Chroma
Open-source and managed search infrastructure for AI applications
You are leaving botconomy for the provider’s website. Link verified 9/22/2026.
What this product helps you do
Chroma is open-source data infrastructure for AI retrieval with Python and TypeScript clients plus a managed serverless service. It supports document and metadata storage, user-selected embedding models, dense and sparse vector search, hybrid ranking, full-text and regex search, filters, multimodal records, collection forking, and cloud or self-hosted deployment.
Problems it solves
- AI applications need retrieval across documents, embeddings, and metadata
- Vector-only search can miss lexical or filtered matches
- Teams need a simple local development path that can move to managed infrastructure
- Operating indexes and storage at growing scale requires specialized database work
Chroma is Apache-2.0-licensed search infrastructure for AI applications. It stores documents, embeddings, and metadata and supports dense and sparse vectors, hybrid search, keyword and regex matching, metadata filters, and multimodal retrieval.
Developers can run Chroma locally or on their own infrastructure, or use Chroma Cloud for managed serverless databases. Cloud pricing combines plan fees with metered writes, storage, queries, and network output, while team and enterprise tiers add support, capacity, and deployment controls.
Information
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Chroma
Open-source and managed search infrastructure for AI applications
Chroma is Apache-2.0-licensed search infrastructure for AI applications. It stores documents, embeddings, and metadata and supports dense and sparse vectors, hybrid search, keyword and regex matching, metadata filters, and multimodal retrieval.
Developers can run Chroma locally or on their own infrastructure, or use Chroma Cloud for managed serverless databases. Cloud pricing combines plan fees with metered writes, storage, queries, and network output, while team and enterprise tiers add support, capacity, and deployment controls.
What this product helps you do
Chroma is open-source data infrastructure for AI retrieval with Python and TypeScript clients plus a managed serverless service. It supports document and metadata storage, user-selected embedding models, dense and sparse vector search, hybrid ranking, full-text and regex search, filters, multimodal records, collection forking, and cloud or self-hosted deployment.
Problems it solves
- AI applications need retrieval across documents, embeddings, and metadata
- Vector-only search can miss lexical or filtered matches
- Teams need a simple local development path that can move to managed infrastructure
- Operating indexes and storage at growing scale requires specialized database work
Information
You are leaving botconomy for the provider’s website. Link verified 9/22/2026.
Related AI products
Weaviate
Weaviate stores data objects and vector embeddings for semantic and hybrid search, retrieval-augmented generation, and agent workflows, with open-source self-hosting and managed cloud plans.
MagickML
MagickML provides a visual node builder for creating AI-agent workflows, combining model choice, document processing, custom nodes, event-driven execution, integrations, testing, and deployment with open-source code and hosted metered billing.
Milvus
Milvus is an Apache-2.0 vector database for storing vectors and related scalar data, running approximate nearest-neighbor search, and scaling from an embedded local library to standalone or distributed deployments.