Qdrant vs Weaviate

Vector Search Engine for the next generation of AI applications

Qdrant (read: quadrant) is a vector similarity search engine and vector database.
It provides a production-ready service with a convenient API to store, search, and manage points—vectors with an additional payload.
Qdrant is tailored for extended filtering support, making it useful for all sorts of neural-network or semantic-based matching, faceted search, and other applications.

Qdrant is written in Rust 🦀, which makes it fast and reliable even under high load. See benchmarks.

With Qdrant, embeddings or neural network encoders can be turned into full-fledged applications for matching, searching, recommending, and much more!

Qdrant is also available as a fully managed Qdrant Cloud ⛅ including a free tier.

Quick Start • Agent Skills • Client Libraries • Demo Projects • Integrations • Contact

Getting Started

Agent Skills

Qdrant provides a collection of ready-to-use agent skills that bring Qdrant's vector search capabilities directly into your AI coding assistant. Install these skills to empower your agent in making critical engineering decisions for optimal vector search performance, such as quantization, sharding, tenant isolation, hybrid search, model migration, and more.

Client-Server

To experience the full power of Qdrant locally, run the container with this command:

docker run -p 6333:6333 qdrant/qdrant

Note that this starts an insecure deployment without authentication, open to all network interfaces. Please refer to secure your instance.

Now you can connect to the server with any client. For example, using Python:

from qdrant_client import QdrantClient

client = QdrantClient(url="http://localhost:6333")

Before deploying Qdrant to production, be sure to read our installation and security guides.

Clients

Qdrant offers the following client libraries to help you integrate it into your application stack:

• Official:
  • Go client
  • Rust client
  • JavaScript/TypeScript client
  • Python client
  • .NET/C# client
  • Java client
• Community:
  • Kotlin
  • PHP

Qdrant Edge

Qdrant Edge is a lightweight version of Qdrant designed for edge devices and resource-constrained environments. Unlike Qdrant Server, which uses a client-server architecture, Qdrant Edge runs inside the application process. Data is stored and queried locally and can be synchronized with a Qdrant server. It offers the same powerful vector search capabilities as the client-server version but with a smaller footprint, making it ideal for applications that require low latency and offline functionality.

To get started with Qdrant Edge from Python or Rust, initialize an instance of EdgeShard, which exposes methods to manage data, query it, and restore snapshots. For example:

from qdrant_edge import Distance, EdgeConfig, EdgeVectorParams, EdgeShard, Point, UpdateOperation

shard = EdgeShard.create("./shard", EdgeConfig(
    vectors={"my-vector": EdgeVectorParams(size=4, distance=Distance.Cosine)}
))
shard.update(UpdateOperation.upsert_points([
    Point(id=1, vector={"my-vector": [0.1, 0.2, 0.3, 0.4]}, payload={"color": "red"})
]))

Where Do I Go from Here?

• Quick Start Guide
• Detailed Documentation
• Take the Qdrant Essentials course
• Follow this tutorial to create a semantic search engine with Qdrant

Demo Projects

Discover Semantic Text Search 🔍

Unlock the power of semantic embeddings with Qdrant, transcending keyword-based search to find meaningful connections in short texts. Deploy a neural search in minutes using a pre-trained neural network, and experience the future of text search. Try it online!

Explore Similar Image Search - Food Discovery 🍕

There's more to discovery than text search, especially when it comes to food. People often choose meals based on appearance rather than descriptions and ingredients. Let Qdrant help your users find their next delicious meal using visual search, even if they don't know the dish's name. Check it out!

Master Extreme Classification - E-Commerce Product Categorization 📺

Enter the cutting-edge realm of extreme classification, an emerging machine learning field tackling multi-class and multi-label problems with millions of labels. Harness the potential of similarity learning models, and see how a pre-trained transformer model and Qdrant can revolutionize e-commerce product categorization. Play with it online!

API

REST

Qdrant provides a REST API with an OpenAPI 3.0 specification, enabling client generation for virtually any framework or programming language.

You can also download the raw OpenAPI definitions.

gRPC

For faster, production-tier searches, Qdrant also provides a gRPC interface.

Features

Dense, Sparse, and Multi Vector Search

Qdrant supports dense vectors for semantic similarity, sparse vectors for full-text search, and multivector search for objects with multiple embeddings or late interaction models like ColBERT.

Filtering on Payload

Attach any JSON payload to your vectors and filter on it using a rich set of conditions—keyword matching, full-text, numeric ranges, geo-locations, and more—combined with should, must, and must_not clauses.

Hybrid Search

Combine multiple vectors in a single query to get the best of semantic understanding and keyword precision, with results merged via configurable fusion strategies, such as Reciprocal Rank Fusion (RRF) and Distribution-Based Score Fusion (DBSF).

Vector Quantization and On-Disk Storage

Built-in quantization cuts RAM usage by up to 97% and lets you tune the trade-off between search speed and precision.

Distributed Deployment

Scale horizontally with sharding and replication, and update or resize collections with zero downtime.

Highlighted Features

• Faceting - aggregate search results by payload values.
• Recommendation - use positive and negative examples to find similar points.
• Discovery - constrain search to a specific region of the vector space.
• Search Relevance Tuning - tools for adjusting search results, such as Maximal Marginal Relevance (MMR) and the Relevance Feedback Query.
• Multitenancy - scalable partitioning of data for multi-user environments.
• Observability - comprehensive metrics, telemetry, and audit logging for monitoring and debugging.
• Query Planning and Payload Indexes - leverages stored payload information to optimize query execution strategy.
• SIMD Hardware Acceleration - utilizes modern CPU x86-x64 and Neon architectures to deliver better performance.
• GPU Support - for accelerated indexing, with support for NVIDIA and AMD GPUs.
• Async I/O - uses io_uring to maximize disk throughput utilization even on network-attached storage.
• Write-Ahead Logging - ensures data persistence with update confirmation, even during power outages.

Web UI

Web UI provides a visual way to interact with your data and monitor the health of your deployment. It enables you to explore your collections, manage data, interact with the REST API, and more.

Integrations

Qdrant integrates with the tools you're already using across every stage of your AI stack. You can connect to embedding providers, AI application frameworks, and data pipeline tools, as well as observability platforms for monitoring and tracing your vector search in production. No-code and low-code automation platforms are supported too. Refer to the Ecosystem page for the complete list.

Contributing

We are happy to receive your contributions! Before opening a pull request, please read our Contributing Guide.

[!IMPORTANT]
Our development branch is dev, not master. Please fork the repo, branch from dev, and open your pull request against dev. PRs targeting master will be asked to retarget.

Contacts

• Have questions? Join our Discord channel or mention @qdrant_engine on X
• Want to stay in touch with the latest releases? Subscribe to our Newsletters
• Looking for a managed cloud? Check pricing. Need something personalized? We're at info@qdrant.tech

License

Qdrant is licensed under the Apache License, Version 2.0. View a copy of the License file.

Weaviate

Weaviate is a cloud-native vector database that stores both objects and vectors, enabling semantic search at scale. It combines vector similarity search with keyword filtering, retrieval-augmented generation (RAG), and reranking in a single query interface. Common use cases include RAG systems, semantic and image search, recommendation engines, chatbots, and content classification.

Weaviate supports two approaches to store vectors: automatic vectorization at import using integrated models (OpenAI, Cohere, HuggingFace, and others) or direct import of pre-computed vector embeddings. Production deployments benefit from built-in multi-tenancy, replication, RBAC authorization, and many other features.

To get started quickly, have a look at one of these tutorials:

• Quickstart - Weaviate Cloud
• Quickstart - local Docker instance

Installation

Weaviate offers multiple installation and deployment options:

• Docker
• Kubernetes
• Weaviate Cloud

See the installation docs for more deployment options, such as AWS and GCP.

Getting started

You can easily start Weaviate and a local vector embedding model with Docker.
Create a docker-compose.yml file:

services:
  weaviate:
    image: cr.weaviate.io/semitechnologies/weaviate:1.36.0
    ports:
      - "8080:8080"
      - "50051:50051"
    environment:
      ENABLE_MODULES: text2vec-model2vec
      MODEL2VEC_INFERENCE_API: http://text2vec-model2vec:8080

  # A lightweight embedding model that will generate vectors from objects during import
  text2vec-model2vec:
    image: cr.weaviate.io/semitechnologies/model2vec-inference:minishlab-potion-base-32M

Start Weaviate and the embedding service with:

docker compose up -d

Install the Python client (or use another client library):

pip install -U weaviate-client

The following Python example shows how easy it is to populate a Weaviate database with data, create vector embeddings and perform semantic search:

import weaviate
from weaviate.classes.config import Configure, DataType, Property

## Connect to Weaviate
client = weaviate.connect_to_local()

## Create a collection
client.collections.create(
    name="Article",
    properties=[Property(name="content", data_type=DataType.TEXT)],
    vector_config=Configure.Vectors.text2vec_model2vec(),  # Use a vectorizer to generate embeddings during import
    # vector_config=Configure.Vectors.self_provided()  # If you want to import your own pre-generated embeddings
)

## Insert objects and generate embeddings
articles = client.collections.get("Article")
articles.data.insert_many(
    [
        {"content": "Vector databases enable semantic search"},
        {"content": "Machine learning models generate embeddings"},
        {"content": "Weaviate supports hybrid search capabilities"},
    ]
)

## Perform semantic search
results = articles.query.near_text(query="Search objects by meaning", limit=1)
print(results.objects[0])

client.close()

This example uses the Model2Vec vectorizer, but you can choose any other embedding model provider or bring your own pre-generated vectors.

Client libraries and APIs

Weaviate provides client libraries for several programming languages:

• Python
• JavaScript/TypeScript
• Java
• Go
• C#/.NET

There are also additional community-maintained libraries.

Weaviate exposes REST API, gRPC API, and GraphQL API to communicate with the database server.

Weaviate features

These features enable you to build AI-powered applications:

• ⚡ Fast Search Performance: Perform complex semantic searches over billions of vectors in milliseconds. Weaviate's architecture is built in Go for speed and reliability, ensuring your AI applications are highly responsive even under heavy load. See our ANN benchmarks for more info.

• 🔌 Flexible Vectorization: Seamlessly vectorize data at import time with integrated vectorizers from OpenAI, Cohere, HuggingFace, Google, and more. Or you can import your own vector embeddings.

• 🔍 Advanced Hybrid & Image Search: Combine the power of semantic search with traditional keyword (BM25) search, image search and advanced filtering to get the best results with a single API call.

• 🤖 Integrated RAG & Reranking: Go beyond simple retrieval with built-in generative search (RAG) and reranking capabilities. Power sophisticated Q&A systems, chatbots, and summarizers directly from your database without additional tooling.

• 📈 Production-Ready & Scalable: Weaviate is built for mission-critical applications. Go from rapid prototyping to production at scale with native support for horizontal scaling, multi-tenancy, replication, and fine-grained role-based access control (RBAC).

• 💰 Cost-Efficient Operations: Radically lower resource consumption and operational costs with built-in vector compression. Vector quantization and multi-vector encoding reduce memory usage with minimal impact on search performance.

• ⏱️ Object TTL: Automatically expire and remove stale data with configurable time-to-live settings per collection, with full RBAC and multi-tenancy support.

For a complete list of all functionalities, visit the official Weaviate documentation.

Useful resources

AI Agent Skills

Weaviate Agent Skills is a collection of skills for AI coding agents (Claude Code, Cursor, GitHub Copilot, and others) that enable them to work with Weaviate more accurately and efficiently. Skills cover searching, querying, collection management, data import, and full application blueprints (RAG, agentic RAG, chatbots, and more).

Install with:

npx skills add weaviate/agent-skills

Demo projects & recipes

These demos are working applications that highlight some of Weaviate's capabilities. Their source code is available on GitHub.

• Elysia (GitHub): Elysia is a decision tree based agentic system which intelligently decides what tools to use, what results have been obtained, whether it should continue the process or whether its goal has been completed.
• Verba (GitHub): A community-driven open-source application designed to offer an end-to-end, streamlined, and user-friendly interface for Retrieval-Augmented Generation (RAG) out of the box.
• Healthsearch (GitHub): An open-source project aimed at showcasing the potential of leveraging user-written reviews and queries to retrieve supplement products based on specific health effects.
• Awesome-Moviate (GitHub): A movie search and recommendation engine that allows keyword-based (BM25), semantic, and hybrid searches.

We also maintain extensive repositories of Jupyter Notebooks and TypeScript code snippets that cover how to use Weaviate features and integrations:

• Weaviate Python Recipes
• Weaviate TypeScript Recipes

Blog posts

• What is a Vector Database
• What is Vector Search
• What is Hybrid Search
• How to Choose an Embedding Model
• What is RAG
• RAG Evaluation
• Advanced RAG Techniques
• What is Multimodal RAG
• What is Agentic RAG
• What is Graph RAG
• Overview of Late Interaction Models

Integrations

Weaviate integrates with many external services:

Contributing

We welcome and appreciate contributions! Please see our Contributor guide for the development setup, code style guidelines, testing requirements and the pull request process. If you wish to contribute to our Enterprise Edition, your contributions will be under the BSD license.

Join our Community forum to discuss ideas and get help.

License

This repository contains code in our Community Edition and our Enterprise Edition. Most of this repository is available under the BSD 3-Clause License. Files in our wl/ folder are only available under a commercial license with a license key issued by us. If you are interested in a commercial license, please contact us at http://weaviate.io/enterprise-edition. Our binaries, available for download here, combine these two elements. Enterprise features require a license key.

Please see our licensing FAQ for more information.