Milvus vs Meilisearch
Two of the top vector databases, side by side: score, setup, license, activity and what each review found.
Milvus
Distributed vector database with dense, sparse and hybrid search at scale
Meilisearch
Rust search engine API with full-text, vector and hybrid search
| What we compare | Milvus | Meilisearch |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 82, widely used | 92, widely used |
| Freshness | 100, active | 100, active |
| Maintenance | 84, healthy | 81, healthy |
| Easy to run | 67, easy | 33, some setup |
| Agent-ready | 70, partly | 30, minimal |
| Facts from GitHub and the README | ||
| Stars | 46.3k | 59.5k |
| License | Apache-2.0 (permissive) | custom license (read the license) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | Sep 2026 | Oct 2026 |
| Language | Not stated | Not stated |
| Docker | Yes | Yes |
| GPU | Optional | Not needed |
| arm64 or Apple Silicon | Mentioned | Not stated |
Milvus
Milvus is a distributed vector database (Go and C++, LF AI & Data Foundation) that separates compute and storage on Kubernetes, with a Standalone Docker mode and pip-installable Milvus Lite. It offers HNSW, IVF, FLAT, SCANN and DiskANN indexes, GPU CAGRA, sparse BM25 and learned-sparse vectors for hybrid search, metadata filtering, multi-tenancy, hot/cold storage, auth, TLS and RBAC.
Who it is for: teams needing billion-scale vector search on Kubernetes
Strengths
- Index types HNSW, IVF, FLAT, SCANN, DiskANN plus GPU CAGRA
- Dense, sparse (BM25, SPLADE, BGE-M3) and hybrid search in one collection
- Multi-tenancy at database, collection, partition or partition-key level
- Mandatory auth, TLS and RBAC; Milvus Lite via pip for local dev
Weaknesses
- Distributed mode is Kubernetes-native with several microservices to operate
- No port, RAM or Docker command in the README; install lives in docs
- Zilliz is the major contributor and promotes its managed cloud
- Source build needs Go 1.21+, CMake, GCC 11+ and Python 3.8 to 3.11
- GPU optional
- Compose
- Compose runs Milvus, MinIO
- Models: any embedding model or service; pymilvus[model] wraps embedding and reranking models
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