Key takeaways
- Qdrant is an open-source vector database written in Rust; Qdrant Cloud is its managed hosted service.
- A local Qdrant listens on http://localhost:6333 for REST and 6334 for gRPC; cloud clusters have their own URL and API key.
- Qdrant offers strong payload filtering, hybrid search and self-hosting; Pinecone is fully managed and proprietary.
- OpenSearch suits teams needing keyword search, analytics and vectors in one engine.
- For small projects, pgvector or Chroma is simpler; Qdrant suits dedicated vector workloads with heavy filtering.
Qdrant is an open-source vector database, written in Rust, for storing embeddings with metadata and running fast similarity search with filtering; Qdrant Cloud is the company's managed service that runs Qdrant clusters for you. It is a popular choice for RAG, semantic search and recommendations. This page explains Qdrant Cloud and how to use it, finding the Qdrant URL, who builds and uses Qdrant, and how it compares with OpenSearch, Pinecone, Chroma and PostgreSQL with pgvector.
What is Qdrant Cloud?
Qdrant Cloud is the managed, hosted version of the Qdrant vector database. It creates and runs clusters on major cloud providers, handling setup, scaling, backups and upgrades, and offers a free tier for small projects alongside paid plans.
You create a cluster in the Qdrant Cloud console, get a URL and API key, and connect from your application with Qdrant's client libraries, exactly as you would to a self-hosted instance. This suits teams who want Qdrant's features without operating infrastructure. Because the core database is open source, you can also self-host the same software with Docker if you need full control or data residency on your own servers.
How do I use Qdrant Cloud?
Sign up, create a cluster, copy its URL and create an API key, then connect with a Qdrant client, create a collection with the right vector size and distance metric, upsert points with payloads, and query.
Also asked as: how to use qdrant cloud
from qdrant_client import QdrantClient
from qdrant_client.models import VectorParams, Distance, PointStruct
client = QdrantClient(url="https://YOUR-CLUSTER-URL", api_key="YOUR_KEY")
client.create_collection("notes", vectors_config=VectorParams(size=384, distance=Distance.COSINE))
client.upsert("notes", points=[PointStruct(id=1, vector=[0.1] * 384, payload={"subject": "os", "page": 3})])
hits = client.query_points("notes", query=[0.1] * 384, limit=3).points
Keep the API key in environment variables, match the vector size to your embedding model, and add payload indexes for fields you filter on.
What is the Qdrant URL?
For a local Qdrant instance, the default URL is http://localhost:6333 for the REST API, with gRPC on port 6334. For Qdrant Cloud, each cluster has its own HTTPS URL shown in the console, used together with an API key.
Also asked as: what is qdrant url
The REST URL also serves a web dashboard at /dashboard on local instances, useful for inspecting collections. In Docker, map the ports with -p 6333:6333 -p 6334:6334 and mount a volume for storage so data persists. When your app runs in another container, use the Qdrant service name as the host, such as http://qdrant:6333, rather than localhost.
Where do I find my Qdrant URL?
For Qdrant Cloud, open the cloud console, select your cluster, and copy its endpoint URL from the cluster details; create or copy an API key from the same area. For self-hosted Qdrant, the URL is your server's address with port 6333.
Also asked as: where to find qdrant url
Common mistakes: using localhost from inside a different container, forgetting the port, using HTTP instead of HTTPS for cloud clusters, and missing the API key. If connection fails, test the URL with a simple request in the browser or with curl, check firewall rules, and confirm the cluster is running. Never expose a self-hosted Qdrant to the internet without authentication.
Who developed Qdrant?
Qdrant is developed by Qdrant, a company founded in 2021 that maintains the open-source database under the Apache 2.0 licence and runs Qdrant Cloud. The core is written in Rust for performance and memory safety.
The open-source project has an active community on GitHub, with client libraries for Python, JavaScript and TypeScript, Rust, Go, Java and .NET. Because it is open source, you can inspect the code, self-host freely, and contribute. The company funds development through its cloud and enterprise offerings, a common model for open-source infrastructure.
Who uses Qdrant?
Qdrant is used by startups, enterprises and developers building RAG systems, semantic search, recommendation engines, anomaly detection and AI agents' memory. The company publishes case studies and the open-source project has a large community of users.
Typical users choose it for fast filtered search, where vector similarity is combined with conditions on metadata, such as user, language or date, and for the option to self-host. It integrates with LangChain, LlamaIndex and other frameworks, so it appears in many tutorials. For your own decision, what matters is how it performs on your data and operational needs, not who else uses it.
What is the difference between Qdrant and OpenSearch?
Qdrant is a dedicated vector database focused on similarity search with rich filtering. OpenSearch is a search and analytics engine that combines strong keyword search, aggregations and logs with vector search.
Also asked as: qdrant vs opensearch
Choose Qdrant when vectors are the core workload and you want a lean, fast, specialised engine with excellent filtering. Choose OpenSearch when you need full-text search, BM25 relevance tuning, analytics and vectors in one system, or already run OpenSearch for logs or search. Hybrid search is possible in both: Qdrant supports sparse and dense vectors together, and OpenSearch combines BM25 with vector queries. Test retrieval quality on your own queries.
Qdrant vs Pinecone: how do they compare?
Both are vector databases with fast similarity search and metadata filtering. Qdrant is open source and can be self-hosted or used through Qdrant Cloud; Pinecone is a proprietary, fully managed service with no self-hosting option.
Feature-wise, both support namespaces or collections, filtering and hybrid search with sparse and dense vectors. Differences show in deployment control, data residency options, pricing models and operational responsibility. Qdrant lets you run the same software locally for development and in production on your own infrastructure; Pinecone minimises operations by being serverless. Benchmark both on your data, latency targets and expected scale.
Which is better, Pinecone or Qdrant?
Neither is better universally. Choose Pinecone if you want zero infrastructure work and a fully managed serverless service. Choose Qdrant if you want open source, self-hosting options, strong filtering and the flexibility to move between local, self-hosted and cloud.
Also asked as: which is better pinecone or qdrant
Decision factors:
- Control and residency: Qdrant can run on your own servers.
- Operations: Pinecone requires none; self-hosted Qdrant requires some.
- Lock-in: open-source Qdrant reduces it.
- Cost: compare on your expected data size and query volume.
- Developer experience: try both SDKs on a small prototype.
For most RAG projects, retrieval quality depends more on chunking, hybrid search and reranking than on this choice.
Which is better: Qdrant, Chroma or Postgres?
For small projects and prototypes, Chroma is the simplest. If you already use PostgreSQL, pgvector keeps vectors with your data and is often enough. Qdrant is the strongest of the three as a dedicated vector database for larger workloads with heavy filtering.
Also asked as: which is better qdrant chroma or postgres
A rough guide:
- Chroma: notebooks, prototypes, small local apps.
- Postgres with pgvector: apps already on Postgres; joins, transactions, row-level security.
- Qdrant: larger vector workloads, high query volume, complex payload filters, multi-tenant setups.
RAG.NextUpgrad runs hybrid dense and BM25 retrieval in about 220 MB on a free tier, a reminder that the simplest store that meets your needs is usually the right one. Measure recall and latency on your data before switching.
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.
