Chroma is an open-source (Apache 2.0) embedding database designed for developer simplicity and rapid prototyping of AI applications. Core library is free; Chroma Cloud in preview with pricing TBD. The platform emphasizes developer experience with Python-first design, simple APIs, and minimal configuration—install with pip, create collection, add embeddings in three lines of code. Supports in-memory, persistent local storage, and client-server deployment modes. Built-in embedding functions integrate with OpenAI, Cohere, Sentence Transformers, and custom models. Metadata filtering enables hybrid queries combining vector similarity with attribute filters. For text-to-SQL prototyping: fastest path to implementing RAG with schema and query example storage. Limitations include lack of production features (replication, sharding) available in Pinecone/Weaviate. Best suited for development, prototyping, and smaller-scale production deployments. Page should cover: quick start tutorial, deployment mode comparison, embedding function configuration, metadata filtering patterns, limitations for production, comparison with production vector databases, and migration path to larger solutions.