How to choose a vector database

Traditional databases, such as relational databases, store data with rows and columns inside tables. Each row represents a record, and each column represents a field of that record. This setup works well for structured data, but it can be limiting when dealing with unstructured data.

Vector databases, on the other hand, transform this unstructured data into vectors, which are essentially machine learning representations that portray complex data in a simplified form. These vectors can then be compared and searched, making vector databases particularly useful for handling large data sets and improving the performance of data-driven applications.

The key difference between vector databases and traditional databases lies in their approach to data management. While traditional databases focus on storing data in a structured format, vector databases prioritize the efficient representation and retrieval of vector data. This makes vector databases useful with modern technology, where the ability to quickly access and analyze relevant information can provide a significant competitive advantage. This includes things like AI and large language models (LLMs), where finding the most relevant data can be the difference between an app making the right or wrong choice.

Like most types of tech, vector databases come in various flavors — each one with its own unique strengths, weaknesses, and use cases. Let's explore some popular types.

Graph-based vector databases are designed to efficiently handle complex, interconnected data. They represent data as nodes (or vertices) and edges: nodes represent entities, and edges represent relationships between entities.

The main advantage of this design is the ability to efficiently handle complex, interconnected data. They excel at analyzing connections and relationships between data points, which can be crucial in certain applications. They can be less intuitive for simple similarity searches, though. This is because they are designed to handle complex relationships, which can make simple searches more complicated than necessary.

Graph-based databases excel in scenarios where the relationships between data points are as important as the data points themselves. This includes things like social network analysis and knowledge graphs, where the relationships between different pieces of information are key.

Vector databases are available in two different forms: integrated into a more full-featured product or as a point solution.

An integrated vector database combines the capabilities of vector data with the functions you’d expect from a traditional database into a single platform. This means you can store, manage, and query your data both as structured business data and as unstructured vector data within the same system.

However, a point solution is a specialized, bespoke system designed specifically for storing, managing, and querying vector data. The focus of point solutions is on optimizing vector operations and similarity search, so they can perform well on vector-specific tasks. They’re usually standalone systems that need to be integrated into your existing applications and architectures.

When choosing a vector database, thoroughly evaluate the product’s feature set and how it addresses your specific use case and requirements. These features can significantly impact the database's performance, usability, and compatibility with your existing systems. Let's delve into some of these essential features:

• Vector dimensions: This refers to the number of numerical elements each vector embedding contains. Each dimension corresponds to a specific feature or property of the data object, and the dimensionality of vectors will have a direct impact on both the accuracy and efficiency of the vector search.

• Algorithms: A vector database has algorithms that calculate vector similarity. These are essentially mathematical equations used to calculate how close or related different vector embeddings are to each other.

• Native integration: To get the benefits, you need your vector database to be able to seamlessly integrate with your existing databases and systems. This means you can perform combined queries that use both the vector similarity search and conventional SQL operations.

• Storage and retrieval: The efficiency of a vector database in storing and retrieving data is crucial. This performance can impact the speed of your applications and the overall user experience.

• Performance: The performance of a vector database is determined by how quickly it can execute operations like searches, updates, and deletions. High-performance vector databases can handle large data sets and provide quick, accurate results.

• Searching, sorting, and filtering: A robust vector database should offer powerful search capabilities, including the ability to sort and filter results. This can help you quickly find relevant information in large data sets. This is especially important as vector databases are often used to “prompt” LLMs. High-quality prompts can only be retrieved through high-relevance search.  

• Management and maintenance: Consider how easy it is to manage and maintain the database. This includes tasks like adding new data, updating existing data, and ensuring the database remains secure and reliable.

There's plenty to consider when choosing your vector database, but that doesn't mean some options aren't easier than others. 

At Elastic, we've created a flexible and adaptable vector database solution out of the box. Our support for machine learning models gives you advanced analytics and predictive capabilities, so you can uncover valuable insights and make data-driven decisions.

One of our most important features is the Hierarchical Navigable Small Worlds (HNSW) storage. This graph-based algorithm means Elastic can handle large data sets and deliver quick, accurate vector search results. Coupled with robust search capabilities, including filtering and sorting, Elastic makes it easy to find relevant information in your data.

We also prioritize security, offering advanced features, such as role-based access control and document- and field-level security. These ensure that your data remains secure and that only authorized users can access sensitive information.

Whenever you're ready, here are four ways we can help you harness insights from your data:

1. Start a free trial and see how Elastic can help your business.

2. Tour our solutions to see how the Elastic Search AI Platform works and how our solutions will fit your needs.

3. Explore how vector databases power AI search.

4. Share this article with someone you know who'd enjoy reading it via email, LinkedIn, X, or Facebook.