MongoDB Atlas vs. TiDB
Compare MongoDB Atlas vs. TiDB 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. TiDB 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
TiDB
TiDB is designed with scalability as one of its core features. It offers both horizontal and vertical scaling capabilities to handle growing workloads and data volumes.
MongoDB Atlas vs. TiDB 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 - Pre-filtering using an MQL match experssion that compares an indexed field with boolean, number, or string.
Yes, vector search & SQL search
No. MongoDB organizes data into databases and collections, but it does not have a hierarchical structure like sub-collections within collections.
HNSW
HNSW
No. HNSW only
MongoDB (Atlas Vector Search)
Atlas has support for vector embeddings that are less than or equal to 2048 dimensions.
TiDB
TiDB offers vector search through its serverless cluster and supports vectors with a maximum dimension of 16,000. The Vector data type in TiDB is designed to store single-precision floating-point numbers (Float32). It only supports cosine distance and L2 distance for similarity measurement.
MongoDB Atlas vs. TiDB 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
No, vector search is an add-on to TiDB Cloud serverless.
C#, Java, Node, Pymango
No. TiDB does not provide specific SDKs. Instead, it is designed to be compatible with MySQL, which means TiDB can be used with any language with MySQL client or driver support.
MongoDB Atlas vs. TiDB: what’s right for me?
MongoDB (Atlas Vector Search)
Altas is a managed cloud database based on MongoDB document database.
SaaS
TiDB
TiDB is an open-source distributed SQL database for OLAP and OLTP workloads. It now offers a vector search capability (in public beta) as an add-on to its SaaS solution, TiDB Cloud Serverless.
Apache 2.0