Choosing a Vector Database: Milvus vs. Chroma

In this comparison, we delve into the functionalities and performance of two open-source vector databases: Milvus and Chroma. We assess these platforms based on their capabilities in handling vector data storage, indexing, searching, scalability, and ecosystem support. Additionally, we examine the purpose-built features and performance trade-offs between Milvus and Chroma. Milvus is a versatile and comprehensive open-source vector database, offering extensive support for various index types, including 11 different options. It supports hybrid search operations and offers flexible in-memory and on-disk indexing configurations. Furthermore, Milvus ensures strong consistency and provides multi-language SDKs encompassing Python, Java, JavaScript, Go, C++, Node.js, and Ruby. On the other hand, Chroma is a relatively simpler vector database with a primary focus on enabling easy initiation and usage. It currently supports only the HNSW algorithm for its KNN search operations and lacks advanced features such as RBAC support. Additionally, it offers limited SDK options, primarily focusing on Python and JavaScript. While Chroma's simplicity may be adequate for specific applications, its limitations could restrict its adaptability across diverse use cases. With its comprehensive functionality and extensive feature set, Milvus emerges as a more versatile and scalable solution for addressing a broader spectrum of vector data management needs. In the upcoming Milvus 2.4 release, we plan to support the inverted index with tantivy, which promises significant enhancements to prefiltering speed. This update further solidifies Milvus as a cutting-edge open-source vector database that continues to evolve and adapt to emerging requirements in the AI ecosystem. In summary, while Chroma offers simplicity and ease of use, Milvus distinguishes itself with its comprehensive feature set, extensive index type support, and robust multi-language SDKs. As a result, Milvus remains a highly recommended open-source vector database for developers and organizations seeking to optimize their applications' performance, scalability, and data management capabilities. Milvus Lite, a lightweight alternative to the full Milvus version, has also been introduced. It aims to preserve the ease of initiation while retaining an extensive set of features, making it particularly useful for specific use cases such as integration into Python applications without adding extra weight or spinning up a Milvus instance in Colab or Notebook for quick experiments.