Chroma

Overview & Role

Chroma is an open-source embedding database designed for AI applications. It simplifies the process of storing, indexing, and searching embeddings, making it easier to build applications like RAG (Retrieval Augmented Generation) with large language models.

Domain Classification & Tags

Read Performance Profile

Throughput: Varies significantly based on index size, query complexity (e.g., number of nearest neighbors, filtering), and underlying storage. No single, universally applicable benchmark. For small to medium datasets (e.g., millions of vectors), reads can be very fast (hundreds to thousands of QPS).

Payload Size: Query vector size (typically 768 to 1536 dimensions for common embeddings) and the number of results requested (k). Result payload includes vector ID, distance, and optionally metadata and the original document text.

Processing Latency: Highly dependent on index size, hardware, and query complexity. Milliseconds for typical k-NN queries on well-indexed data (e.g., 10-100ms for millions of vectors). Can increase with complex filtering or very large k values.

Write Performance Profile

Throughput: Varies based on batch size, vector dimensionality, and underlying storage. Can range from hundreds to thousands of vectors per second for single-node deployments. Distributed deployments can scale higher. Batching writes significantly improves throughput.

Payload Size: Individual vector size (e.g., 768-1536 dimensions), associated metadata, and the document text itself (if stored).

Processing Latency: Typically tens to hundreds of milliseconds per write operation (especially if not batched). Batching reduces per-item latency. Indexing overhead contributes to latency.