Elastic vs. Vespa
Compare Elastic vs. Vespa by the following set of capabilities. We want you to choose the best database for you, even if it’s not us.
Elastic vs. Vespa 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.
Vespa
Vespa is a scalable search engine with a robust distributed architecture that supports horizontal scaling by adding more nodes. It features automatic sharding and data redistribution, allowing it to efficiently manage large datasets and high query volumes.
Elastic vs. Vespa 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 (paged tensor attributes)
Yes. (combine vector and traditional search)
Yes, vector search & keyword seach
1 (HNSW)
HNSW, Hybrid HNSW-IF (Inverted File), paged tensor attributes
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.
Vespa
Vespa is a powerful search engine and vector database that can handle multiple searches simultaneously. It's great at vector search, text search, and searching through structured data.
Elastic vs. Vespa 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
Yes.
Python, Java, Go, C++, Node.js, Rust, Ruby, .NET (C#), PHP, Perl
Python, Java
Elastic vs. Vespa: 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
Vespa
Vespa is a powerful search engine and vector database that can handle multiple searches simultaneously. It's great at vector search, text search, and searching through structured data.
Apache 2.0
Compare Elastic with others
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.