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 - Configure hybrid search
- Embedder setup - Integrate embedding providers
- Search preview - Explore search capabilities
Qdrant is a registered trademark of Qdrant Solutions GmbH. This comparison is based on publicly available information and our own analysis.