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Engineered for Massive Scale

What is

?

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Vector Search Basics

Two different vector embeddings should be close to each other if they represent a similar input object.

Embeddings are generated by neural networks and can represent thousands of dimensions.

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Vector Search Basics

Although word counting produces embeddings, dense embeddings are needed to capture semantics

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	an	another	embedding	is	this	Query Sim.
“this is an embedding”	[1,	0,	1,	1,	1]	3
“this is another embedding”	[0,	1,	1,	1,	1]	2

Query:

“What is an embedding?”

Sparse embedding: �e.g. One Hot Encoding

Dense embedding:�e.g. from BERT

Engineered For Epic Scale

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Vector Search in Production

Written in Rust and offers great performance
Allows to interact by HTTP or gRPC protocols.
Runs both in single and multiple node setup.
Incorporates category, geo-coordinates and full-text filters
Supports hybrid, multimodal, multivector and multi-staged search
Official Python, Javascript/Typescript, Rust and Go SDKs.
Makes vector search affordable.

For Managed Cloud solutions, check out�Cloud Embeddings Inference.

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Qdrant-at-a-Glance

Vector Search Engine. Not Database.

Qdrant is designed from the ground up to handle high-dimensional vector math and (dis-)similarity-based retrieval. This allows for leveraging the full potential of vector search beyond simple similarity ranking from multi-stage filtering to dynamic exploration of high-dimensional spaces.

Specialized for Advanced Vector Operations

Qdrant is purposed to handle extremely high-dimensional embeddings. It’s designed with a vector index as a central component of the system, allowing a custom, finely tuned approach to data and index management that secures high performance even as data grows and changes dynamically

Engineered for Vector Search at Scale

Qdrant is built from the ground up with search as the core functionality. Conventional databases focus on ACID transactions and strong consistency. In contrast, search engines are optimized for scalability, low-latency search, and high availability.

Built-Out for Search-First Workflows

Specialized for Heavy, High-Dimensional Vectors

Qdrant is purpose-built to handle extremely high-dimensional embeddings, up to 100,000 dimensions per PDF page in advanced retrieval tasks. Traditional databases aren’t designed for this scale or complexity.�

Engineered Around Search Patterns, Not Transactional Consistency

Conventional databases focus on ACID transactions, strong consistency, and structured query capabilities. In contrast, search engines are optimize for:

Scalability, often across multiple nodes
Low-latency search, even under heavy workloads
High availability, accepting eventual consistency �

Built-Out for Search-First Workflows

Qdrant is built from the ground up with search as the core functionality, not transactional database plugin

This avoids bloating a relational or operational database with vector-specific infrastructure, enabling better performance, better scaling, and easier maintenance

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How Qdrant Achieves Search

Vector Search�Scalable similarity and discovery search (billions of vectors)

Core Capabilities

Hybrid Search

Combine dense + sparse embeddings, filters, and metadata

Filtering

Numeric, categorical, geo, temporal filters out-of-the-box

Distributed & Resilient

Replication, sharding, multi-tenancy

Advanced Features

Re-ranking

Maximum Marginal Relevance (MMR), score boosting

Quantization

Binary, scalar & product; lower cost without major recall loss

Multi-vectorsLate interaction for retrieval models (e.g. ColBERT)

Performance Optimizations

HNSW tuning, payload indexing, prefetching

Similarity Search

with MMR

Filterable HNSW

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Qdrant Open Source �Usually deployed with Docker containers. Lightweight, offers all the functionalities of Qdrant.

Qdrant Managed Cloud�Run on one of the three major cloud providers: AWS, Azure, or GCP. Provides a management UI and API. For US regions, we offer Cloud Inference that processes raw data into vectors.

Qdrant Hybrid Cloud �All the benefits of cloud deployment, but keeping the data on your premises. Requires a Kubernetes cluster and might be managed from Qdrant Cloud UI, but no data leaves your environment.

Qdrant Private Cloud�A dedicated, on-premise solution that guarantees supreme data privacy and sovereignty.

Python SDK Local Mode �Suitable mostly for quick experiments, but not intended to be running in production.

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Getting Started with Qdrant

Ecosystem

and more…

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Qdrant Innovations

FastEmbed�

Lightweight, fast Python library built for embedding generation built in & integrated with Qdrant.

Minimum dependencies: doesn’t require GPU and doesn’t download GBs of PyTorch dependencies
Designed for Speed: uses the ONNX Runtime & data parallelism
Cutting edge models support:

Late interaction (ColPali, ColBERT)
Sparse Neural (SPLADE, BM42, miniCOIL)
MUVERA embeddings
all the top open-source embedding models for text & images

Seamless integration with Qdrant

Inference & upsert/search in one go
Access embedding models developed in Qdrant (miniCOIL, BM42)

MCP Servers�

mcp-server-qdrant

An official MCP server for storing and retrieving information in Qdrant. �Supports stdio, sse & steamable-http protocols.�

Applicable for:

inline retrieval augmented generation (RAG)
automating codebase documentation
personalizing your coding assistant based on your project requirements/best practices�

Built to be a basis for your customizations.

mcp-for-docs

universal open-source API reference for AI coding assistants based on semantic code retrieval

Qdrant Edge

Beta�

Qdrant Edge is a lightweight, in-process vector search engine designed to run on edge devices, from IoT sensors to mobile phones.

Runs as a lightweight, in-process library.
Memory usage with built-in compression options and offload data to disk
Supports dense and multimodal vectors with structured filtering.
Retrieval runs fully offline. Sync with Qdrant Cloud only when required
Edge-Scale Multitenancy with Native SDKs

Private beta available to selected teams building embedded or edge-native AI systems.

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Qdrant Innovations

FastEmbed�

Generate high-quality embeddings fast. A small Python library for embedding generation, built in and integrated with Qdrant.

Works out of the box in Qdrant.
Few dependencies: runs on CPU; skips multi-GB PyTorch downloads.
Made for speed: uses ONNX Runtime and data parallelism.

Key features

Use Qdrant models (miniCOIL, BM42).
Support for late-interaction (ColPali, ColBERT) and sparse-neural methods (SPLADE, BM42, miniCOIL), MUVERA embeddings and more.
Run inference and upsert/search in one call.

Import:

MCP Servers�

�Build custom retrieval-based AI apps fast. Start from these servers and add tools/commands for your data and workflows.

mcp-server-qdrant: official MCP server for storing and retrieving data in Qdrant.
mcp-for-docs: open-source API reference for AI coding assistants using semantic code retrieval.

Key features

Automate codebase documentation.
Personalize your coding assistant to your project’s conventions and rules.
Do inline RAG.
Speaks stdio, sse, and streamable-http protocols.

Run:

Qdrant Edge�

�Bring vector search to the edge: an embeddable, high-performance engine that runs directly on mobile and other edge devices.

Run on low-CPU devices.
Use one API to manage and synchronize data on-device and in your cloud cluster.
Fit common on-device cases: phones and laptops, smart-home/IoT, robotics.

Key features

Use local storage to avoid network latency.
Support multi-tenant setups; treat each device as its own tenant.
Embed as a library; runs in-process with no background daemons.

Use:

from qdrant_client.models import Document, Image

docker run mcp-server-qdrant

client = QdrantClient(path="qdrant_edge.db")

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Vector Search

An essential part of the AI Transformation

Gig Economy

E-Commerce

Legal-Tech

Fashion

Biometrics

Med-Tech

Anti-fraud

Agriculture

Manufacturing

Streaming Services

Ed-Tech

Marketplaces

Media & News

HR-Tech

Ad-Tech

Online Dating

Anomaly Detection

Recommendations

RAG / Information Assistants

Search Systems