Chroma

Store text, images, or any data as embeddings (numerical representations that capture meaning), then query for 'things similar to this.' It's the database layer that makes RAG (retrieval-augmented generation, feeding relevant documents to an LLM) work. Apache 2.0, rewritten in Rust for performance. The developer experience is the selling point: `pip install chromadb`, four lines of Python, and you have a working vector store. No infrastructure needed to start.

Self-hosted is free with no feature restrictions. Chroma Cloud (hosted version) offers a free tier and paid plans starting at $30/month for 5M embeddings with usage-based pricing above that.

Solo: pip install and run in-process, zero ops. Small teams: self-host the server mode with Docker, minimal ops. Medium: evaluate Chroma Cloud vs self-hosted based on query volume. Large: self-host for control or Chroma Cloud for managed infrastructure.

The catch: Chroma optimizes for developer experience over raw performance at scale. If you're storing billions of vectors, Milvus or Qdrant handle that better. And the Rust rewrite is still maturing. Some edge cases and features are catching up to the Python-era version.