FAISS vs. Pinecone
Compare FAISS vs. Pinecone by the following set of capabilities. We want you to choose the best database for you, even if it’s not us.
FAISS vs. Pinecone on Scalability
Yes, for the Serverless tier.
No. Can not scale beyond single node.
Yes, for the Serverless tier.
No distributed data replacement
Static sharding
FAISS scalability
Without any distributed data replacement, FAISS is not able to scale beyond a single node
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.
FAISS 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.
Yes, with limited roles (only Org Owner & members are supported)
Available with the Pinecone S1 solution only
Yes. Sparse & Dense Vectors and Scalar filtering.
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.
FLAT, IVS_FLAT, IVF_SQ8, IVF_PQ, HNSW, BIN_FLAT and BIN_IVF_FLAT
Closed source Index (proprietary)
FAISS functionality
FAISS is an algorithm to support kNN search.
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.
FAISS 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
Python, JavaScript
REST API, Python, Node.js
yes, with the collection backup & restore
FAISS vs. Pinecone: what’s right for me?
FAISS
Faiss is a powerful library for efficient similarity search and clustering of dense vectors, with GPU-accelerated algorithms and Python wrappers, developed at FAIR, the fundamental AI research team at Meta License: MIT license
Pinecone
Pinecone is a managed, cloud-native vector database.
SaaS