Weaviate vs. Qdrant
Compare Weaviate vs. Qdrant by the following set of capabilities. We want you to choose the best database for you, even if it’s not us.
Weaviate vs. Qdrant on Scalability
No. Only scale at the server level.
No. Only scale at the server level.
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.
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.
Weaviate vs. Qdrant 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
No. Authentication only
Yes. Sparse & Dense Vectors and Scalar filtering.
Yes. Sparse & Dense Vectors and Scalar filtering.
1 (HNSW)
1 (HNSW)
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.
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.
Weaviate vs. Qdrant 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
Python, Go, Rust
Weaviate vs. Qdrant: 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
Qdrant
Open source Qdrant is maintained by the commercial company offering a cloud version of Qdrant.
License: Apache-2.0 license
