Quick comparison
See how Meilisearch and Qdrant compare at a glance.
Meilisearch
QdrantWhere Meilisearch fits as your Qdrant alternative
Answers to the most common evaluation questions when comparing the two.
Can Meilisearch replace Qdrant for vector search?
Yes for hybrid keyword and semantic workloads. Both engines are written in Rust; Meilisearch is search-first with vectors built in, Qdrant is vector-first.
- Hybrid keyword and semantic search in a single query, no app-side fusion.
- Typo tolerance, facets, geo, ranking rules, all included.
- Built-in embedders for OpenAI, Cohere, Mistral, Voyage, Jina, HuggingFace, AWS Bedrock, Gemini, Cloudflare Workers AI, and REST.
How does Meilisearch hybrid search compare to Qdrant?
Meilisearch combines keyword and dense vector search natively in one query. Qdrant supports vector search with limited full-text capabilities.
- Single query for keyword and semantic with no app-side reranking required.
- Full-text features like typo tolerance, ranking rules, and stop words are built in.
- Qdrant has sparse vector support if you need that specific pattern.
Does Meilisearch include built-in embedders like Qdrant Cloud?
Yes, on every plan, including the open-source Community Edition.
- Embedder integrations for OpenAI, Cohere, Mistral, Voyage, Jina, HuggingFace, AWS Bedrock, Gemini, Cloudflare Workers AI, and REST.
- Available on every plan, not gated to Cloud or higher tiers.
- Bring your own embeddings is also supported.
What's the migration path from Qdrant to Meilisearch?
Meilisearch publishes a dedicated Qdrant migration guide.
- Export Qdrant collections with payload and vectors.
- Import via the documents endpoint after mapping payload fields and configuring an embedder.
- Run both engines in parallel behind a feature flag before cutting over.
Where can I host Meilisearch in Europe?
Meilisearch Cloud offers EU regions you can pick at project creation.
- Meilisearch Cloud EU regions for data residency in Europe.
- GDPR-compliant and SOC 2 Type II certified.
- For teams with stricter requirements, Meilisearch can also be self-hosted on European infrastructure.
What Qdrant does well
Qdrant is a capable solution with its own strengths.
Rust-based performance
Built in Rust for high performance and memory safety, like Meilisearch.
Sparse vector support
Native support for sparse vectors enabling hybrid sparse-dense search.
Advanced filtering
Rich payload filtering options combined with vector similarity search.
Which one should you choose?
The right choice depends on your specific needs and constraints.
Choose Meilisearch if you…
Need excellent full-text search
Meilisearch handles typo tolerance and full-text ranking with zero configuration.
Want true hybrid search
Seamlessly combine keyword and semantic search in a single query.
Need built-in embeddings
Generate embeddings automatically without external services, on every plan.
Want a managed search service
Meilisearch Cloud is fully managed with HA, EU/US regions, and automated backups.
Choose Qdrant if you…
Need sparse vector support
Native support for sparse vectors and hybrid sparse-dense search.
Working with massive vector datasets
Optimized for very large-scale vector similarity search.
Need advanced vector filtering
Complex payload filtering with vector search.
Feature comparison
A detailed look at the features and capabilities of each solution.
| Feature | Meilisearch | Qdrant |
|---|---|---|
| Licensing | ||
| License | MIT CE / BUSL-1.1 EE | Apache 2.0 |
| Self-hosting | ||
| Search Features | ||
| Full-text search | Basic | |
| Typo tolerance | ||
| Faceted search | ||
| Geo search | ||
| AI Features | ||
| Vector search | ||
| Hybrid search | Limited | |
| Built-in embeddings | Cloud only | |
| Sparse vectors | ||