How To Evaluate a Vector Database?

In the data-driven world, the exponential growth of unstructured data has led to the rise of vector databases. These powerful tools specialize in storing, indexing, and searching unstructured data through high-dimensional numerical representations known as vector embeddings. They are used for building recommender systems, chatbots, and applications for searching similar images, videos, and audio. When selecting a vector database, scalability, functionality, and performance are the top three most crucial metrics to consider. Scalability is essential for accommodating growing data demands effectively, while functionality includes both vector-oriented features like support for multiple index types and database-oriented features such as Change Data Capture (CDC) and multi-tenancy support. Performance is evaluated using benchmarking tools like ANN-Benchmark and VectorDBBench, which measure recall rate, QPS, latency, and other metrics. Various vector search technologies are available beyond vector databases, including vector search libraries, lightweight vector databases, vector search plugins, and purpose-built vector databases. Each type has its strengths and weaknesses, so the choice depends on specific business needs.