MongoDB Atlas vs. Pinecone
Compare MongoDB Atlas vs. Pinecone 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. Pinecone on Scalability
Yes. Atlas introduced search nodes, providing dedicated infrastructure for Atlas search and vector search workloads.
Yes, for the Serverless tier.
Yes, for the Serverless tier.
Yes. Atlas can dynamically balance the data between shards via range migrations.
Static sharding
Pinecone
Pinecone supports the separation of compute and storage with their Serveless Tier.
For its POD-based clusters, Pinecone employs static sharding, which requires users to manually reshard data when scaling out the cluster.
MongoDB Atlas vs. Pinecone 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, with limited roles (only Org Owner & members are supported)
Available with the Pinecone S1 solution only
Yes - Pre-filtering using an MQL match experssion that compares an indexed field with boolean, number, or string.
Yes. Sparse & Dense Vectors and Scalar filtering.
No. MongoDB organizes data into databases and collections, but it does not have a hierarchical structure like sub-collections within collections.
Yes. Users cans organizes data into namespaces and should aware that there are a limited number of namespaces available. Please consult with Pinecone on the limitations.
HNSW
Closed source Index (proprietary)
MongoDB (Atlas Vector Search)
Atlas has support for vector embeddings that are less than or equal to 2048 dimensions.
Pinecone
RBAC is not enough for large organizations. Storage optimized (S1 ) has some performance challenges and can only get 10-50 QPS. The number of namespaces is limited and users should be careful when using metadata filtering as a way around this limitation as it will have a big impact on performance. Furthermore, data isolation is not available with this approach.
MongoDB Atlas vs. Pinecone 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
C#, Java, Node, Pymango
REST API, Python, Node.js
yes, with the collection backup & restore
MongoDB Atlas vs. Pinecone: what’s right for me?
MongoDB (Atlas Vector Search)
Altas is a managed cloud database based on MongoDB document database.
SaaS
Pinecone
Pinecone is a managed, cloud-native vector database.
SaaS
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.