Milvus vs Weaviate

Milvus excels in high-performance similarity search with advanced indexing, while Weaviate shines in hybrid search and ease of use with its… See pricing, features & verdict.

Data Tools
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Quick Comparison

Milvus

Best For:
High-performance similarity search at scale, especially for applications requiring complex indexing (e.g., IVF, HNSW)
Architecture:
Distributed, horizontally scalable with support for multiple index types and ML framework integration
Pricing Model:
Free with no usage limits; enterprise editions available with custom pricing
Ease of Use:
Moderate; requires configuration for optimal performance, but integrates well with ML ecosystems
Scalability:
High; designed for billion-scale data with distributed clustering
Community/Support:
Large, active open-source community with enterprise support options

Weaviate

Best For:
Hybrid search combining vector similarity and keyword filtering, ideal for semantic search and object storage
Architecture:
Distributed, built in Go with a GraphQL API, supports schema-based data modeling
Pricing Model:
Free tier with limited features; paid tiers start at $100/month for team plans, enterprise pricing custom
Ease of Use:
High; user-friendly with GraphQL API and schema-based design simplifying development
Scalability:
High; supports horizontal scaling and cloud-native deployment
Community/Support:
Growing open-source community with commercial support available

Feature Comparison

Search & Indexing

ANN Search

Milvus
Weaviate

Hybrid Search

Milvus
Weaviate

Filtering

Milvus
Weaviate

Index Types

Milvus
Weaviate

Scalability

Horizontal Scaling

Milvus
Weaviate

Replication

Milvus
Weaviate

Cloud-managed Option

Milvus
Weaviate

Developer Experience

Python SDK

Milvus
Weaviate

REST API

Milvus
Weaviate

Documentation

Milvus
Weaviate

Community Size

Milvus
Weaviate

Legend:

Full support⚠️Partial / LimitedNot supported

Our Verdict

Milvus excels in high-performance similarity search with advanced indexing, while Weaviate shines in hybrid search and ease of use with its GraphQL API. Both are scalable, but Milvus is better for ML-heavy workloads, and Weaviate for semantic search with schema-based data.

When to Choose Each

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Choose Milvus if:

For applications requiring complex indexing (e.g., IVF, HNSW) and integration with ML frameworks, especially at billion-scale data.

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Choose Weaviate if:

For hybrid search use cases combining vector similarity and keyword filtering, or when using a schema-based approach with GraphQL.

💡 This verdict is based on general use cases. Your specific requirements, existing tech stack, and team expertise should guide your final decision.

Frequently Asked Questions

What is the main difference between Milvus and Weaviate?

Milvus focuses on high-performance similarity search with advanced indexing, while Weaviate emphasizes hybrid search (vector + keyword) and schema-based data modeling with a GraphQL API.

Which is better for small teams?

Weaviate's free tier with limited features may be more accessible for small teams, while Milvus' free tier requires more configuration but offers scalability.

Can I migrate from Milvus to Weaviate?

Migration would require reformatting data to fit Weaviate's schema-based model and may involve reindexing, as the two systems use different architectures.

What are the pricing differences?

Milvus is free with no usage limits, while Weaviate offers a free tier with limited features and paid tiers starting at $100/month for team plans.

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