Vector Databases
Milvus vs Weaviate
Side by side
- Pricing model
- MilvusFlat rate
- WeaviateHybrid
- Starting price
- MilvusFree / Open Source
- WeaviateFreemium
- Free tier
- MilvusYes
- WeaviateYes
- Maturity
- MilvusEstablished
- WeaviateEstablished
- Availability
- MilvusGenerally available
- WeaviateGenerally available
- Last checked
- MilvusOct 15, 2026
- WeaviateOct 16, 2026
| Fact | Milvus | Weaviate |
|---|---|---|
| Pricing model | Flat rate | Hybrid |
| Starting price | Free / Open Source | Freemium |
| Free tier | Yes | Yes |
| Maturity | Established | Established |
| Availability | Generally available | Generally available |
| Last checked | Oct 15, 2026 | Oct 16, 2026 |
When each one fits
Choose Milvus when…
Enterprise architects and large teams who need to handle billions of vectors with enterprise-grade reliability, RBAC, and Kubernetes-native deployment
Organizations with compliance requirements who need a self-hosted vector database with full data control
Teams building large-scale recommendation systems, image search, or multimodal AI applications
Look past Milvus if…
Solo developers or small teams prototyping who need quick setup since Milvus has significant operational complexity
Startups who need a managed zero-ops solution without Kubernetes expertise
Milvus strengths
Billion-scale vectors
Multiple index types (HNSW, IVF, DiskANN)
RBAC
Multi-tenancy
GPU acceleration
Kubernetes-native
Streaming ingestion
Zilliz Cloud managed option
LangChain and LlamaIndex integrations
Choose Weaviate when…
Developers building semantic search or RAG pipelines who want automatic vectorization without managing embedding models separately
Enterprise teams who need a compliant vector database with self-hosted or managed options
Startups prototyping AI features who want built-in integrations with OpenAI and Cohere
Look past Weaviate if…
Teams who already manage their own embedding pipeline and need only raw ANN search
Developers needing maximum raw query performance who prefer a leaner Rust-based engine
Weaviate strengths
Built-in vectorization modules
GraphQL and REST API
Multi-tenancy
Hybrid BM25 and vector search
Schema-based objects
Horizontal scaling
LangChain and LlamaIndex integrations
Self-hosted and managed cloud
Trade-offs
Known weaknesses from each tool’s record. Sourced ones link to where they came from.
Milvus
Limited language support beyond Python and Go/C++
No source on file
Complex setup for non-Kubernetes environments
No source on file
Weaviate
No native support for Windows operating systems or binaries.
Inability to reindex data smoothly, particularly for updating embeddings or inverted indexes.
No support for cross-tenant queries, requiring multiple separate queries to search across tenants.