An honest comparison of open source vector databases

Vector databases offer a wide range of benefits, particularly in generative artificial intelligence (ai) and, more specifically, large language models (LLM). These benefits can range from advanced indexing to precise similarity searches, helping to generate powerful, cutting-edge projects.

In this article, we will provide an honest comparison of three open source vector databases that have established impressive reputations: Chroma, Milvus, and Weaviate. We’ll explore their use cases, key features, performance metrics, supported programming languages, and more to provide a complete and unbiased overview of each database.

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 on very large, unstructured or semi-unstructured data sets. These data sets can consist of text, images, or sensor data, and a vector database organizes this information into a manageable format.

Vector databases work using high-dimensional vectors that can contain hundreds of different dimensions, each linked to a specific property of a data object. Thus creating an unmatched level of complexity.

Not to be confused with a vector index or vector search library, a vector database is a complete management solution for storing and filtering metadata in one way:

• It is completely scalable
• Can be easily backed up
• Allows dynamic data changes.
• Provides a high level of security.

The benefits of using open source vector databases

Open source vector databases offer numerous benefits over licensed alternatives, including:

• They are a flexible solution that can be easily modified to meet specific needs, unlike licensed options that are typically designed for a particular project.
• Open source vector databases are supported by a large community of developers who are ready to help with any problems or provide advice on how projects could be improved.
• An open source solution is economical and no license fees, subscription fees or unexpected costs During the project.
• Due to the transparent nature of open source vector databases, developers can work more efficientlyunderstanding each component and how the database was built.
• Open source products are constantly improving and evolving with changes in technology as they are supported by active communities.

Now that we understand 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’ll focus on the strengths, features, and uses of Chroma, Milvus, and Weaviate, before moving on to a direct comparison to determine the best option for your needs.

1. chroma

Chroma is designed to help developers and companies of all sizes create LLM applications, providing all the resources necessary to build sophisticated projects. Chroma ensures that a project is highly scalable and performs optimally so that high-dimensional vectors can be stored, searched, and retrieved quickly.

It has gained popularity due to its reputation for being an extremely flexible solution, with a wide range of implementation options. Additionally, Chroma can be deployed directly to the cloud or run on-premises, making it a viable option for any business, regardless of its IT infrastructure.

Use cases

Chroma also supports multiple data types and formats, making it suitable for almost any application. However, one of Chroma’s key strengths is its support for audio data, making it the best choice for audio-based search engines, music recommendation apps, and other sound-based projects.

2. The kite

Milvus has built a strong reputation in the world of machine learning and data science, with impressive capabilities in terms of vector indexing and querying. Using powerful algorithms, Milvus offers ultra-fast data processing and retrieval speeds. and GPU support, even when working with very large data sets. Milvus can also integrate with other popular frameworks such as PyTorch and TensorFlow, allowing it to be added to existing ML workflows.

Use cases

Milvus is recognized for its similarity search and analysis capabilities, with extensive support for multiple programming languages. This flexibility means that developers are not limited to backend operations and can even perform tasks normally reserved for server-side languages ​​on the front-end. For example, you could generate PDF files with JavaScript while leveraging real-time data from Milvus. This opens new avenues for application development, especially for educational content and accessibility-focused applications.

This open source vector database can be used in a wide range of industries and a large number of applications. Another notable example is in e-commerce, where Milvus can power accurate recommendation systems to suggest products based on customer preferences and purchasing habits.

It is also suitable for image/video analysis projects, helping with image similarity searches, object recognition, and content-based image retrieval. Another key use case is natural language processing (NLP), which provides semantic search and document clustering capabilities, as well as providing the backbone of question and answer systems.

3. Knit

The third open source vector database in our honest comparison is Weaviate, which is available at a self-hosted and fully managed solution. Countless companies are using Weaviate to handle and manage large data sets due to its excellent level of performance, simplicity, and highly scalable nature.

Capable of handling a variety of data types, Weaviate is very flexible and can store both vectors and data objects, making it ideal for applications that need a variety of search techniques (e.g. vector searches and word searches). clue).

Use cases

In terms of 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
• Analysis and detection of cybersecurity threats
• Anomaly detection
• Automated data harmonization

Now that we briefly understand what each vector database can offer, let’s consider the finer details that distinguish each open source solution in our handy comparison table.

Comparison table

Every 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 if you know the exact project you are working on and the level of support required.

Chroma is the newest solution and is not supported by the other two in terms of community support; However, its ease of use and flexibility make it an excellent choice, especially for projects involving audio searching.

Milvus has the highest GitHub star rating and strong community support, with an impressive number of companies relying on 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 variety of projects.

Nahla Davies is a software developer and technology writer. Before dedicating her full-time job to technical writing, she managed, among other interesting things, to work as a lead programmer at an Inc. 5,000 experiential brand organization whose clients include Samsung, Time Warner, Netflix, and Sony.