MongoDB Atlas vs OpenSearch

MongoDB Atlas vs. OpenSearch

Compare MongoDB Atlas vs. OpenSearch 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. OpenSearch on Scalability

MongoDB AtlasOpenSearch

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

❌

OpenSearch

OpenSearch supports horizontal scaling, cluster management optimizations, and efficient shard allocation, making it suitable for handling large datasets and high query loads effectively.

MongoDB Atlas vs. OpenSearch 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 AtlasOpenSearch

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 & keyword search & scalar filtered 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

ANN

Multiple in-memory indexes support

❌

MongoDB (Atlas Vector Search)

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

OpenSearch

OpenSearch supports:

Vectors with up to 16,000 dimensions.
Vector generation through external libraries or directly within OpenSearch.
Both binary and dense vectors.
Cosine Similarity, Inner Product, and L2 Distance (Euclidean).
Integration with multiple engines, including NMSLIB, Faiss, and Lucene, to facilitate vector indexing and searching.

MongoDB Atlas vs. OpenSearch 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 AtlasOpenSearch

Purpose-built for Vectors

Add on to Atlas

No. Vector search is an add-on to OpenSearch.

Tunable consistency

❌

Support for both stream and batch of vector data

❌

Binary Vector support

❌

Multi-language SDK

C#, Java, Node, Pymango

Java, Python, JavaScript, Go, and .Net

Database rollback

✔️

❌

MongoDB Atlas vs. OpenSearch: what’s right for me?

MongoDB (Atlas Vector Search)

Altas is a managed cloud database based on MongoDB document database.

SaaS

OpenSearch

OpenSearch is an open-source software suite for search, analytics, security monitoring, and observability applications. It is not purpose-built for vector storage and search workloads but introduces a vector search plugin to provide this capability. Amazon OpenSearch Service is an AWS-managed service for OpenSearch.

Apache 2.0