Elastic vs. Pgvector
Compare Elastic vs. Pgvector by the following set of capabilities. We want you to choose the best database for you, even if it’s not us.
Elastic vs. Pgvector on Scalability
Yes. pgvector enables separation of storage and compute by allowing you to store your application data on one database while storing vectors, lookup values, and filter values on a separate database.
No. Only scale at the server level.
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.
pgvector scalability
You can use a solution like YugaByteDB to extend the capabilities of Postgres for distributed environments.
Elastic vs. Pgvector 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. Sparse & Dense Vectors and Scalar filtering.
1 (HNSW)
HNSW & IVFFlat
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.
Elastic vs. Pgvector 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
pgvector is an add-on to Postgres
Python, Java, Go, C++, Node.js, Rust, Ruby, .NET (C#), PHP, Perl
Use pgvector from any language with a Postgres client
Elastic vs. Pgvector: 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
Pgvector
pgvector is a PostgreSQL extension designed to facilitate the storage, querying, and indexing of vectors within a PostgreSQL database.
License: PostgreSQL License
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.