Elasticsearch vs Weaviate: Vector Databases Comparison
Compare Elasticsearch and Weaviate side by side. Both are tools in the Vector Databases category.
Quick Comparison
| Category | Vector Databases | Vector Databases |
| Pricing | — | Open Source |
| Best For | — | Developers who need a flexible, open-source vector database with multimodal and hybrid search |
| Website | elastic.co | weaviate.io |
| Key Features | — |
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| Use Cases | — |
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When to Choose Elasticsearch vs Weaviate
- Multimodal search across text, images, and more
- RAG with hybrid retrieval
- E-commerce product search
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 Weaviate
Weaviate is an open-source vector database that combines vector search with structured filtering and generative capabilities. It supports multiple vectorization modules, hybrid search (combining BM25 and vector search), and built-in integrations with LLMs for retrieval-augmented generation. Weaviate offers both self-hosted and managed cloud deployments, with a GraphQL API that makes it easy to query complex data structures.
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 →