Qdrant vs. Vespa
Compare Qdrant vs. Vespa by the following set of capabilities. We want you to choose the best database for you, even if it’s not us.
Qdrant vs. Vespa on Scalability
No. Only scale at the server level.
Yes.
Static sharding
Both
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.
Vespa
Vespa is a scalable search engine with a robust distributed architecture that supports horizontal scaling by adding more nodes. It features automatic sharding and data redistribution, allowing it to efficiently manage large datasets and high query volumes.
Qdrant vs. Vespa 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 (paged tensor attributes)
Yes. Sparse & Dense Vectors and Scalar filtering.
Yes, vector search & keyword seach
1 (HNSW)
HNSW, Hybrid HNSW-IF (Inverted File), paged tensor attributes
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.
Vespa
Vespa is a powerful search engine and vector database that can handle multiple searches simultaneously. It's great at vector search, text search, and searching through structured data.
Qdrant vs. Vespa 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
Yes.
Python, Go, Rust
Python, Java
Qdrant vs. Vespa: 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
Vespa
Vespa is a powerful search engine and vector database that can handle multiple searches simultaneously. It's great at vector search, text search, and searching through structured data.
Apache 2.0
