendor lock-in.
Do I need a vector database like Qdrant?
If you are building semantic search, recommendations, or a RAG chatbot over your own content, a vector database like Qdrant makes retrieval fast and scalable. For a few hundred documents, simpler options may suffice, but they rarely scale.
Need Qdrant done right?
Book a free consultation and we’ll map the fastest, most cost-effective path for your project.
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