Chroma DB Overview, Features & Pricing (2026)
Overview
Chroma DB is a vector-first database that stores embeddings and performs similarity queries for semantic search, recommendations, and LLM retrieval. It offers simple APIs and SDKs so developers can add fast nearest-neighbor search and manage embeddings. The service targets engineering and data teams building retrieval layers and production search experiences.
Use cases
- Semantic search across documents, knowledge bases, or websites.
- Context retrieval for LLM-powered assistants and chatbots.
- Recommendation systems built on embedding similarity.
- Prototyping and validating retrieval strategies for analytics and search features.
How it helps
- Reduce time to find relevant content with low-latency vector queries.
- Improve relevance of search and recommendations through semantic matching.
- Simplify integration into model pipelines to accelerate development cycles.
Key features
- Low-latency vector search to speed up retrieval and user response times.
- Scalable embedding storage for growing datasets and predictable performance.
- Developer-friendly SDKs and REST APIs for seamless integration into pipelines.
- Flexible query filters and metadata support for targeted semantic search.
- Experimentation tools to accelerate Data Science workflows and evaluation.
Pricing
Chroma DB is a paid product. Check the official site for current details.
Why to choose Chroma DB?
Chroma DB provides developer-focused APIs for efficient vector storage and low-latency retrieval, making it straightforward to add semantic search and retrieval to applications.
