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Milvus vs Weaviate

A side-by-side comparison of Milvus and Weaviate, two Vector DB tools, drawn from Ignaite's continuously-verified listings.

Compared from listings verified as of

Milvus

Vector DB

Distributed vector database built for billion-scale search.

View Milvus

Weaviate

Vector DB

Open-source vector database with built-in vectorisers.

View Weaviate

At a glance

Feature comparison of Milvus and Weaviate
AttributeMilvusWeaviate
CategoryVector DBVector DB
PricingFREEMIUMFREEMIUM
LicenseOpen coreOpen core
DeploymentHybridHybrid
PlatformsAPIAPI
Model supportModel-agnosticModel-agnostic
Vendor (differs)ZillizWeaviate

The honest brief

Milvus

Storage/compute split plus DiskANN make it the most robust open-source choice at billion-vector scale.

  • Scales to billion-vector deployments
  • Storage/compute separation
  • Many index types (HNSW, IVF, DiskANN) + GPU
  • Mature project with a large community
  • Operationally heavy to self-host
  • Overkill for small workloads
  • Performance hinges on data quality
  • Higher latency than Qdrant at p50

Weaviate

Built-in vectorizer modules embed text inline — raw text in, vectors out — so you skip running a separate embedding pipeline.

  • Hybrid BM25 + vector search
  • Self-hostable or managed cloud
  • GraphQL and REST APIs
  • Resource-heavy at large scale
  • Module config has a learning curve
  • Managed tier costs add up
  • Newer than some lexical engines