Choosing a Vector Database: Milvus vs. Chroma DB
This Milvus vs. Chroma DB comparison was last updated on July 19, 2024. To provide you with the latest findings, this blog will be regularly updated with the latest information.
The rise of large language models (LLMs) like ChatGPT has revolutionalized the world of AI and machine learning, spurring demand for vector databases serving as the long-term memory for these models. This demand has led to the development of various vector search systems, spanning traditional relational databases with integrated vector search plugins, lightweight vector databases, vector search libraries like FAISS, and purpose-built vector databases.
Chroma vector database is a noteworthy lightweight vector database, prioritizing ease of use and development-friendliness. In contrast, Milvus, an AI native, open-source purpose-built vector database, excels in handling large-scale, high-performance, and low-latency applications.
While both databases proficiently store and retrieve vector embeddings generated by embedding models, they cater to distinct needs. Chroma DB is a good choice for developers dealing with datasets smaller than one million vectors, prioritizing quick and straightforward implementation. On the other hand, Milvus, crafted by Zilliz, is specifically designed for applications demanding extreme scale up to billions or even trillions of vector points, robust searching capability, and quick response times. Its architecture is finely tuned for these critical performance metrics, positioning Milvus as a robust and innovative solution for the most demanding vector database applications.
This comparison between Milvus and Chroma vector database aims to delve into these distinctions and provide a comprehensive understanding of their respective capabilities. We’ll also introduce Milvus Lite, a lightweight version of Milvus, and compare it with Chroma.
Milvus vs. Chroma DB on Horizontal Scalability
Milvus outperforms Chroma in elastic and horizontal scalability.
| Features | Milvus | Chroma |
|---|---|---|
| Separation of storage and compute | Yes | Yes |
| Separation of query and insertions | Yes. At the component level (which provides more fine-grained scalability). | No. Can not scale beyond single node. |
| Dynamic segment placement vs. static data sharding | Dynamic segment placement | No distributed data replacement |
| Cloud-native | Yes | No |
| Billion/trillion-scale vector support | Yes | No. It can only handle up to one million vectors. |
Milvus features a distributed system with separate computing and storage components, providing seamless scalability up to billions or even trillions of vectors from various embedding models to accommodate increasing business needs. This architecture also allows independent scaling of computing and storage resources, offering flexibility and cost-effectiveness aligned with evolving business requirements.
Moreover, Milvus can dynamically allocate new nodes to an action group, speeding up operations or reducing the number of nodes, thus freeing resources for other actions. Dynamically allocating nodes allows for easier scaling and resource planning and guarantees low latency and high throughput.
Conversely, while prioritizing simplicity and ease of use, Chroma grapples with scalability limitations, with a storage upper limit of up to one million vector points. Its confinement to a single node and the absence of distributed data replacement hinder its suitability for applications with increasing demands.
Milvus vs. Chroma DB on Functionality
In terms of functionality, both Milvus and Chroma offer a suite of features designed to manage and retrieve vector embeddings efficiently.
| Features | Milvus | Chroma |
|---|---|---|
| Role-based Access Control (RBAC) | Yes | No |
| Disk Index support | Yes | No |
| Hybrid Search (ie Scalar filtering) | Yes with scalar filtering | Yes with scalar filtering |
| Partitions/namespaces/logical groups | Yes | No |
| Index type supported | 14 indexes: FLAT, IVF_FLAT, IVF_SQ8, IVF_PQ, HNSW, BIN_FLAT, BIN_IVF_FLAT, DiskANN, ScaNN, SPARSE_INVERTED_INDEX, SPARSE_WAND, CAGRA, GPU_IVF_FLAT, and GPU_IVF_PQ), HNSW |
Milvus distinguishes itself with robust support for role-based access control (RBAC), providing an effective mechanism for data access management. This feature proves particularly valuable for enterprise-grade applications, enhancing data isolation and protection capabilities. Milvus further incorporates multiple in-memory indexes and table-level partitions, ensuring high-performance retrieval of relevant documents in the form of vector embeddings in real-time use cases. Additionally, the platform offers flexibility with on-disk indexes, providing choices for developers and businesses more sensitive to cost considerations and not requiring high query per second (QPS).
On the other hand, Chroma lacks RBAC support, which could limit its data access management and protection capabilities. The platform primarily relies on basic in-memory indexing, presenting a more straightforward approach but with potential limitations for applications with more complex requirements.
Milvus and Chroma enable hybrid search operations, allowing users to conduct vector similarity searches with efficient metadata filtering before and after the search operation. In the upcoming Milvus 2.4, we will support the inverted index with tantivy, promising a substantial boost in prefiltering speed.
Another notable difference between Milvus and Chroma lies in their index-type support. Milvus supports an extensive array of 14 indexes, including 14 indexes, including FLAT, IVF_FLAT, IVF_SQ8, IVF_PQ, HNSW, BIN_FLAT, BIN_IVF_FLAT, DiskANN, ScaNN, SPARSE_INVERTED_INDEX, SPARSE_WAND, CAGRA, GPU_IVF_FLAT, and GPU_IVF_PQ. In contrast, Chroma relies solely on the HNSW algorithm for its KNN search.
While Chroma's features may be adequate for specific applications, its limitations could impact its adaptability across diverse use cases. With its comprehensive functionality, Milvus is a versatile solution that addresses a broader spectrum of vector embedding management needs.
Milvus vs. Chroma DB on Open-source Foundations and Purpose-built Features
Both Milvus and Chroma are open-source databases licensed under Apache 2.0.
| Features | Milvus | Chroma |
|---|---|---|
| Purpose-built for Vectors | Yes | Yes |
| Tunable consistency | Yes | No |
| Support for both stream and batch of vector data | Yes | No |
| Binary Vector support | Yes | No |
| Multi-language SDK | Python, Java, JavaScript, Go, and Node.js SDKs Fully supported | Python, Javascript |
Milvus was built by Zilliz engineers in 2019. It was later donated to the LF AI & Data Foundation in 2021 to enhance its accessibility to a broader range of developers and organizations. Milvus boasts 27,000+ GitHub stars, 260+ community contributors, and over 10 million docker image downloads.
Chroma is maintained by a single commercial entity called Chroma. With over 10,000 GitHub stars, Chroma initially focused on analytical workloads over embeddings. However, with the emergence of AI and LLMs like ChatGPT, it transitioned into a general-purpose embedding store.
Milvus and Chroma offer purpose-built features to address specific needs in vector data applications. Milvus provides a comprehensive feature set, including tunable consistency, support for stream and batch processing of vector data, binary vector support, and a multi-language SDK encompassing Python, Java, Go, C++, Node.js, and Ruby.
Chroma prioritizes simplicity and ease of use over extensive features, resulting in a more constrained offering. It provides a limited selection of SDKs, primarily focusing on Python and JavaScript.
Milvus Lite vs. Milvus vs. Chroma DB on Ease of Use and Ecosystem
Chroma prioritizes easy initiation and usage. However, this simplicity comes with trade-offs, including compromised search performance, scalability limitations, and the exclusion of many beneficial database management features.
Milvus Lite is a lightweight alternative to Milvus that runs locally within your Python application. It preserves the ease of initiation while retaining an extensive set of features. Based on the popular open-source Milvus vector database, Milvus Lite reuses the core components for vector indexing and query parsing while removing elements designed for high scalability in distributed systems. This design makes a compact and efficient solution ideal for environments with limited computing resources, such as laptops, Jupyter Notebooks, and mobile or edge devices.
Milvus Lite integrates with various AI development stacks like LangChain and LlamaIndex, enabling its use as a vector store in Retrieval Augmented Generation (RAG) pipelines for efficient retrieval of vector embeddings without the need for server setup. Simply run pip install pymilvus (version 2.4.3 or above) to incorporate it into your AI application as a Python library.
Milvus Lite shares the Milvus API, ensuring that your client-side code works for both small-scale local deployments and Milvus servers deployed on Docker or Kubernetes with billions of vectors.
Note: Milvus Lite is good for starting with vector similarity search or building demos and prototypes. For a production use case, we recommend using Milvus on Docker and Kubenetes or considering the fully managed Milvus on Zilliz Cloud.
For more detailed information about Milvus Lite, refer to the following resources:
What’s next?
VectorDB Comparison: Compare Any Open Source Vector Database to An Alternative
Milvus update: What’s New in Milvus 2.4.0?
Fully managed Milvus: Try Zilliz Cloud for Free
Webinar: Unlocking the power of vector search in Zilliz Cloud
RAG: What is RAG?
Zilliz Cloud latest update: Zilliz Cloud Available in 11 Regions across 3 Major Cloud Providers
Fendy FengFendy Feng is the Technical Marketing Writer at Zilliz. She has extensive experience developing and enhancing the impact of open-source projects in various global markets by producing high-quality, tailored content. Before joining Zilliz, Fendy worked as a Content Strategist at PingCAP, a fast-growing E-Series startup renowned for its open-source distributed SQL database.
- Milvus vs. Chroma DB on Horizontal Scalability
- Milvus vs. Chroma DB on Functionality
- Milvus vs. Chroma DB on Open-source Foundations and Purpose-built Features
- Milvus Lite vs. Milvus vs. Chroma DB on Ease of Use and Ecosystem
- What’s next?
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