Documentation on Qdrant - Vector Search Engine

Getting Started

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

Interfaces & Tools

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

Migrate to Qdrant

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

Installation

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

Installation requirements

The following sections describe the requirements for deploying Qdrant.

CPU and memory

The preferred size of your CPU and RAM depends on:

Number of vectors
Vector dimensions
Payloads and their indexes
Storage
Replication
How you configure quantization

Our Cloud Pricing Calculator can help you estimate required resources without payload or index data.

Supported CPU architectures:

64-bit system:

x86_64/amd64
AArch64/arm64

32-bit system:

Not supported

Storage

For persistent storage, Qdrant requires block-level access to storage devices with a POSIX-compatible file system. Network systems such as iSCSI that provide block-level access are also acceptable. Qdrant won’t work with Network file systems such as NFS, or Object storage systems such as S3.

Local 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.

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.

Distributed Deployment

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

Distributed deployment

Since version v0.8.0 Qdrant supports a distributed deployment mode. In this mode, multiple Qdrant services communicate with each other to distribute the data across the peers to extend the storage capabilities and increase stability.

How many Qdrant nodes should I run?

The ideal number of Qdrant nodes depends on how much you value cost-saving, resilience, and performance/scalability in relation to each other.

Prioritizing cost-saving: If cost is most important to you, run a single Qdrant node. This is not recommended for production environments. Drawbacks:

Snapshots

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

Snapshots

Available as of v0.8.4

Snapshots are tar archive files that contain data and configuration of a specific collection on a specific node at a specific time. In a distributed setup, when you have multiple nodes in your cluster, you must create snapshots for each node separately when dealing with a single collection.

This feature can be used to archive data or easily replicate an existing deployment. For disaster recovery, Qdrant Cloud users may prefer to use Backups instead, which are physical disk-level copies of your data.

Upgrades

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

Upgrading Qdrant

If you are several versions behind, multiple updates might be required to reach the latest version. When upgrading Qdrant, upgrade to the latest patch version of each intermediate minor version first. For example, if you are running version 1.15 and want to upgrade to 1.17, you must first upgrade all cluster nodes to 1.16.3 before upgrading to 1.17. A Qdrant node with version 1.17 will be compatible with a node with version 1.16, but not with a node with version 1.15. If you run a single node cluster, you also can not skip versions to ensure that all data migrations are properly applied. Qdrant Cloud does this automatically for you.

Security

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

Security

Qdrant supports various security features to help you secure your instance. Most of these must to be explicitly configured to make your instance production ready. Please read the following section carefully.

Secure Your Instance

By default, all self-deployed Qdrant instances are not secure. They are open to all network interfaces and do not have any kind of authentication configured. They may be open to everybody on the internet without any restrictions. You must therefore take security measures to make your instance production-ready. Please read through this section carefully for instructions on how to secure your instance.

Production Checklist

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

Things to Check Before Taking Qdrant into Production

A practical checklist to ensure Qdrant is optimized, stable, and ready to handle real-world load.

1. Distributed Deployment & Sharding

Architect for scale from day one. Retrofitting these patterns onto an existing deployment is costly.

Ensure you have enough shards to scale. Qdrant scales horizontally through sharding. Plan for enough shards to evenly distribute your data and load across the nodes in your cluster. At a minimum, you need one shard or replica per node.

Troubleshooting

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

Solving common errors

Too many files open (OS error 24)

Each collection segment needs some files to be open. At some point you may encounter the following errors in your server log:

Error: Too many files open (OS error 24)

In such a case you may need to increase the limit of the open files. It might be done, for example, while you launch the Docker container:

docker run --ulimit nofile=10000:10000 qdrant/qdrant:latest

The command above will set both soft and hard limits to 10000.

Cloud

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

User Manual

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.

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.

Capacity Planning

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

Capacity Planning

When setting up your cluster, you’ll need to figure out the right balance of RAM and disk storage. The best setup depends on a few things:

How many vectors you have and their dimensions.
The amount of payload data you’re using and their indexes.
What data you want to store in memory versus on disk.
Your cluster’s replication settings.
Whether you’re using quantization and how you’ve set it up.

Calculating RAM size

You should store frequently accessed data in RAM for faster retrieval. If you want to keep all vectors in memory for optimal performance, you can use this rough formula for estimation:

Cloud 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.

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:

Qdrant Cloud CLI

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

Qdrant Cloud CLI

qcloud is the official command-line interface for managing Qdrant Cloud. It lets you manage clusters, authentication, and anything the Qdrant Cloud API has to offer—all from your terminal.

Installation

From GitHub Releases

Download the latest release from GitHub Releases.

Select the archive that matches your OS and CPU architecture, extract it, and place the qcloud binary somewhere in your PATH (e.g. ~/.local/bin or /usr/local/bin).

macOS: The binary is currently not signed. If macOS blocks it, run xattr -d com.apple.quarantine qcloud after extracting.

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.
Topology Aware Multi-AZ Setup: Premium customers can deploy their clusters across multiple availability zones for higher availability and resilience. This guarantees a 99.95% uptime SLA for Multi-AZ clusters.

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

Qdrant Tools

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 MCP Server

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.

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.

Deploy Qdrant

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

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