Home on Qdrant - Vector Search Engine

Getting Started

info@qdrant.tech (Andrey Vasnetsov) — Mon, 01 Jan 0001 00:00:00 +0000

Cloud Quickstart

info@qdrant.tech (Andrey Vasnetsov) — Mon, 01 Jan 0001 00:00:00 +0000

Quick Start with Qdrant Cloud

Learn how to set up Qdrant Cloud and perform your first semantic search in just a few minutes. We’ll use a sample dataset of menu items embedded with the sentence-transformers/all-MiniLM-L6-v2 model via Cloud Inference. This is one of the free embedding models available on Qdrant Cloud. For a list of the available free and paid models, refer to the Inference tab of the Cluster Detail page in the Qdrant Cloud Console.

Managed Services

info@qdrant.tech (Andrey Vasnetsov) — Mon, 01 Jan 0001 00:00:00 +0000

Getting Started

info@qdrant.tech (Andrey Vasnetsov) — Mon, 01 Jan 0001 00:00:00 +0000

Getting Started with Qdrant Managed Cloud

Welcome to Qdrant Managed Cloud! This document contains all the information you need to get started.

Prerequisites

Before creating a cluster, make sure you have a Qdrant Cloud account. Detailed instructions for signing up can be found in the Qdrant Cloud Setup guide. Qdrant Cloud supports granular role-based access control.

You also need to provide payment details. If you have a custom payment agreement, first create your account, then contact our Support Team to finalize the setup.

Account Setup

info@qdrant.tech (Andrey Vasnetsov) — Mon, 01 Jan 0001 00:00:00 +0000

Setting up a Qdrant Cloud Account

Registration

There are different ways to register for a Qdrant Cloud account:

With an email address and passwordless login via email
With a Google account
With a GitHub account
By connection an enterprise SSO solution

Every account is tied to an email address. You can invite additional users to your account and manage their permissions.

Email Registration

Inviting Additional Users to an Account

You can invite additional users to your account, and manage their permissions on the Account -> Access Management page in the Qdrant Cloud Console.

Billing & Payments

info@qdrant.tech (Andrey Vasnetsov) — Mon, 01 Jan 0001 00:00:00 +0000

Qdrant Cloud Billing & Payments

Qdrant database clusters in Qdrant Cloud are priced based on CPU, memory, and disk storage usage. To get a clearer idea for the pricing structure, based on the amounts of vectors you want to store, please use our Pricing Calculator.

Billing

You can pay for your Qdrant Cloud database clusters either with a credit card or through an AWS, GCP, or Azure Marketplace subscription.

Your payment method is charged at the beginning of each month for the previous month’s usage. There is no difference in pricing between the different payment methods.

Premium Tier

info@qdrant.tech (Andrey Vasnetsov) — Mon, 01 Jan 0001 00:00:00 +0000

Qdrant Cloud Premium Tier

Qdrant Cloud offers an optional premium tier for customers who require additional features and better SLA support levels. The premium tier includes:

24/7 Support: Our support team is available around the clock to help you with any issues you may encounter (compared to 10x5 in standard).
Shorter Response Times: Premium customers receive priority support and can expect faster response times, with shorter SLAs.
99.9% Uptime SLA: We guarantee 99.9% uptime for your Qdrant Cloud clusters (compared to 99.5% in standard).
Single Sign-On (SSO): Premium customers can use their existing SSO provider to manage access to Qdrant Cloud.
VPC Private Links: Premium customers can connect their Qdrant Cloud clusters to their VPCs using private links.
Storage encryption with shared keys: Premium customers can encrypt their data at rest using their own keys.

Please refer to the Qdrant Cloud SLA for a detailed definition on uptime and support SLAs.

Interfaces & Tools

info@qdrant.tech (Andrey Vasnetsov) — Mon, 01 Jan 0001 00:00:00 +0000

Qdrant Cloud API

info@qdrant.tech (Andrey Vasnetsov) — Mon, 01 Jan 0001 00:00:00 +0000

Qdrant Cloud API: Powerful gRPC and Flexible REST/JSON Interfaces

Note: This is not the Qdrant REST or gPRC API of the database itself. For database APIs & SDKs, see our list of interfaces

Introduction

The Qdrant Cloud API lets you automate the Qdrant Cloud platform. You can use this API to manage your accounts, clusters, backup schedules, authentication methods, hybrid cloud environments, and more.

To cater to diverse integration needs, the Qdrant Cloud API offers two primary interaction models:

Support

info@qdrant.tech (Andrey Vasnetsov) — Mon, 01 Jan 0001 00:00:00 +0000

Support

info@qdrant.tech (Andrey Vasnetsov) — Mon, 01 Jan 0001 00:00:00 +0000

Qdrant Cloud Support and Troubleshooting

Community Support

All Qdrant Cloud users are welcome to join our Discord community.

Qdrant Cloud Support

Paying customers have access to our Support team. Links to the support portal are available in the Qdrant Cloud Console.

Support is handled via Jira Service Management (JSM). When creating a support ticket, you will be asked to select a request type and provide information to help us understand and prioritize your issue.

Security

info@qdrant.tech (Andrey Vasnetsov) — Mon, 01 Jan 0001 00:00:00 +0000

Qdrant Cloud Security

Compliance and Certifications

Qdrant is committed to maintaining high standards of security and compliance. We are both SOC2 Type 2 and HIPAA certified, ensuring that our systems and processes meet rigorous security criteria. You can find our compliance reports in our Trust Center. The trust center also contains our internal security policies and procedures, so you can learn how we manage data protection, vulnerabilities, disaster recovery, incident responses, and more.

Getting Started

info@qdrant.tech (Andrey Vasnetsov) — Mon, 01 Jan 0001 00:00:00 +0000

Migrate to Qdrant

info@qdrant.tech (Andrey Vasnetsov) — Mon, 01 Jan 0001 00:00:00 +0000

Qdrant Quickstart

info@qdrant.tech (Andrey Vasnetsov) — Mon, 01 Jan 0001 00:00:00 +0000

How to Get Started with Qdrant Locally

In this short example, you will use the Python Client to create a Collection, load data into it and run a basic search query.

Download and run

First, download the latest Qdrant image from Dockerhub:

docker pull qdrant/qdrant

Then, run the service:

docker run -p 6333:6333 -p 6334:6334 \
 -v "$(pwd)/qdrant_storage:/qdrant/storage:z" \
 qdrant/qdrant

Under the default configuration all data will be stored in the ./qdrant_storage directory. This will also be the only directory that both the Container and the host machine can both see.

User Manual

info@qdrant.tech (Andrey Vasnetsov) — Mon, 01 Jan 0001 00:00:00 +0000

Inference

info@qdrant.tech (Andrey Vasnetsov) — Mon, 01 Jan 0001 00:00:00 +0000

Inference

Inference is the process of using a machine learning model to create vector embeddings from text, images, or other data types. While you can create embeddings on the client side, you can also let Qdrant generate them while storing or querying data.

There are several advantages to generating embeddings with Qdrant:

No need for external pipelines or separate model servers.
Work with a single unified API instead of a different API per model provider.
No external network calls, minimizing delays or data transfer overhead.

Depending on the model you want to use, inference can be executed:

Qdrant Web UI

info@qdrant.tech (Andrey Vasnetsov) — Mon, 01 Jan 0001 00:00:00 +0000

Qdrant Web UI

You can manage both local and cloud Qdrant deployments through the Web UI.

If you’ve set up a deployment locally with the Qdrant Quickstart, navigate to http://localhost:6333/dashboard.

If you’ve set up a deployment in a cloud cluster, find your Cluster URL in your cloud dashboard, at https://cloud.qdrant.io. Add :6333/dashboard to the end of the URL.

Access the Web UI

Qdrant’s Web UI is an intuitive and efficient graphic interface for your Qdrant Collections, REST API and data points.

API & SDKs

info@qdrant.tech (Andrey Vasnetsov) — Mon, 01 Jan 0001 00:00:00 +0000

Interfaces

Qdrant supports these “official” clients.

Note: If you are using a language that is not listed here, you can use the REST API directly or generate a client for your language using OpenAPI or protobuf definitions.

Client Libraries

Client Repository	Installation	Version
Python + (Client Docs)	`pip install qdrant-client[fastembed]`	Latest Release
JavaScript / Typescript	`npm install @qdrant/js-client-rest`	Latest Release
Rust	`cargo add qdrant-client`	Latest Release
Go	`go get github.com/qdrant/go-client`	Latest Release
.NET	`dotnet add package Qdrant.Client`	Latest Release
Java	Available on Maven Central	Latest Release

API Reference

All interaction with Qdrant takes place via the REST API. We recommend using REST API if you are using Qdrant for the first time or if you are working on a prototype.

Qdrant Tools

info@qdrant.tech (Andrey Vasnetsov) — Mon, 01 Jan 0001 00:00:00 +0000

Qdrant MCP Server

info@qdrant.tech (Andrey Vasnetsov) — Mon, 01 Jan 0001 00:00:00 +0000

Tutorials

info@qdrant.tech (Andrey Vasnetsov) — Mon, 01 Jan 0001 00:00:00 +0000

Overview

info@qdrant.tech (Andrey Vasnetsov) — Mon, 01 Jan 0001 00:00:00 +0000

Qdrant Tutorial Repository

Basic Tutorials

Get up and running with Qdrant in minutes.

Tutorial	Objective	Stack	Time	Level
Qdrant Local Quickstart	Basic CRUD operations and local deployment.	Python	10m	Beginner
Semantic Search 101	Build a search engine for science fiction books.	Python	5m	Beginner

Search Engineering Tutorials

Master vector search modalities, reranking, and retrieval quality.

Tutorial	Objective	Stack	Time	Level
Semantic Search Intro	Deploy a search service for company descriptions.	FastAPI	30m	Beginner
Hybrid Search with FastEmbed	Combine dense and sparse search.	FastAPI	20m	Beginner
Relevance Feedback	Relevance Feedback Retrieval in Qdrant	Python	30m	Intermediate
Collaborative Filtering	Collaborative filtering using sparse embeddings.	Python	45m	Intermediate
Multivector Document Retrieval	PDF RAG using ColPali and embedding pooling.	Python	30m	Intermediate
Retrieval Quality Evaluation	Measure quality and tune HNSW parameters.	Python	30m	Intermediate
Hybrid Search with Reranking	Implement late interaction and sparse reranking.	Python	40m	Intermediate
Semantic Search for Code	Navigate codebases using vector similarity.	Python	45m	Intermediate
Multivectors and Late Interaction	Effective use of multivector representations.	Python	30m	Intermediate
Static Embeddings	Evaluate the utility of static embeddings.	Python	20m	Intermediate

Operations & Scale

Production-grade management, monitoring, and high-volume optimization.

Integrations

info@qdrant.tech (Andrey Vasnetsov) — Mon, 01 Jan 0001 00:00:00 +0000

Support

info@qdrant.tech (Andrey Vasnetsov) — Mon, 01 Jan 0001 00:00:00 +0000

Release Notes

info@qdrant.tech (Andrey Vasnetsov) — Mon, 01 Jan 0001 00:00:00 +0000

Ecosystem Guides

info@qdrant.tech (Andrey Vasnetsov) — Mon, 01 Jan 0001 00:00:00 +0000

Practice Datasets

info@qdrant.tech (Andrey Vasnetsov) — Mon, 01 Jan 0001 00:00:00 +0000

Common Datasets in Snapshot Format

You may find that creating embeddings from datasets is a very resource-intensive task. If you need a practice dataset, feel free to pick one of the ready-made snapshots on this page. These snapshots contain pre-computed vectors that you can easily import into your Qdrant instance.

Available datasets

Our snapshots are usually generated from publicly available datasets, which are often used for non-commercial or academic purposes. The following datasets are currently available. Please click on a dataset name to see its detailed description.

Explore the Qdrant Ecosystem

info@qdrant.tech (Andrey Vasnetsov) — Mon, 01 Jan 0001 00:00:00 +0000

Welcome to Qdrant Cloud

info@qdrant.tech (Andrey Vasnetsov) — Mon, 01 Jan 0001 00:00:00 +0000