Pgvector vs. Pinecone
Compare Pgvector vs. Pinecone by the following set of capabilities. We want you to choose the best database for you, even if it’s not us.
Pgvector vs. Pinecone on Scalability
Yes. pgvector enables separation of storage and compute by allowing you to store your application data on one database while storing vectors, lookup values, and filter values on a separate database.
Yes, for the Serverless tier.
Yes, for the Serverless tier.
Static sharding
pgvector scalability
You can use a solution like YugaByteDB to extend the capabilities of Postgres for distributed environments.
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.
Pgvector 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. Sparse & Dense Vectors and Scalar filtering.
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.
HNSW & IVFFlat
Closed source Index (proprietary)
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.
Pgvector vs. Pinecone on Purpose-built
pgvector is an add-on to Postgres
Use pgvector from any language with a Postgres client
REST API, Python, Node.js
yes, with the collection backup & restore
Pgvector vs. Pinecone: what’s right for me?
Pgvector
pgvector is a PostgreSQL extension designed to facilitate the storage, querying, and indexing of vectors within a PostgreSQL database.
License: PostgreSQL License
Pinecone
Pinecone is a managed, cloud-native vector database.
SaaS
