Qdrant vs. Pinecone
Compare Qdrant vs. Pinecone by the following set of capabilities. We want you to choose the best database for you, even if it’s not us.
Qdrant 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
Qdrant 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.
Qdrant 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.
No. Authentication only
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)
Qdrant functionality
Qdrant uses three types of indexes to power the database. The three indexes are a Payload index, similar to an index in a conventional document-oriented database, a Full-text index for string payload, and a vector index. Their hybrid search approach is a combination of vector search with attribute filtering.
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.
Qdrant 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, Go, Rust
REST API, Python, Node.js
yes, with the collection backup & restore
Qdrant vs. Pinecone: what’s right for me?
Qdrant
Open source Qdrant is maintained by the commercial company offering a cloud version of Qdrant.
License: Apache-2.0 license
Pinecone
Pinecone is a managed, cloud-native vector database.
SaaS