Qdrant vs. TiDB
Compare Qdrant vs. TiDB by the following set of capabilities. We want you to choose the best database for you, even if it’s not us.
Qdrant vs. TiDB 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.
TiDB
TiDB is designed with scalability as one of its core features. It offers both horizontal and vertical scaling capabilities to handle growing workloads and data volumes.
Qdrant vs. TiDB 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. Sparse & Dense Vectors and Scalar filtering.
Yes, vector search & SQL search
1 (HNSW)
HNSW
No. HNSW only
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.
TiDB
TiDB offers vector search through its serverless cluster and supports vectors with a maximum dimension of 16,000. The Vector data type in TiDB is designed to store single-precision floating-point numbers (Float32). It only supports cosine distance and L2 distance for similarity measurement.
Qdrant vs. TiDB 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
No, vector search is an add-on to TiDB Cloud serverless.
Python, Go, Rust
No. TiDB does not provide specific SDKs. Instead, it is designed to be compatible with MySQL, which means TiDB can be used with any language with MySQL client or driver support.
Qdrant vs. TiDB: 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
TiDB
TiDB is an open-source distributed SQL database for OLAP and OLTP workloads. It now offers a vector search capability (in public beta) as an add-on to its SaaS solution, TiDB Cloud Serverless.
Apache 2.0
Compare Qdrant 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.