MongoDB Atlas vs. Vespa
Compare MongoDB Atlas vs. Vespa by the following set of capabilities. We want you to choose the best database for you, even if it’s not us.
MongoDB Atlas vs. Vespa on Scalability
Yes. Atlas introduced search nodes, providing dedicated infrastructure for Atlas search and vector search workloads.
Yes.
Yes. Atlas can dynamically balance the data between shards via range migrations.
Both
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.
MongoDB Atlas 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 - Pre-filtering using an MQL match experssion that compares an indexed field with boolean, number, or string.
Yes, vector search & keyword seach
No. MongoDB organizes data into databases and collections, but it does not have a hierarchical structure like sub-collections within collections.
HNSW
HNSW, Hybrid HNSW-IF (Inverted File), paged tensor attributes
MongoDB (Atlas Vector Search)
Atlas has support for vector embeddings that are less than or equal to 2048 dimensions.
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.
MongoDB Atlas 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
Add on to Atlas
Yes.
C#, Java, Node, Pymango
Python, Java
MongoDB Atlas vs. Vespa: what’s right for me?
MongoDB (Atlas Vector Search)
Altas is a managed cloud database based on MongoDB document database.
SaaS
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
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.