Discover the Best Vector Database for Your Needs: A Comprehensive Consideration Guide

Vector database plays a critical role in machine learning model and generative AI prompting. It boosts the outcome accuracy, personlizes results, lower response latency, and eventually enhances your AI application user experiences.

Thus in this article, we are going to list out what aspects you need to consider when selecting a vector database cloud or service provider.

Table of Contents: Discover the Best Vector Database for Your Needs: A Comprehensive Consideration Guide

• What is Vector Database and Why We need it
• Speed Performance
• Scalability
• Relevancy
• Cost-efficiency
• Wrap-up

What is Vector Database & Why

Vector database, a burgeoning trend in the AI landscape, offers a groundbreaking solution for managing and analyzing high-dimensional data. Unlike a traditional relational database, which struggles to represent complex relationships and patterns, vector databases leverage mathematical structures to capture and understand data in a more nuanced way, like comparing the distance between the question and the answer. This enables them to support emerging technologies like semantic discovery, image search, and personalized recommendations.

The proliferation of vector databases has been driven by the exponential growth in data volume, especially in some sectors and product propositions, such as AI chatbot, e-commerce and retail fields. Take eCommerce for example, in general a system needs to manage billions of products and match them to shopper preferences, which has become a daunting task. Vector databases provide a robust and scalable platform for handling such large datasets, enabling retailers to improve the accuracy and efficiency of their product searching and recommendation systems.

It hugely boosts the efficiency of system operation and productivity. However, transitioning to a vector database requires careful consideration. The setup and configuration process can be complex, and the cost of ownership can be substantial. Businesses must evaluate their specific needs and determine if the benefits of a vector database outweigh the potential drawbacks.

For simplifying the evaluation process particularly for beginners, let’s explore 4 main aspects you need to consider from the following sections.

Speed Performance

First thing first, the key metrics of evaluating a vector database performance is the responding speed. Here are three dimensions for your reference:

1. Data Refreshing

Whatever applications or platforms you are running with AI, update-to-date data is indispensable. So a good vector database should be equipped with high speed of importing new dataset from external applications using APIs or internal integrated systems which can be converted into embeddings

2. Query Latency or QPS

Apart from refreshing the dataset, the instant responding time is a critical part to determine whether it might impact or enhance user experience. For instance, How much time does it take to execute a query and receive the result? How many queries can the system process in one second?

3. Namespaces

Namespaces are for applications to segment the data in different sections in the database index. The methodology is like SQL Key query, Non-sql index query. The purpose is to have the ability for an index to serve multiple purposes and allow users to search over a subset of data versus an entire dataset. So having namespaces help improve query performance and lower cost.

Scalability

Machine learning and AI applications are required to be scalable in the dataset aspect. So evaluating a vector database must be on the scalability after the speed performance. For instance, we should figure out if there is a limit to the number of vector embeddings given by the vector DB provider and what would be the cost and conditions if we need to remove limits.

Most vector DBs allow you to scale both horizontally and vertically. Vertical scaling means adding resources to the existing system (scaling up), while horizontal scaling involves adding additional servers (scaling out). Each option has its pros and cons and needs to be evaluated case by case, but both require manual actions.

In a perfect case, we can scale automatically and don’t need to worry about how to scale at all because it’s all taken care of.

Relevancy

We’ve discussed the speed and server side capacity. Here are 3 aspects more related to the user experience when you select a vector DB as follows:

1. Result Accuracy

A vector DB uses a combination of different machine learning algorithms that can participate in the Approximate Nearest Neighbor (ANN) search, which allows it to search for the nearest item. Since vector DBs provide approximate results, there can be tradeoffs between accuracy and speed. However, a good system can provide ultra-fast search with great accuracy.

2. Hybrid Search Capability

Interacting with databases can be a hybrid case because normally users might use a clear, simple and searchable keyword to get the answer. Instead, your system doesn’t need to guess by using ANN. This can be applied in a vector DB capability as well.

A good vertical DB should provide both semantic search and keyword search, which optimize the cost efficiency, speed and accuracy

3. Sort out by Metadata

Metadata provides users more dimension to query specific information while it doesn’t increase the system pressure on the speed of searching and responding. A good vector database can allow users to add more meaningful information and also efficiently work with the system namspaces

Cost-efficiency

The cost of implementing a vector database can vary depending on the specific vendor, deployment model, and data volume. We need to check out clearly what the pricing model is, such as free, monthly, pay-as-you-go by tier, etc.

Many open-source vector database solutions are available, such as Pinecone and Faiss, which can further reduce costs. These open-source solutions provide a cost-effective entry point for businesses and organizations to explore the benefits of vector databases without the need for significant upfront investments. However, we also need to check out the limitation and security of open-source vector DB if your embedding dataset include sensitive information

Wrap up

Looking for a right vector DB can be a daunting task except that you have a clear logic and strategy to look up things you aim to have applied to your applications. Hope this piece is helpful and see you next time.