Qdrant vs Chroma

Qdrant vs. Chroma

Compare Qdrant vs. Chroma 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. Chroma on Scalability

QdrantChroma

Separation of storage and compute

❌

Separation of query and insertions

No. Only scale at the server level.

No. Can not scale beyond single node.

Multi-replication

✔️

❌

Dynamic segment placement vs. static data sharding

Static sharding

No distributed data replacement

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.

Chroma scalability

Without any distributed data replacement, Chroma is not able to scale beyond a single node

Qdrant vs. Chroma 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.

QdrantChroma

Role-based Access Control (RBAC)

No. Authentication only

❌

Disk Index support

✔️

❌

Hybrid Search (ie Scalar filtering)

Yes. Sparse & Dense Vectors and Scalar filtering.

Yes with scalar filtering

Partitions/namespaces/logical groups

❌

Index type supported

1 (HNSW)

Multiple in-memory indexes support

❌

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.

Chroma functionality

Chroma uses HNSW algorithm to support kNN search.

Qdrant vs. Chroma 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: