If you're looking for a Chroma alternative, one of the most popular options is Vespa, an online service that makes it easier to apply AI to large data sets. It includes a single search engine and vector database that supports vector search, lexical search and structured data. That means it can be used to build production search applications at any scale, with fast vector search and filtering combined with machine-learned models, and scalable and efficient machine-learned model inference.
Another top contender is Jina, which adds search power with a collection of AI tools, in particular for multimodal data. Jina includes multimodal and bilingual embeddings, rerankers, LLM-readers and prompt optimizers. It supports more than 100 languages and has auto fine-tuning for embeddings. The system also can handle multimodal data structures and serve large multimodal models with a lightweight Python vector database.
If you need high performance and scalability, check out Zilliz, a managed vector database service based on open-source Milvus. It's designed for high speed for vector retrieval and high scalability and reliability. Zilliz can be used for a variety of use cases, including retrieval, augmented generation and recommender systems, with built-in embedding pipelines and support for multiple clouds.
Last, Algolia offers a powerful AI-powered search foundation for building personalized search experiences. It includes a range of tools for fast, scalable and easy-to-use search that combines keyword search with vector understanding. Algolia is used in a variety of industries and use cases, including enterprise search, headless commerce, mobile search and voice search, and offers flexible pricing and abundant documentation.