Meilisearch vs lancedb
Two of the top projects in Vector databases, side by side: who leads on each score part and who each one suits, then setup, license, activity and what each review found.
Meilisearch
Rust search engine with typo tolerance, filtering and hybrid semantic search
lancedb
Embedded vector database built on the Lance columnar format
Verdict
Meilisearch ranks higher, 75 to 69. Of the 5 score parts we measure for both, Meilisearch leads on 1, lancedb on 2 and 2 are even.
- AdoptionMeilisearch leads by 61 points, 95 to 34: a wide gap.
- FreshnessEven at 100.
- Maintenancelancedb leads by 8 points, 88 to 80.
- Easy to runlancedb leads by 17 points, 67 to 50: a wide gap.
- Agent-readyEven at 30.
Pick Meilisearch if you are
Developers adding site, app or product search, self-hosted or managed
- Single binary or Docker image; quick start is one command on port 7700
- Typo tolerance, facets, filters, sorting, synonyms and geosearch built in
Pick lancedb if you are
Developers needing an embedded vector store for multimodal AI apps
- Runs embedded and locally, with no separate server required per the README
- Combines vector search, full-text search and SQL in one store
| What we compare | Meilisearch | lancedb |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 95, widely used | 34, known |
| Freshness | 100, active | 100, active |
| Maintenance | 80, fair | 88, healthy |
| Easy to run | 50, easy | 67, easy |
| Agent-ready | 30, minimal | 30, minimal |
| Facts from GitHub and the README | ||
| Stars | 59.6k | 11.7k |
| License | custom license (read the license) | Apache-2.0 (permissive) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | Oct 2026 | Oct 2026 |
| Language | Rust | Rust |
| Docker | Yes | Yes |
| GPU | Not needed | Optional |
| arm64 or Apple Silicon | Mentioned | Mentioned |
Meilisearch
Meilisearch is a search engine with a RESTful API for full-text, semantic and hybrid search, including typo tolerance, facets, sorting, synonyms and geosearch. It runs as a single binary or Docker image on port 7700, and ships an experimental MCP server at /mcp plus a LangChain vector store integration. The Community Edition is MIT licensed; sharding is in the Enterprise Edition.
Who it is for: Developers adding site, app or product search, self-hosted or managed
Strengths
- Single binary or Docker image; quick start is one command on port 7700
- Typo tolerance, facets, filters, sorting, synonyms and geosearch built in
- Hybrid full-text plus semantic search through one REST API
- SDKs for JavaScript, Python, PHP, Ruby, Go and Rust, plus a React integration
Weaknesses
- Sharding and other advanced features need the Enterprise Edition and a commercial agreement
- README recommends Meilisearch Cloud over self-hosting
- MCP server at /mcp is marked experimental
- RAM and disk requirements are not stated in the README
- no GPU
- Docker
- Models: embedding models for semantic search (providers unknown)
- port 7700
lancedb
LanceDB is a vector database written in Rust on top of the Lance columnar format. It stores vectors, metadata and multimodal data (text, images, video, point clouds) and supports vector similarity search, full-text search and SQL filtering. It runs locally or in your own cloud, with Python, TypeScript and Rust SDKs plus a REST API.
Who it is for: Developers needing an embedded vector store for multimodal AI apps
Strengths
- Runs embedded and locally, with no separate server required per the README
- Combines vector search, full-text search and SQL in one store
- Automatic data versioning and zero-copy access without extra infrastructure
- Python, TypeScript and Rust SDKs; integrates with LangChain, LlamaIndex, Pandas, Polars, DuckDB
Weaknesses
- README gives no benchmarks or hardware figures to back the billion-vector claims
- Managed Cloud and Enterprise tiers exist; feature split with open source is not detailed
- README has little setup detail and defers to external docs
- RAM and GPU requirements are not stated
- GPU optional
- Docker + Compose
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