Milvus vs Vespa
Milvus excels in large-scale vector similarity search with advanced indexing, while Vespa is optimized for real-time applications requiring… See pricing, features & verdict.
Quick Comparison
| Feature | Milvus | Vespa |
|---|---|---|
| Best For | Billion-scale similarity search with ML integration | Real-time applications requiring combined vector, text, and structured data queries |
| Architecture | Distributed, horizontally scalable with support for IVF, HNSW, and DiskANN indexes | Distributed serving engine optimized for low-latency queries and real-time computation |
| Pricing Model | Free tier with no limits | Free tier with no limits |
| Ease of Use | Moderate; requires configuration for optimal performance | Moderate; complex setup for advanced features like custom indexing |
| Scalability | High; designed for large-scale vector data | High; handles billions of documents with millisecond latency |
| Community/Support | Active open-source community with enterprise support options | Established enterprise-grade support with active community |
Milvus
- Best For:
- Billion-scale similarity search with ML integration
- Architecture:
- Distributed, horizontally scalable with support for IVF, HNSW, and DiskANN indexes
- Pricing Model:
- Free tier with no limits
- Ease of Use:
- Moderate; requires configuration for optimal performance
- Scalability:
- High; designed for large-scale vector data
- Community/Support:
- Active open-source community with enterprise support options
Vespa
- Best For:
- Real-time applications requiring combined vector, text, and structured data queries
- Architecture:
- Distributed serving engine optimized for low-latency queries and real-time computation
- Pricing Model:
- Free tier with no limits
- Ease of Use:
- Moderate; complex setup for advanced features like custom indexing
- Scalability:
- High; handles billions of documents with millisecond latency
- Community/Support:
- Established enterprise-grade support with active community
Feature Comparison
| Feature | Milvus | Vespa |
|---|---|---|
| Search & Indexing | ||
| ANN Search | — | — |
| Hybrid Search | — | — |
| Filtering | — | — |
| Index Types | — | — |
| Scalability | ||
| Horizontal Scaling | — | — |
| Replication | — | — |
| Cloud-managed Option | — | — |
| Developer Experience | ||
| Python SDK | — | — |
| REST API | — | — |
| Documentation | — | — |
| Community Size | — | — |
Search & Indexing
ANN Search
Hybrid Search
Filtering
Index Types
Scalability
Horizontal Scaling
Replication
Cloud-managed Option
Developer Experience
Python SDK
REST API
Documentation
Community Size
Legend:
Our Verdict
Milvus excels in large-scale vector similarity search with advanced indexing, while Vespa is optimized for real-time applications requiring combined vector, text, and structured data queries. Both are free and scalable but cater to different use cases.
When to Choose Each
Choose Milvus if:
For applications requiring high-performance vector similarity search with ML integration, such as recommendation systems or image retrieval.
Choose Vespa if:
For real-time applications needing combined vector, text, and structured data queries, such as search engines or ad targeting systems.
💡 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 Vespa?
Milvus focuses on vector similarity search with advanced indexing, while Vespa combines vector search with text and structured data queries in a single platform, optimized for real-time applications.
Which is better for small teams?
Both are free and suitable for small teams, but Vespa may be easier to adopt for teams needing text search capabilities, while Milvus is better for teams focused on vector search.
Can I migrate from Milvus to Vespa?
Migration is possible but requires exporting data from Milvus and reformatting it for Vespa, as the two systems have different indexing and query paradigms.
What are the pricing differences?
Both tools offer a free tier with no limits. Neither has publicly documented paid tiers, though enterprise support options may exist for both.