Chroma vs OpenSearch

Chroma vs. OpenSearch

Compare Chroma vs. OpenSearch 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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Chroma vs. OpenSearch on Scalability

ChromaOpenSearch

Separation of storage and compute

❌

Separation of query and insertions

No. Can not scale beyond single node.

Yes.

Multi-replication

❌

Dynamic segment placement vs. static data sharding

No distributed data replacement

Both

Cloud-native

❌

Billion-scale vector support

❌

Chroma scalability

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

OpenSearch

OpenSearch supports horizontal scaling, cluster management optimizations, and efficient shard allocation, making it suitable for handling large datasets and high query loads effectively.

Chroma vs. OpenSearch 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.

ChromaOpenSearch

Role-based Access Control (RBAC)

❌

Disk Index support

❌

Hybrid Search (ie Scalar filtering)

Yes with scalar filtering

yes, vector search & keyword search & scalar filtered search

Partitions/namespaces/logical groups

❌

Index type supported

1 (HNSW)

ANN

Multiple in-memory indexes support

❌

Chroma functionality

Chroma uses HNSW algorithm to support kNN search.

OpenSearch

OpenSearch supports:

Vectors with up to 16,000 dimensions.
Vector generation through external libraries or directly within OpenSearch.
Both binary and dense vectors.
Cosine Similarity, Inner Product, and L2 Distance (Euclidean).
Integration with multiple engines, including NMSLIB, Faiss, and Lucene, to facilitate vector indexing and searching.

Chroma vs. OpenSearch 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

ChromaOpenSearch

Purpose-built for Vectors

✔️

No. Vector search is an add-on to OpenSearch.

Tunable consistency

❌

Support for both stream and batch of vector data

❌

Binary Vector support

❌

Multi-language SDK

Python, JavaScript

Java, Python, JavaScript, Go, and .Net

Database rollback

❌

Chroma vs. OpenSearch: what’s right for me?

Chroma

Chroma is maintained by a single commercial company offering a non-scalable single node. License: Apache-2.0 license

OpenSearch

OpenSearch is an open-source software suite for search, analytics, security monitoring, and observability applications. It is not purpose-built for vector storage and search workloads but introduces a vector search plugin to provide this capability. Amazon OpenSearch Service is an AWS-managed service for OpenSearch.

Apache 2.0