chroma

at. On aesthetics, that’s always been our aesthetic, I think a lot of developer tools are leaning into the nostalgia of the early PC boom during this AI boom (fun fact, all the icons on our homepage are done by hand!). On differences, we support optimized regexes vs full-scans, lending better performance. We also support trigram based full-text search which can often be useful for scenarios which need substring matches. We also support forking, which allows for cheap copy-on-write clones of your data, great for dataset versioning and tracking git repos with minimal cost. We've been building with support for generic sparse vectors (in beta) which enables techniques like SPLADE to be used, rather than just BM25. You can also run Chroma locally, enabling low-latency local workflows. This is great for AI apps where you need to iterate on a dataset until it passes evals, and then push it up to the cloud. Chroma is Apache 2.0 open source - https://github.com/chroma-core/chroma and has a massive developer community behind it. Customers can run embedded, single-node and distributed Chroma themselves. We've suffered from depending on closed-source database startups and wanted to give developers using Chroma confidence in the longevity of their choice. Lastly, we are building with AI workloads front and center and this changes what you build, how you build it and who you build for in the long term. We think search is changing and that the primary consumer of the search API for AI applications is shifting from human engineers, to language models. We are building some exciting things in this direction, more on that soon.