MongoDB Atlas vs TiDB

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.

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MongoDB Atlas vs. TiDB on Scalability

MongoDB AtlasTiDB

Separation of storage and compute

Yes. Atlas introduced search nodes, providing dedicated infrastructure for Atlas search and vector search workloads.

❌

Separation of query and insertions

❌

Yes.

Multi-replication

❌

Dynamic segment placement vs. static data sharding

Yes. Atlas can dynamically balance the data between shards via range migrations.

Both

Cloud-native

❌

Billion-scale vector support

❌

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.

MongoDB AtlasTiDB

Role-based Access Control (RBAC)

❌

Disk Index support

❌

Hybrid Search (ie Scalar filtering)

Yes - Pre-filtering using an MQL match experssion that compares an indexed field with boolean, number, or string.

Yes, vector search & SQL search

Partitions/namespaces/logical groups

No. MongoDB organizes data into databases and collections, but it does not have a hierarchical structure like sub-collections within collections.

❌

Index type supported

HNSW

Multiple in-memory indexes support

❌

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

MongoDB AtlasTiDB

Purpose-built for Vectors

Add on to Atlas

No, vector search is an add-on to TiDB Cloud serverless.

Tunable consistency

❌

Support for both stream and batch of vector data

❌

Binary Vector support

❌

Multi-language SDK

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.

Database rollback

✔️

❌

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