Key takeaways
- Qdrant is a fast, open-source vector database with strong filtering, hybrid search and quantisation.
- You can self-host it with Docker or Kubernetes, or run it locally, free under the Apache 2.0 licence.
- Qdrant uses its own HNSW implementation, not FAISS, and newer versions can build indexes on GPUs.
- It is written in Rust for speed, memory safety and predictable performance without garbage collection.
- Postgres with pgvector suits smaller, relational workloads; Qdrant suits large, vector-heavy ones.
Yes, you can self-host Qdrant: it is an open-source vector database under the Apache 2.0 licence, running from a single Docker image on your laptop, a server or Kubernetes, free for commercial use. Teams choose it for fast similarity search with rich filtering, hybrid search and memory-saving quantisation. I build hybrid retrieval for RAG systems such as RAG.NextUpgrad, so this page covers why to use Qdrant, self-hosting and local use, licensing, GPU, HNSW and FAISS questions, why it is written in Rust, using it in n8n, and how it compares with Postgres.
Why use Qdrant?
Use Qdrant when vector search is central to your application and you want speed, accurate filtering and efficient memory use. It applies payload filters during graph search, so filtered queries stay accurate and fast, a common weakness elsewhere.
Other strengths: sparse vectors for keyword-style hybrid search, multiple named vectors per record, multi-vector representations, scalar, product and binary quantisation, snapshots, distributed mode with sharding and replication, and clear REST and gRPC APIs. It runs as a single, lightweight service, which makes self-hosting straightforward. It is a strong fit for RAG, semantic search, recommendations and multi-tenant SaaS products. Its documentation and benchmarks are open, so you can verify performance claims on your own data before committing.
Can I self-host Qdrant?
Yes. Run the official Docker image on any server, deploy with Qdrant's Helm chart on Kubernetes, or use the release binaries. Self-hosting gives you full control over data location, security and costs.
Also asked as: can i self host qdrant
Self-hosting checklist:
- Mount persistent storage for
/qdrant/storage. - Enable API key authentication and TLS before exposing it.
- Size memory for your vectors, or use on-disk storage with quantisation.
- Schedule snapshots and copy them off the server.
- Monitor with Prometheus metrics and health checks.
- Use distributed mode with replicas for high availability.
Qdrant also offers a managed cloud and a hybrid option that runs in your own infrastructure under its management.
Can I use Qdrant locally?
Yes, in two ways: run the Docker container on your machine and connect to it on localhost, or use the Python client's local mode, which runs an embedded, in-memory or on-disk version without any server, ideal for notebooks, tests and prototypes.
from qdrant_client import QdrantClient
client = QdrantClient(path="./qdrant_local") # or QdrantClient(":memory:")
Local mode supports the same API for basic operations, so you can develop locally and switch to a server by changing the connection. For larger local experiments, Docker gives the full feature set and the web dashboard for browsing collections. Keep the local storage folder out of git, since it can grow large.
Is Qdrant free for commercial use?
Yes. Qdrant's open-source database is released under the Apache 2.0 licence, which allows commercial use, modification and distribution, as long as you include the licence and notices. Self-hosting has no licence fees.
Costs come from infrastructure and operations: servers, storage, backups and engineering time. Qdrant Cloud and enterprise offerings are paid services for teams that prefer managed hosting or need support. Client libraries are also open source. As always, check the current licence in the repository before shipping, and review any third-party embedding models' licences separately.
Does Qdrant use the GPU?
Queries run on CPUs, which are fast for vector search, but newer Qdrant versions can use GPUs to accelerate building HNSW indexes, which helps when indexing very large collections quickly. GPU support uses a Vulkan-based implementation working across vendors.
Also asked as: does qdrant use gpu
For most applications, CPU-based Qdrant is sufficient; index building is a one-time or occasional cost, and queries are efficient. GPU acceleration matters when you ingest millions of vectors frequently. Embedding generation, usually the heavier compute task, happens outside Qdrant, in your embedding model, which is where GPUs typically help most in RAG pipelines.
Does Qdrant use HNSW?
Yes. Qdrant uses its own implementation of HNSW, Hierarchical Navigable Small World graphs, for approximate nearest-neighbour search, with modifications that support filtering during search and efficient updates.
You can tune HNSW parameters such as m, the number of links per node, and ef_construct for index quality, and set hnsw_ef at query time to balance recall and speed. Qdrant also builds payload indexes for filter fields and can create extra links that keep filtered searches accurate. For small collections, it may use exact search, which is fast enough and perfectly accurate.
Does Qdrant use FAISS?
No. Qdrant does not use FAISS; it implements its own indexing and storage in Rust, including HNSW, quantisation and filtering. FAISS is a separate C++ library from Meta for vector search within an application.
The difference matters: FAISS gives you fast in-memory search but leaves persistence, updates, filtering, APIs and scaling to you, while Qdrant provides a complete database service. Some benchmarks compare them, but they serve different roles. Use FAISS for research and embedded offline search; use Qdrant when you need a production service with updates, filters and multiple clients. Both use similar ideas, such as graph-based indexes and quantisation, so concepts learned in one transfer to the other.
Why is Qdrant written in Rust?
Rust offers performance comparable to C and C++, memory safety without a garbage collector, and safe concurrency, so Qdrant can be fast and reliable with predictable latency and no garbage collection pauses during heavy query loads.
For a database, these properties matter: memory bugs can corrupt data or create security vulnerabilities, and pauses can cause latency spikes. Rust's compiler catches many such bugs before release. Its modern tooling and ecosystem also help a small team build complex systems. Several other modern infrastructure projects chose Rust for similar reasons. For users, the practical result is a single, efficient binary with modest memory overhead.
How do I use Qdrant in n8n?
n8n includes a Qdrant Vector Store node that inserts documents with embeddings and retrieves similar ones, usable inside AI workflows such as RAG chatbots and AI agents. Connect it with your Qdrant URL and API key as credentials.
A typical RAG workflow in n8n: a trigger receives documents, a text splitter chunks them, an embeddings node creates vectors, and the Qdrant node stores them. A second workflow receives questions, retrieves relevant chunks through the Qdrant node, often as a tool for an AI Agent node, and generates answers. Self-hosted n8n and self-hosted Qdrant together keep data on your own infrastructure.
Postgres vs Qdrant: which vector database should I use?
Use Postgres with pgvector when your data is relational, your vector collection is small to medium, and you want one database with transactions, joins and row-level security. Use Qdrant when vectors dominate, collections are large, filtering is heavy and you need specialised performance features.
Also asked as: why rust? (qdrant vector database · how to use qdrant vector database in n8n · postgres vs. qdrant vector database comparison
Postgres keeps operations simple if you already run it, and full-text search enables hybrid retrieval in one place. Qdrant offers more vector-specific features, such as built-in quantisation, sparse vectors and filtered HNSW, and scales vector workloads more easily. Many teams start with pgvector and move vectors to Qdrant when scale or latency demands it, keeping metadata in Postgres.
Sources
GenAI engineer and AI product builder with 2+ years shipping production-grade AI systems: RAG pipelines, fine-tuned LLMs, hybrid retrieval and multi-modal apps across vision, speech and OCR, architected end to end from ingestion to deployment. Leads engineering for SCULT INDIA's 14-member team, founded the 500+ member TechVerse Enclave community and has mentored 200+ students. Three hackathon first prizes: Changethon 2025 (IIT Roorkee), Product Genesis at Vividhotsava 2025 (CSJMU) and BYTEBATTLE (MeetKats).
Open to GenAI roles, hackathon judging, mentorship sessions and guest talks at colleges. For campus sessions the inviting institution covers the visit end to end: travel (a First AC train ticket for journeys up to five hours, a flight for anything longer), accommodation, meals, and pickup and drop; the honorarium and workshop fees are negotiable on WhatsApp or on the call, and requests are welcome from institutions that can cover the visit this way. Email pranjulrathour41@gmail.com.
