Qdrant vs TiDB

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.

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Qdrant vs. TiDB on Scalability

QdrantTiDB

Separation of storage and compute

❌

Separation of query and insertions

No. Only scale at the server level.

Yes.

Multi-replication

✔️

❌

Dynamic segment placement vs. static data sharding

Static sharding

Both

Cloud-native

✔️

❌

Billion-scale vector support

✔️

❌

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.

QdrantTiDB

Role-based Access Control (RBAC)

No. Authentication only

❌

Disk Index support

✔️

❌

Hybrid Search (ie Scalar filtering)

Yes. Sparse & Dense Vectors and Scalar filtering.

Yes, vector search & SQL search

Partitions/namespaces/logical groups

❌

Index type supported

1 (HNSW)

HNSW

Multiple in-memory indexes support

❌

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

QdrantTiDB

Purpose-built for Vectors

✔️

No, vector search is an add-on to TiDB Cloud serverless.

Tunable consistency

❌

Support for both stream and batch of vector data

❌

Binary Vector support

❌

Multi-language SDK

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.

Database rollback

❌

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