Why Companies Use Vector Databases

Companies are utilizing vector databases to reference large volumes of data with their ability to structure and sort unstructured data such as text, images, audio, video, and more. Vector databases are extremely useful due to their proficiency and efficiency in similarity searches and advanced searching, perfect for generative AI, LLM chatbots, and recommendation systems. The complexity of these databases ensures simplicity when accessing them even as they scale larger and larger.

With the surge of popularity in RAG or retrieval augmented generation in AI, vector databases have become more and more prevalent. Vector databases organize data by representing each data point as a vector, which is a mathematical object containing numerical values corresponding to different attributes or features of the data. Using numbers and math here is especially attractive since numerical data is the most comprehensive way to communicate accuracy to our computer hardware, not to mention our GPUs are innately computationally driven.

Open-Source vs. Proprietary Vector Databases

Choosing between open-source and proprietary vector databases involves several considerations. Open-source databases are cost-effective, flexible, and benefit from community support, regular updates, and enhanced security through transparent code. They also avoid vendor lock-in, offering long-term flexibility.

Conversely, proprietary databases provide comprehensive support and enterprise-level features. They offer user-friendly interfaces, easy deployment, and specific compliance guarantees, making them suitable for companies needing ready-to-use solutions. Proprietary options also ensure stability in product roadmaps and long-term planning.

Ultimately, the decision hinges on a company’s specific needs, resources, and strategic goals, balancing cost, customization, support, and ease of use. For free options, lets go over 5 Open-Source Vector Databases you can use to get started.

Top 3 Open-Source Vector Databases

Our top picks for Vector Databases in no particular order are Milvus, Weviate, and Qdrant for all being open source, free self-hostable, and the option for cloud management capabilities.

Milvus

Milvus is an open-source vector database developed by Zilliz, designed to manage massive amounts of vector data for AI and machine learning applications. It excels in handling high-dimensional data, offering fast and scalable similarity searches. Milvus supports various indexing methods like IVF, HNSW, and ANNOY, ensuring optimized performance for different use cases.

Milvus also integrates seamlessly with popular data processing frameworks such as TensorFlow, PyTorch, and Kubernetes. Its robust community and active development ensure continuous improvements and support.

Milvus is completely free to self-host and can be linked to cloud storage.

Weaviate

Weaviate is an open-source vector database built by SeMI Technologies, tailored for natural language processing and semantic search. It uses machine learning to understand the context of unstructured data, providing precise and relevant search results.

Weaviate offers a GraphQL API for easy data integration and manipulation, making it accessible for developers. It also includes built-in modules for various use cases, such as text, image, and video search, with the ability to create custom modules. Weaviate's focus on context and semantics sets it apart in the realm of vector databases.

Weaviate is completely free to self-host, but there is a cost per dimension to connect cloud storage or use Weviate’s cloud service.

Qdrant

Qdrant is an open-source vector database developed for real-time, high-performance search and recommendation systems. It is optimized for handling high-dimensional vectors and provides fast, accurate similarity searches.

Qdrant supports filtering and hybrid search, combining vector similarity with traditional search criteria. The database offers a user-friendly REST API, making it easy to integrate with various applications. With a focus on performance and scalability, Qdrant is ideal for dynamic, large-scale data environments.

Qdrant is completely free to self-host, but there is a cost per month to connect cloud storage or use Qdrant’s cloud, hybrid cloud, or private cloud service.

Vector Databases in Retrieval Augmented Generation

In a Retrieval Augmented Generation (RAG) model, the vector database plays a crucial role in storing and retrieving high-dimensional embeddings of textual data. Text data, such as documents or snippets, is transformed into vector embeddings using pre-trained models like BERT or GPT. These embeddings capture semantic meaning and are stored in a vector database.

When a query is posed to the AI system, it is also converted into a vector. The vector database then performs a vector search using techniques like an approximate nearest neighbor (ANN) to find relevant embeddings from the stored dataset. The retrieved documents are used to augment the input to a generative model by providing contextually relevant information for producing more accurate, informative, and contextually appropriate responses.

Integrating Vector Databases for Enterprise

Vector databases are essential for businesses harnessing AI and big data. They excel in managing vast unstructured data and providing advanced search capabilities, crucial for quick and accurate information retrieval. Their scalability allows them to handle growing data demands, while integration with AI and machine learning enables innovative applications. Real-time processing offers a competitive edge by delivering immediate insights.

Both open-source and proprietary options provide unique features, making vector databases a strategic choice for maximizing data potential. As technology evolves, its importance in data-driven decision-making will continue to grow. The best part about open-source options is the option to deploy on local hardware without having to pay a single dime in testing. Pair your local hardware, perform model prototyping, and create innovative AI solutions for deployment!