# Qdrant review

> Qdrant is an open-source vector similarity search engine and vector database written in Rust, with filtering on JSON payloads.

Devtoolwatch Board, the week of Oct 25–31, 2026. Canonical page: https://index.devtoolwatch.com/tools/qdrant/

## TL;DR

Qdrant is a vector database and search engine that stores vectors with JSON payloads and searches them through REST and gRPC APIs. It is for developers building semantic search, recommendation, or RAG retrieval. It supports dense, sparse, and multivector search, hybrid search, and filters applied during HNSW traversal.

## Qdrant at a glance

- **Category:** [RAG, data and memory](https://index.devtoolwatch.com/categories/rag-data-and-memory/)
- **Maintained by:** Qdrant (organization, [@qdrant](https://github.com/qdrant) on GitHub)
- **License:** Apache 2.0
- **Language:** Rust, Python, Shell
- **Repository:** [github.com/qdrant/qdrant](https://github.com/qdrant/qdrant)
- **Website:** [qdrant.tech](https://qdrant.tech)
- **npm package:** [@qdrant/js-client-rest](https://www.npmjs.com/package/@qdrant/js-client-rest) 1.19.0
- **PyPI package:** [qdrant-client](https://pypi.org/project/qdrant-client/) 1.19.1
- **Latest release:** [v1.19.2](https://github.com/qdrant/qdrant/releases/tag/v1.19.2) on GitHub, Oct 5, 2026
- **First published:** May 30, 2020

## What Qdrant does

- Qdrant stores, searches, and manages points, which are vectors with an attached JSON payload.
- Qdrant supports dense vectors for semantic similarity, sparse vectors for full-text search, and multivector search for late interaction models like ColBERT.
- Qdrant filters on payloads using keyword, full-text, numeric range, and geo conditions combined with should, must, and must_not clauses.
- Qdrant combines multiple vectors in one hybrid query and merges results with fusion strategies such as RRF and DBSF.
- Qdrant offers built-in vector quantization and on-disk storage to reduce RAM usage and tune speed against precision.
- Qdrant scales horizontally with sharding and replication, and supports multitenancy for multi-user environments.
- Qdrant provides recommendation from positive and negative examples, discovery search, faceting, and MMR-based relevance tuning.

### How it works

Qdrant runs as a server, for example in a Docker container on port 6333. Applications connect through a REST API with an OpenAPI 3.0 spec, a gRPC interface, or official clients for Python, JavaScript/TypeScript, Go, Rust, .NET, and Java. A Web UI lets you explore collections. Qdrant Edge runs inside the application process instead and can sync data with a Qdrant server.

## How to install Qdrant

**With Docker**

```
docker run -p 6333:6333 qdrant/qdrant
```

### Requirements

- The Python client qdrant-client requires Python 3.10 or later.
- The JavaScript REST client @qdrant/js-client-rest requires Node.js 22.0.0 or later.
- Docker is needed for the README's local quick start.

### First steps

1. Start a local Qdrant server with the Docker command, which exposes port 6333.
2. Connect from Python with QdrantClient(url="http://localhost:6333") from the qdrant_client package.
3. Read the installation and security guides before deploying, because the default container runs without authentication.
4. Follow the Quick Start Guide or the beginner tutorial to build a semantic search engine.

## Who Qdrant is for

- Developers building semantic search, image search, or recommendation systems on top of embeddings.
- Teams building RAG retrieval or AI agent memory that need metadata filtering.
- Applications needing hybrid keyword and vector search in a single query.
- Edge or offline applications that need in-process vector search through Qdrant Edge.

### Limitations

- Qdrant's default Docker command starts an insecure deployment with no authentication, open to all network interfaces.
- Qdrant Edge is labeled Beta on the homepage.
- Qdrant Edge's getting-started path covers Python and Rust only.
- Qdrant Serverless is listed as coming soon and is not yet available.

## Usage data (the week of Oct 25–31, 2026)

- Board rank: #178 overall, #19 in RAG, data and memory, momentum score 47
- Downloads that week: 4.5M (+5% vs 4-week average)
- New GitHub stars per week (last 12 weeks): Aug 9: 138; Aug 16: 150; Aug 23: 140; Aug 30: 149; Sep 6: 124; Sep 13: 184; Sep 20: 155; Sep 27: 102; Oct 4: 115; Oct 11: 119; Oct 18: 122; Oct 25: 129

## Qdrant alternatives

| Rank | Tool | Stars | Downloads/wk |
|---|---|---|---|
| #1 | [XERJ](https://index.devtoolwatch.com/tools/xerj-org-xerj/) | 17.9k | – |
| #2 | [Memvid](https://index.devtoolwatch.com/tools/memvid-memvid/) | 17.3k | – |
| #3 | [LEANN](https://index.devtoolwatch.com/tools/startrail-org-leann/) | 13.7k | – |
| #4 | [Graphify](https://index.devtoolwatch.com/tools/graphify-labs-graphify/) | 140k | 480.7k |
| #5 | [MarkItDown](https://index.devtoolwatch.com/tools/markitdown/) | 193.2k | 4.7M |
| #6 | [Firecrawl](https://index.devtoolwatch.com/tools/firecrawl/) | 197.4k | 2.9M |

## Qdrant FAQ

### Is Qdrant free and open source?

Yes. Qdrant's source code is on GitHub (qdrant/qdrant) under the Apache 2.0 license, so you can use it for free.

### How do I install Qdrant?

Run `docker run -p 6333:6333 qdrant/qdrant`. It needs Node.js >=22.0.0. The install section above has the other options from the project's README.

### Is Qdrant still maintained?

Yes. The qdrant/qdrant repository was last updated on Oct 21, 2026, and the latest release, v1.19.2 on GitHub, came out on Oct 5, 2026.

### How popular is Qdrant?

Qdrant has 35.4k GitHub stars as of Nov 1, 2026. It gained 129 new stars in the week of Oct 25–31, 2026, against a 12-week average of 136 a week. Its main package had about 4.5M downloads that week, +5% against its own 4-week average. That made it #19 among RAG, data and memory tools on the Devtoolwatch Board.

### Qdrant vs pgvector: how do they compare?

Qdrant and pgvector are both tracked by Devtoolwatch as RAG, data and memory tools. Qdrant has 35.4k GitHub stars and pgvector has 23.5k. In the week of Oct 25–31, 2026, Qdrant's main package had 4.5M downloads and pgvector's had 9.4M. pgvector is an open-source Postgres extension for exact and approximate vector similarity search, storing vectors alongside the rest of your data. [pgvector review](https://index.devtoolwatch.com/tools/pgvector/)

### Qdrant vs Milvus: how do they compare?

Qdrant and Milvus are both tracked by Devtoolwatch as RAG, data and memory tools. Qdrant has 35.4k GitHub stars and Milvus has 46.5k. In the week of Oct 25–31, 2026, Qdrant's main package had 4.5M downloads and Milvus's had 1.1M. [Milvus review](https://index.devtoolwatch.com/tools/milvus/)

### What are the best alternatives to Qdrant?

Other RAG, data and memory tools on the Devtoolwatch Board in the week of Oct 25–31, 2026, by momentum: XERJ (#1), Memvid (#2), LEANN (#3), Graphify (#4), MarkItDown (#5). The alternatives table above has their numbers.

### Is Qdrant a database?

Yes. Qdrant is a vector similarity search engine and vector database. It stores points, which are vectors with an additional JSON payload, and provides an API to store, search, and manage them. Write-ahead logging keeps data persistent, even during power outages.

### Can Qdrant run locally?

Yes. Qdrant runs locally with docker run -p 6333:6333 qdrant/qdrant, then you connect at http://localhost:6333. For in-process use without a server, Qdrant Edge stores and queries data locally and can sync with a Qdrant server.

### How do I use Qdrant in Python?

Qdrant's Python client is the qdrant-client package, which needs Python 3.10 or later. Import QdrantClient from qdrant_client and create a client with QdrantClient(url="http://localhost:6333") to connect to a running server.

### Can Qdrant be used for RAG?

Yes. Qdrant lists RAG and AI agents among its use cases. It provides vector search with metadata filtering, hybrid dense and sparse search, and reranking options such as late interaction models and MMR.

### Does Qdrant support hybrid search?

Yes. Qdrant combines multiple vectors in one query and merges results with fusion strategies such as Reciprocal Rank Fusion and Distribution-Based Score Fusion. The homepage lists support for BM25, SPLADE++, and miniCOIL sparse representations.

### What does Qdrant integrate with?

Qdrant integrates with embedding providers, AI application frameworks, data pipeline tools, observability platforms, and no-code and low-code automation platforms. The Ecosystem page in the documentation has the complete list.

## Sources

1. [qdrant/qdrant on GitHub](https://github.com/qdrant/qdrant)
2. [Qdrant README](https://github.com/qdrant/qdrant/blob/master/README.md)
3. [@qdrant/js-client-rest on npm](https://www.npmjs.com/package/@qdrant/js-client-rest)
4. [qdrant-client on PyPI](https://pypi.org/project/qdrant-client/)
5. [Qdrant website](https://qdrant.tech)
6. [Release v1.19.2 (Oct 5, 2026)](https://github.com/qdrant/qdrant/releases/tag/v1.19.2)
7. [Release v1.19.1 (Sep 4, 2026)](https://github.com/qdrant/qdrant/releases/tag/v1.19.1)
8. [Release v1.19.0 (Aug 5, 2026)](https://github.com/qdrant/qdrant/releases/tag/v1.19.0)
9. [Hacker News: We were not accepted into Google Summer of Code. So, we started our own](https://news.ycombinator.com/item?id=39494741)
10. [Hacker News: Qdrant, the Vector Search Database, raised $28M in a Series A round](https://news.ycombinator.com/item?id=39101682)
11. [Hacker News: Serverless Semantic Search, Free tier only](https://news.ycombinator.com/item?id=36693239)
12. [Devtoolwatch Board, the week of Oct 25–31, 2026](https://index.devtoolwatch.com/)