An Honest Comparison of Open Source Vector Databases
We will explore their use cases, key features, performance metrics, supported programming languages, and more to provide a comprehensive and unbiased overview of each database.
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Vector databases offer a wide range of benefits, particularly in generative artificial intelligence (AI), and more specifically, large language models (LLMs). These benefits can range from advanced indexing to accurate similarity searches, helping to deliver powerful, state-of-the-art projects,
In this article, we will provide an honest comparison of three open-source vector databases that have established an impressive reputation—Chroma, Milvus, and Weaviate. We will explore their use cases, key features, performance metrics, supported programming languages, and more to provide a comprehensive and unbiased overview of each database.
What Are Vector Databases?
In its most simplistic definition, a vector database stores information as vectors (vector embeddings), which are a numerical version of a data object.
As such, vector embeddings are a powerful method of indexing and searching across very large and unstructured or semi-unstructured datasets. These datasets can consist of text, images, or sensor data and a vector database orders this information into a manageable format.
Vector databases work using high-dimensional vectors which can contain hundreds of different dimensions, each linked to a specific property of a data object. Thus creating an unrivaled level of complexity.
Not to be confused with a vector index or a vector search library, a vector database is a complete management solution to store and filter metadata in a way that is:
- Is completely scalable
- Can be easily backed up
- Enables dynamic data changes
- Provides a high level of security
The Benefits of Using Open Source Vector Databases
Open source vector databases provide numerous benefits over licensed alternatives, such as:
- They are a flexible solution that can be easily modified to suit specific needs, unlike licensed options which are typically designed for a particular project.
- Open source vector databases are supported by a large community of developers who are ready to assist with any issues or provide advice on how projects could be improved.
- An open-source solution is budget-friendly with no licensing fees, subscription fees, or any unexpected costs during the project.
- Due to the transparent nature of open-source vector databases, developers can work more effectively, understanding every component and how the database was built.
- Open source products are constantly being improved and evolving with changes in technology as they are backed by active communities.
Open Source Vector Databases Comparison: Chroma Vs. Milvus Vs. Weaviate
Now that we have an understanding of what a vector database is and the benefits of an open-source solution, let’s consider some of the most popular options on the market. We will focus on the strengths, features, and uses of Chroma, Milvus, and Weaviate, before moving on to a direct head-to-head comparison to determine the best option for your needs.
Chroma is designed to assist developers and businesses of all sizes with creating LLM applications, providing all the resources necessary to build sophisticated projects. Chroma ensures a project is highly scalable and works in an optimal way so that high-dimensional vectors can be stored, searched for, and retrieved quickly.
It has grown in popularity due to its reputation as being an extremely flexible solution, with a wide range of deployment options. In addition, Chroma can be deployed directly on the cloud or it can be run on-site, making it a viable option for any business, regardless of its IT infrastructure.
Multiple data types and formats are also supported by Chroma, making it suitable for almost any application. However, one of Chroma’s key strengths is its support for audio data, making it a top choice for audio-based search engines, music recommendation applications, and other sound-based projects.
Milvus has gained a strong reputation in the world of ML and data science, boasting impressive capabilities in terms of vector indexing and querying. Utilizing powerful algorithms, Milvus offers lightning-fast processing and data retrieval speeds and GPU support, even when working with very large datasets. Milvus can also be integrated with other popular frameworks such as PyTorch and TensorFlow, allowing it to be added to existing ML workflows.
This open-source vector database can be used across a wide range of industries and in a large number of applications. Another prominent example involves eCommerce, where Milvus can power accurate recommendation systems to suggest products based on a customer’s preferences and buying habits.
It’s also suitable for image/ video analysis projects, assisting with image similarity searches, object recognition, and content-based image retrieval. Another key use case is natural language processing (NLP), providing document clustering and semantic search capabilities, as well as providing the backbone to question and answer systems.
The third open source vector database in our honest comparison is Weaviate, which is available in both a self-hosted and fully-managed solution. Countless businesses are using Weaviate to handle and manage large datasets due to its excellent level of performance, its simplicity, and its highly scalable nature.
Capable of managing a range of data types, Weaviate is very flexible and can store both vectors and data objects which makes it ideal for applications that need a range of search techniques (E.G. vector searches and keyword searches).
In terms of its use, Weaviate is perfect for projects like Data classification in enterprise resource planning software or applications that involve:
- Similarity searches
- Semantic searches
- Image searches
- eCommerce product searches
- Recommendation engines
- Cybersecurity threat analysis and detection
- Anomaly detection
- Automated data harmonization
Now we have a brief understanding of what each vector database can offer, let’s consider the finer details that set each open source solution apart in our handy comparison table.
|Open Source Status||Yes - Apache-2.0 license||Yes - Apache-2.0 license||Yes - BSD-3-Clause license|
|Publication Date||February 2023||October 2019||January 2021|
|Use Cases||Suitable for a wide range of applications, with support for multiple data types and formats. Specializes in Audio-based search projects and image/video retrieval.||Suitable for a wide range of applications, with support for a plethora of data types and formats. Perfect for eCommerce recommendation systems, natural language processing, and image/video-based analysis||Suitable for a wide range of applications, with support for multiple data types and formats. Ideal for Data classification in enterprise resource planning software.|
|Key Features||Impressive ease of use. Development, testing, and production environments all use the same API on a Jupyter Notebook. Powerful search, filter, and density estimation functionality.||Uses both in-memory and persistent storage to provide high-speed query and insert performance. Provides automatic data partitioning, load balancing, and fault tolerance for large-scale vector data handling. Supports a variety of vector similarity search algorithms.||Offers a GraphQL-based API, providing flexibility and efficiency when interacting with the knowledge graph. Supports real-time data updates, to ensure the knowledge graph remains up-to-date with the latest changes. Its schema inference feature automates the process of defining data structures.|
|Community and Industry Recognition||Strong community with a Discord channel available to answer live queries.||Active community on GitHub, Slack, Reddit, and Twitter. Over 1000 enterprise users. Extensive documentation.||Dedicated forum and active Slack, Twitter, and LinkedIn communities. Plus regular Podcasts and newsletters. Extensive documentation.|
Each open-source vector database in our honest comparison guide is powerful, scalable, and completely free. This can make choosing the perfect solution a little difficult but the process can be made easier by knowing the exact project you are working on and the level of support required.
Chroma is the newest solution and is not as well backed as the other two in terms of community support, however, its ease of use and flexibility make it a great option, especially for projects that involve audio search.
Milvus has the highest GitHub Star rating and strong community support, with an impressive number of enterprise businesses trusting this vector database to meet their needs. Therefore, Milvus is a good choice for natural language processing and image/ video analysis projects.
Finally, Weaviate offers self-hosted and fully managed solutions, with extensive documentation and support available. A key use case is data classification in enterprise resource planning software, but this solution is perfect for a range of projects.
Nahla Davies is a software developer and tech writer. Before devoting her work full time to technical writing, she managed—among other intriguing things—to serve as a lead programmer at an Inc. 5,000 experiential branding organization whose clients include Samsung, Time Warner, Netflix, and Sony.