Vector Database

Nowadays, more and more businesses use vector databases for their day-to-day processes. With this kind of technology, you can store and access large quantities of data in just about any format, ranging from text to images, videos, and audio content.

Most importantly, by using a vector database, you can establish relationships between two data points. Companies use them to create recommendation engines, improve browsing experience, detect anomalies, and perform data clustering.

Vector Database Basics

In a normal database, numbers, strings, and other data are represented in columns and rows. In comparison, a vector database relies exclusively on vectors, making the database more optimized and changing its common use cases.

When we use a vector database, all data is stored within a virtual space. The connections between two data points are showcased with vectors, allowing us to determine their differences and similarities. These systems rely on ANN or Approximate Nearest Neighbor search to determine mutual relationships.

With a vector database, you can quickly and accurately retrieve related data points. Given that the database can only provide approximation, we commonly have to sacrifice accuracy for speed. In other words, the slower the query processing the more accurate the results and vice versa. 

How Do Vector Databases Work? 

Here’s how all this works in practice:

  • A vector database creates an index of vectors by relying on specific algorithms such as HNSW, PQ, or LSH. Vectors are placed on a virtual map, which will later allow for faster retrieval
  • During the querying process, the databases make a comparison between indexed vectors and indexed query vectors, focusing on the closest neighbors based on their similarities
  • Sometimes, the process includes additional post-processing, during which a database might rank neighbors once again by using different parameters

While a vector database is fantastic for establishing similarities between two data points, it doesn’t work that well for complex queries. As such, many companies prefer using a graph database for establishing patterns and analyzing several data sets concurrently. 

What Makes Vector Databases Great? 

Users index vectors into the database, which allows them to establish relationships between two data points. That way, data practitioners can process embedding models. By relying on database features such as security controls, resource management, fault tolerance, and rapid information retrieval, users can improve their development process.

Vector databases also allow data practitioners to create unique applications and features for their target audiences. For example, you can integrate search and recommendation functions into various programs, thus increasing their usefulness. 

Another thing that makes vector databases great is the way they support machine learning processes. These systems can perform natural language processing and deliver on-demand real-time data to users. That way, your software can deliver more relevant results to queries based on the understanding of context. 

Main Advantages and Disadvantages

Vector databases can be a perfect solution for developing certain types of software and systems, while they utterly fail in other tasks. Specifically, companies use them to create ML models and process search queries, although these solutions struggle with more complex tasks.


  • Vector databases provide enormous flexibility in terms of content that you can use. They can be implemented in both structured data (numbers, text, and symbols) and unstructured data (various types of files and documents). As such, they can be of great help for various searches and recommendation engines 
  • This type of technology integrates well with ML models, which puts it at the forefront of the AI revolution. The software can explore billions of vectors and provides a certain level of scaling, although it isn’t as effective as graph databases. 
  • Although vector databases don’t provide the same type of scaling as some other modern databases, they definitely beat traditional databases in terms of performance. They can handle large quantities of data, which can come in handy in various application 
  • Another major benefit is improved automation, which stems from using vector databases for machine learning systems. Companies can use these systems for knowledge bases and internal searches, as well as various other solutions that would help them improve user experience 
  • Companies can use vector databases to execute various similarity searches. This type of database can determine relationships between two data points within a multi-dimensional space, making it ideal for comparative analysis


  • Reduced accuracy is one of the biggest issues when using vector databases. As the number of data increases and queries go beyond similarity searches, it becomes harder for the database to provide relevant results due to its innate limitations 
  • In many cases, users have to sacrifice speed for accuracy or vice versa. This dynamic can be troublesome for companies looking for quick solutions to their issues
  • As the data quantity increases, users will notice a major reduction in data availability and efficiency. Again, this has to do with systems’ innate limitations and inability to process complex data sets
  • Unlike some other types of databases, vector databases have high memory and storage requirements. This issue becomes even more noticeable as the quantity of data increases, which will hinder scaling

Choosing a Vector Database

Whether or not you’ll have a positive experience with a vector database depends on the product you’re using. Like with any other software, you need to choose a database that fits your particular company needs. Among others, you need to pay attention to integrations, supported data models and scalability.

That being said, these are the main things you should keep in mind when choosing a database:

  • Performance

The main thing you need to consider is performance coupled with scalability. You need to see how much data a vector database can process before slowing down and losing accuracy. Specifically, you should analyze throughput and response times, as this will affect your workload requirements.

  • Ease of Use

Even if you’re not that great with vector databases, you shouldn’t have too much trouble configuring and setting up a system. You also need to consider how much maintenance a system requires. Good documentation and user-friendly interface can help you get acquainted with the software and start producing better results faster. 

  • Data Model and Indexing

You should also take into account indexing methods and data model. Consider the indexing mechanisms so you can be certain that similarities searches and retrievals are efficient. 

  • Integrations

In this day and age, you need software that can be integrated with various other programs. Similarly, your vector database should easily integrate with your existing IT structure, programming languages, and tools. We suggest you analyze SDKs, APIs, and connectors before purchasing a product.

  • Support

Even if you’re proficient with a database, you might have a few questions for their support team. Because of that, it’s much better to pick products that have great call centers and knowledge bases you can tap into. It also wouldn’t hurt if a company has active community that can help you out.


Using a vector database can be a real game-changer for your company. These solutions can be used to develop all sorts of software that can be used internally or sold to external entities. By relying on vectors, these databases can establish relationships between numerous data points, which is vital when creating search and recommendation engines. Nowadays, vector databases are commonly used for the development of machine learning solutions, making them an invaluable component for any IT brand.