Elastic vs. Weaviate
Compare Elastic vs. Weaviate by the following set of capabilities. We want you to choose the best open source database for you, even if it’s not us.
elastic vs. weaviate on Scalability
No. Only scale at the server level.
No. Only scale at the server level.
Static sharding
Static sharding
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.
Weaviate 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.
elastic vs. weaviate 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.
Coming soon
Yes. (combine vector and traditional search)
Yes (combine Sparse and Dense Vectors)
1 (HNSW)
1 (HNSW)
Elastic functionality
Elasticsearch uses reverse index and 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.
Weaviate functionality
Weaviate uses two types of indexes to power the database. An inverted index, which maps data object properties to its location in the database and a vector index to support high performance querying. In addition, their hybrid search approach uses dense vectors to understand the context of the query and combines it with sparse vectors for keyword matches.
elastic vs. weaviate 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
Python, Java, Go, C++, Node.js, Rust, Ruby, .NET (C#), PHP, Perl
Python, Java, Go
elastic vs. weaviate: 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
Weaviate
Weaviate is maintained by a single commercial company offering a cloud version of Weaviate.
License: BSD-3-Clause license
