Elasticsearch
Distributed search engine with vector capabilities
About Elasticsearch
Elasticsearch is a distributed search and analytics engine that supports vector search alongside traditional full-text search capabilities. It enables hybrid search scenarios combining semantic similarity and keyword search. Elasticsearch is widely used in production environments for applications such as search engines, log analysis, and AI-powered search. Its distributed architecture provides scalability, reliability, and performance for large datasets.
Key features
Pricing
Common use cases
Common questions about Elasticsearch
How do I add vector search to Elasticsearch?
Elasticsearch includes vector search capabilities through extensions or built-in features. Check the official documentation for installation and configuration instructions.
Should I use Elasticsearch for vector search?
If you're already using Elasticsearch, adding vector search can be simpler than introducing a new database. However, dedicated vector databases may offer better performance and features at scale.
What are the main use cases for Elasticsearch?
Elasticsearch is commonly used for enterprise search, log analytics + similarity, e-commerce search, and similar applications requiring semantic similarity search.
Does Elasticsearch integrate with popular AI tools?
Most vector databases integrate with LangChain, LlamaIndex, and popular embedding providers. Check the Elasticsearch documentation for specific integration guides and examples.
Comparisons featuring Elasticsearch
Not sure if Elasticsearch is right for you?
Compare it side-by-side with other vector databases to find the best fit for your project.