Elasticsearch vs Milvus: Vector Databases Comparison
Compare Elasticsearch and Milvus side by side. Both are tools in the Vector Databases category.
Quick Comparison
| Category | Vector Databases | Vector Databases |
| Pricing | — | Open Source |
| Best For | — | Organizations that need vector search at billion-scale with high throughput |
| Website | elastic.co | milvus.io |
| Key Features | — |
|
| Use Cases | — |
|
When to Choose Elasticsearch vs Milvus
- Billion-vector similarity search
- Large-scale recommendation systems
- Image and video retrieval
How to Choose a Vector Databases Tool
Key criteria to evaluate when comparing Vector Databases solutions:
About Elasticsearch
Elasticsearch has added k-NN vector search capabilities to its distributed search and analytics engine. Teams can combine vector similarity search with Elasticsearch's powerful full-text search, filtering, and aggregation features in a single platform, making it ideal for hybrid search applications at enterprise scale.
About Milvus
Milvus is an open-source vector database built for scalable similarity search, capable of handling billions of vectors. Backed by the Zilliz company, Milvus supports multiple index types (IVF, HNSW, DiskANN), GPU-accelerated search, and multi-tenancy. Zilliz Cloud offers a fully managed version with automatic scaling. Milvus is widely used in enterprise deployments requiring high-throughput vector search at scale.
What is Vector Databases?
Purpose-built databases for storing, indexing, and querying high-dimensional vector embeddings used in semantic search, RAG, and recommendation systems.
Browse all Vector Databases tools →