Chroma's pitch is simplicity above everything else: adding vector search to a Python or JavaScript application should take a few lines of code, not a database deployment. It can run embedded directly inside an application process for quick prototyping, as a local HTTP server for a slightly more production-like setup, or on Chroma Cloud when it's time to stop managing it yourself.
That progression, from embedded to self-hosted server to managed cloud, without switching APIs along the way, is the main reason it became the default choice for a lot of RAG tutorials and early-stage agent projects. It's less focused on the kind of large-scale, high-throughput search that Pinecone or Qdrant target, and more focused on getting a working vector search feature shipped quickly.
With around 29,000 GitHub stars as of September 2026 and Apache 2.0 licensing, it's mostly used for RAG prototyping and agent memory rather than massive production search indexes, though Chroma Cloud is clearly an attempt to capture some of that larger use case too.