Weaviate vs. Pinecone
Compare Weaviate vs. Pinecone by the following set of capabilities. We want you to choose the best database for you, even if it’s not us.
Weaviate vs. Pinecone on Scalability
Yes, for the Serverless tier.
No. Only scale at the server level.
Yes, for the Serverless tier.
Static sharding
Static sharding
Weaviate scalability
With static sharding, if your data grows beyond the capacity of your server, you will need to add more machines to the cluster and re-shard all of your data. This can be a time-consuming and complex process. Additionally, imbalanced shards can introduce bottlenecks and reduce the efficiency of your system.
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.
Weaviate 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.
Coming soon
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.
1 (HNSW)
Closed source Index (proprietary)
Weaviate functionality
Weaviate uses two types of indexes to power the database. An inverted index, which maps data object properties to its location in the database and a vector index to support high performance querying. In addition, their hybrid search approach uses dense vectors to understand the context of the query and combines it with sparse vectors for keyword matches.
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.
Weaviate 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, Java, Go
REST API, Python, Node.js
yes, with the collection backup & restore
Weaviate vs. Pinecone: what’s right for me?
Weaviate
Weaviate is maintained by a single commercial company offering a cloud version of Weaviate. License: BSD-3-Clause license
Pinecone
Pinecone is a managed, cloud-native vector database.
SaaS
Compare Weaviate with others
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.