Meilisearch vs Weaviate
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
Weaviate
Go vector database with built-in vectorizers, hybrid search and RAG
| What we compare | Meilisearch | Weaviate |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 92, widely used | 42, known |
| Freshness | 100, active | 100, active |
| Maintenance | 81, healthy | 71, fair |
| Easy to run | 50, easy | 50, easy |
| Agent-ready | 30, minimal | 40, minimal |
| Facts from GitHub and the README | ||
| Stars | 59.6k | 16.9k |
| License | custom license (read the license) | custom license (read the license) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | Oct 2026 | Oct 2026 |
| Language | Rust | Go |
| Docker | Yes | Yes |
| GPU | Not needed | Not needed |
| arm64 or Apple Silicon | Mentioned | Mentioned |
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
Weaviate
Weaviate is a Go vector database that stores objects with their vectors and serves hybrid BM25 plus semantic search, filtering, built-in RAG and reranking through REST, gRPC and GraphQL APIs. It can vectorize data at import using modules for OpenAI, Cohere, HuggingFace, Google or a local model2vec image, or accept precomputed vectors. Docker Compose runs it on ports 8080 and 50051; production adds multi-tenancy, replication and RBAC.
Who it is for: teams wanting built-in vectorization, hybrid search and RAG in the database
Strengths
- Vectorizes at import with OpenAI, Cohere, HuggingFace, Google or a local model2vec container
- Hybrid BM25 plus vector, image search, filtering, RAG and reranking in one query
- Multi-tenancy, replication, RBAC, horizontal scaling and vector compression
- REST, gRPC and GraphQL with Python, TypeScript, Java, Go and C# clients
Weaknesses
- Enterprise features in wl/ need a commercial license key; one image mixes both
- Vectorization needs a module container or external API keys
- No RAM or sizing guidance in the README
- Both REST 8080 and gRPC 50051 must be exposed
- no GPU
- Docker + Compose
- Needs optional embedding inference container (e.g. model2vec) or external embedding APIs
- Models: OpenAI, Cohere, HuggingFace, Google and other integrated model providers, local model2vec (minishlab/potion-base-32M), precomputed vectors
- port 8080
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