Elastic vs. MongoDB Atlas
Compare Elastic vs. MongoDB Atlas by the following set of capabilities. We want you to choose the best database for you, even if it’s not us.
Elastic vs. MongoDB Atlas on Scalability
Yes. Atlas introduced search nodes, providing dedicated infrastructure for Atlas search and vector search workloads.
No. Only scale at the server level.
Static sharding
Yes. Atlas can dynamically balance the data between shards via range migrations.
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.
Elastic vs. MongoDB Atlas 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 - Pre-filtering using an MQL match experssion that compares an indexed field with boolean, number, or string.
No. MongoDB organizes data into databases and collections, but it does not have a hierarchical structure like sub-collections within collections.
1 (HNSW)
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.
MongoDB (Atlas Vector Search)
Atlas has support for vector embeddings that are less than or equal to 2048 dimensions.
Elastic vs. MongoDB Atlas 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
Add on to Atlas
Python, Java, Go, C++, Node.js, Rust, Ruby, .NET (C#), PHP, Perl
C#, Java, Node, Pymango
Elastic vs. MongoDB Atlas: 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
MongoDB (Atlas Vector Search)
Altas is a managed cloud database based on MongoDB document database.
SaaS
