> ## Documentation Index
> Fetch the complete documentation index at: https://www.meilisearch.com/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Meilisearch vs Qdrant

> Compare Meilisearch and Qdrant for semantic search. Learn the differences between hybrid search engines and vector databases.

Qdrant is an open-source vector database written in Rust, designed specifically for AI applications and semantic search. It focuses on high-performance vector operations with advanced filtering capabilities.

## Quick comparison

|                          |    Meilisearch    |                 Qdrant                 |
| ------------------------ | :---------------: | :------------------------------------: |
| **Primary focus**        |   Hybrid search   |             Vector database            |
| **Full-text search**     | Native, optimized |           Via sparse vectors           |
| **License**              |        MIT        |               Apache 2.0               |
| **Self-hosting**         |        Yes        |                   Yes                  |
| **Embedding generation** |      Built-in     | Built-in (Cloud Inference) or external |
| **Typo tolerance**       |      Built-in     |             Not applicable             |
| **Cloud pricing**        |  From \$30/month  |     Free 1GB tier, then usage-based    |

## What Qdrant does well

### High-performance vector search

Qdrant's HNSW algorithm, optimized in Rust, delivers excellent vector search performance. Quantization can reduce memory usage significantly while maintaining accuracy.

### Filterable vector search

Qdrant's architecture integrates filtering directly into the search process rather than filtering after retrieval. This enables efficient combination of semantic similarity with metadata filters.

### Deployment flexibility

Unlike some competitors, Qdrant offers self-hosting, managed cloud, and hybrid deployment options. This flexibility supports various data sovereignty and infrastructure requirements.

### Open source

Qdrant is open-source under Apache 2.0 license, allowing inspection, modification, and self-hosting without vendor lock-in.

## When to choose Meilisearch instead

### You need strong full-text search

Meilisearch provides mature full-text search with typo tolerance, prefix matching, and sophisticated relevancy ranking. Qdrant's keyword capabilities via sparse vectors don't match the depth of a dedicated search engine.

### Typo tolerance is important

Meilisearch handles misspellings automatically with configurable tolerance. Vector search operates on embeddings of the exact query text, so typos produce different vectors and potentially different results.

### You want unified hybrid search

Meilisearch combines keyword and semantic search in a single API with adjustable balance. With Qdrant, you'd need to implement hybrid search logic yourself or use their sparse vector support.

### You prefer flexible embedding generation

Meilisearch can generate embeddings automatically through integrations with OpenAI, HuggingFace, Ollama, and any REST-compatible provider. Qdrant Cloud now offers built-in embedding via Cloud Inference, but self-hosted Qdrant still requires external embedding generation.

### Search relevancy tuning matters

Meilisearch offers configurable ranking rules, custom ranking attributes, and relevancy tuning out-of-the-box. Qdrant focuses on vector similarity with less flexibility for traditional relevancy adjustments.

### Your primary use case is site/app search

If you're building e-commerce search, documentation search, or general site search where keyword matching is essential, Meilisearch's full-text capabilities are more comprehensive.

## When to choose Qdrant

Consider Qdrant if:

* You're building AI applications where pure vector search is the primary requirement
* You need advanced vector operations like quantization and custom distance metrics
* You want to combine vector search with complex metadata filtering
* Your team manages embeddings externally and needs a dedicated vector store
* You require flexible deployment options including on-premises or hybrid cloud
* You're building recommendation systems based primarily on similarity matching

## Migration resources

If you're evaluating Meilisearch for semantic search:

* [AI-powered search guide](/capabilities/hybrid_search/getting_started) - Configure hybrid search
* [Embedder setup](/capabilities/hybrid_search/how_to/choose_an_embedder) - Integrate embedding providers
* [Search preview](/resources/self_hosting/getting_started/search_preview) - Explore search capabilities

<Note>
  Qdrant is a registered trademark of Qdrant Solutions GmbH. This comparison is based on publicly available information and our own analysis.
</Note>