Weaviate vs Chroma

Weaviate vs. Chroma

Compare Weaviate 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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Weaviate vs. Chroma on Scalability

WeaviateChroma

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

❌

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.

Chroma scalability

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

Weaviate 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.

WeaviateChroma

Role-based Access Control (RBAC)

Coming soon

❌

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

❌

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.

Chroma functionality

Chroma uses HNSW algorithm to support kNN search.

Weaviate 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: