Meilisearch vs Qdrant
Two of the top vector databases, side by side: score, setup, license, activity and what each review found.
Meilisearch
Rust search engine API with full-text, vector and hybrid search
Qdrant
Rust vector database with payload filtering, REST and gRPC
| What we compare | Meilisearch | Qdrant |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 92, widely used | 72, popular |
| Freshness | 100, active | 100, active |
| Maintenance | 81, healthy | 82, healthy |
| Easy to run | 33, some setup | 33, some setup |
| Agent-ready | 30, minimal | 0, none |
| Facts from GitHub and the README | ||
| Stars | 59.5k | 35k |
| License | custom license (read the license) | Apache-2.0 (permissive) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | Oct 2026 | Oct 2026 |
| Language | Not stated | Not stated |
| Docker | Yes | Yes |
| GPU | Not needed | Optional |
| arm64 or Apple Silicon | Not stated | Not stated |
Meilisearch
Meilisearch is a Rust search engine with a REST API that combines full-text search (typo tolerance, facets, geosearch) with vector and hybrid search, returning results as you type. It adds API keys with fine-grained permissions, tenant tokens for multi-tenancy, conversational search and MCP and LangChain integrations. The Community Edition is MIT; sharding and S3 snapshots require the Enterprise Edition.
Who it is for: app developers needing instant search with semantic ranking
Strengths
- Search-as-you-type under 50 ms with typo tolerance and faceting
- Hybrid semantic plus full-text ranking in one engine
- API keys with fine-grained permissions and tenant tokens for multi-tenancy
- REST API with official SDKs; MCP and LangChain integrations
Weaknesses
- Sharding, S3 snapshots and search-rule previews are Enterprise Edition (BSL or commercial)
- Anonymized telemetry is on by default and must be disabled
- No port, RAM or install details in the README; docs only
- Vector search is documented under experimental features
- no GPU
- Docker
Qdrant
Qdrant is a Rust vector database exposing REST (OpenAPI 3.0) and gRPC on port 6333 for storing points (vectors plus JSON payload) and searching with dense, sparse and multivector (ColBERT) embeddings, rich payload filters and hybrid fusion (RRF, DBSF). It adds quantization, on-disk storage, sharding and replication, multitenancy, GPU-accelerated indexing and a web UI. Qdrant Edge runs the same engine embedded in-process.
Who it is for: developers wanting a filter-heavy vector store with gRPC
Strengths
- Dense, sparse and multivector (ColBERT) search with RRF and DBSF fusion
- Quantization cuts RAM up to 97 percent; on-disk storage and io_uring
- REST with OpenAPI 3.0 spec plus gRPC; six official clients
- Sharding and replication with zero-downtime collection resize
Weaknesses
- Default docker run has no auth and binds all interfaces
- GPU acceleration covers indexing only; search runs on CPU
- Qdrant Edge embedded mode is Python and Rust only
- Sharding and tenant isolation require upfront design
- GPU optional
- Docker
- Models: any embedding model; dense, sparse and late-interaction (ColBERT) vectors
- port 6333