Elastic vs. OpenSearch
Compare Elastic vs. OpenSearch by the following set of capabilities. We want you to choose the best database for you, even if it’s not us.
Elastic vs. OpenSearch on Scalability
No. Only scale at the server level.
Yes.
Static sharding
Both
Elastic scalability
With static sharding, if your data grows beyond the capacity of your server, you will need to add more machines to the cluster and re-shard all of your data. This can be a time-consuming and complex process. Additionally, imbalanced shards can introduce bottlenecks and reduce the efficiency of your system.
OpenSearch
OpenSearch supports horizontal scaling, cluster management optimizations, and efficient shard allocation, making it suitable for handling large datasets and high query loads effectively.
Elastic vs. OpenSearch on Functionality
Performance is the biggest challenge with vector databases as the number of unstructured data elements stored in a vector database grows into hundreds of millions or billions, and horizontal scaling across multiple nodes becomes paramount.
Furthermore, differences in insert rate, query rate, and underlying hardware may result in different application needs, making overall system tunability a mandatory feature for vector databases.
Yes. (combine vector and traditional search)
yes, vector search & keyword search & scalar filtered search
1 (HNSW)
ANN
Elastic functionality
Elasticsearch uses an inverted indexand builds vector search capability on top of the exsting search architecture. Elasticsearch is good at text search, but the whole architecture is not purpose-built for vector search.
OpenSearch
OpenSearch supports:
- Vectors with up to 16,000 dimensions.
- Vector generation through external libraries or directly within OpenSearch.
- Both binary and dense vectors.
- Cosine Similarity, Inner Product, and L2 Distance (Euclidean).
- Integration with multiple engines, including NMSLIB, Faiss, and Lucene, to facilitate vector indexing and searching.
Elastic vs. OpenSearch on Purpose-built
What’s your vector database for?
A vector database is a fully managed solution for storing, indexing, and searching across a massive dataset of unstructured data that leverages the power of embeddings from machine learning models. A vector database should have the following features:
- Scalability and tunability
- Multi-tenancy and data isolation
- A complete suite of APIs
- An intuitive user interface/administrative console
No. Vector search is an add-on to OpenSearch.
Python, Java, Go, C++, Node.js, Rust, Ruby, .NET (C#), PHP, Perl
Java, Python, JavaScript, Go, and .Net
Elastic vs. OpenSearch: what’s right for me?
Elasticsearch
Elasticsearch is built on Apache Lucene and was first released in 2010 by Elastic. License: Dual-licensed Server Side Public License (SSPL) or the Elastic License
OpenSearch
OpenSearch is an open-source software suite for search, analytics, security monitoring, and observability applications. It is not purpose-built for vector storage and search workloads but introduces a vector search plugin to provide this capability. Amazon OpenSearch Service is an AWS-managed service for OpenSearch.
Apache 2.0
The Definitive Guide to Choosing a Vector Database
Overwhelmed by all the options? Learn key features to look for & how to evaluate with your own data. Choose with confidence.