This Chroma vs. Milvus comparison was last updated on February 5, 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 spurred a 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 databases with integrated vector search plugins, lightweight vector databases, and purpose-built vector databases. Chroma is a noteworthy lightweight vector database, prioritizing ease of use and development-friendliness. In contrast, Milvus, an open-source purpose-built vector database, excels in handling large-scale, high-performance, and low-latency applications.
While both databases proficiently manage vector data, they cater to distinct needs. Chroma 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 Chroma and Milvus 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 on Scalability
Milvus outperforms Chroma in elastic and horizontal scalability.
|Separation of storage and compute
|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
|Billion/trillion-scale vector support
|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 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 on Functionality
In terms of functionality, both Milvus and Chroma offer a suite of features designed to manage and retrieve vector data efficiently.
|Role-based Access Control (RBAC)
|Disk Index support
|Hybrid Search (ie Scalar filtering)
|Yes with scalar filtering
|Yes with scalar filtering
|Index type supported
|11 indexes: FLAT, IVF_FLAT, IVF_SQ8, IVF_PQ, HNSW, BIN_FLAT, BIN_IVF_FLAT, DiskANN, GPU_IVF_FLAT, GPU_IVF_PQ, and ScaNN
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 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 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 11 indexes, including FLAT, IVF_FLAT, IVF_SQ8, IVF_PQ, HNSW, BIN_FLAT, BIN_IVF_FLAT, DiskANN, GPU_IVF_FLAT, GPU_IVF_PQ, and ScaNN. 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 data management needs.
Milvus vs. Chroma on open-source foundations and purpose-built features
Both Milvus and Chroma are open-source vector databases licensed under Apache 2.0.
|Purpose-built for Vectors
|Support for both stream and batch of vector data
|Binary Vector support
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 25,000+ 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 Lite vs. Milvus vs. Chroma
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, and it was contributed by Bin Ji, one of the most active members of the Milvus community. It preserves the ease of initiation while retaining an extensive set of features.
In comparison to the full Milvus version, Milvus Lite has the following advantages and benefits:
- It is easy to start and use, offering integration into Python applications without adding extra weight.
- It is a self-contained solution, eliminating dependencies by leveraging the standalone Milvus' capability to work with embedded Etcd and local storage.
- Milvus Lite can be imported as a Python library or utilized as a command-line interface (CLI)-based standalone server.
- Its compatibility with Google Colab and Jupyter Notebook further enhances its flexibility.
Milvus Lite proves particularly useful in the following use cases:
- When you prefer to use Milvus without container techniques and tools like Milvus Operator, Helm, or Docker Compose.
- When you don't require virtual machines or containers for using Milvus.
- When you want to incorporate Milvus features into your Python applications.
- When you want to spin up a Milvus instance in Colab or Notebook for a quick experiment.
Milvus Lite emerges as a versatile and user-friendly solution, offering simplicity without compromising functionality.
Note: We do not recommend using Milvus Lite in any production environment or if you require high performance, strong availability, or high scalability. Instead, consider using Milvus clusters or fully-managed Milvus on Zilliz Cloud for production.
For more detailed information about Milvus Lite, see our blog Introducing Milvus Lite: the Lightweight Version of Milvus.
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