Chroma vs MongoDB Atlas

Chroma vs. MongoDB Atlas

Compare Chroma 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.

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Chroma vs. MongoDB Atlas on Scalability

ChromaMongoDB Atlas

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

No. Can not scale beyond single node.

❌

Multi-replication

❌

Dynamic segment placement vs. static data sharding

No distributed data replacement

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

Cloud-native

❌

Billion-scale vector support

❌

Chroma scalability

Without any distributed data replacement, Chroma is not able to scale beyond a single node

Chroma 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.

ChromaMongoDB Atlas

Role-based Access Control (RBAC)

❌

Disk Index support

❌

Hybrid Search (ie Scalar filtering)

Yes with scalar filtering

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

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

1 (HNSW)

HNSW

Multiple in-memory indexes support

❌

Chroma functionality

Chroma uses HNSW algorithm to support kNN search.

MongoDB (Atlas Vector Search)

Atlas has support for vector embeddings that are less than or equal to 2048 dimensions.

Chroma 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: