What is a Vector Database?

A vector database is designed to store, index, and search high-dimensional data. Put more simply - it stores data in a mathematical ‘array’ of numbers, called ‘vectors’. These vectors are grouped together based upon their similarity to one another. These vectors are typically generated by machine learning models and are used to represent unstructured data such as text, images, audio, and video in a numerical format that captures semantic ‘meaning’. 

This formatting makes it easy for low-latency queries to be run against them. Think of this like shopping for similar items at a grocery store - you wouldn't want apples and bananas located in different parts of the same store. The ability for low-latency queries makes vector databases perfect for AI applications, as they can remember previous inputs.

What are vectors?

In machine learning, a vector is a list of floating-point numbers ([0.23, -0.11, 0.87, ...]) that encode the characteristics of an object, such as a sentence or an image. These are often generated using models like Word2Vec, BERT, or CLIP. The idea is that similar items will have vectors that are close to each other in the vector space (the apples and bananas example listed above). 

Why do we need vector databases?

Traditional databases (like SQL or NoSQL) are not optimized for ‘similarity search’ (apples and bananas again). Vector databases:

• Enable efficient ‘nearest neighbor’ searches, such as finding the most similar document or image.

• Handle large-scale unstructured data.

• Support operations like semantic search, recommendation systems, and anomaly detection.

• Provide indexing strategies (like HNSW, IVF, or PQ) which in turn enable  fast query times.

What are some common use cases for vector databases?

• Semantic search:  Search engines that understand meaning, not just keywords

• Recommendation systems: Suggesting content or products based on similarity

• Image and video search: Finding visually similar content

• Chatbots and LLM memory: Storing context for retrieval-augmented generation

• Fraud detection: Identifying behavior anomalies through vector patterns

How is data stored in a vector database and how do searches work?

Vector databases use approximate nearest neighbor (ANN) algorithms to quickly find vectors that are closest to a query vector based on a similarity metric. ANN trades a bit of accuracy for a significant speed advantage, which allows scalability. 

Each entry typically includes:

• The vector itself

• An optional payload (metadata like text labels, timestamps, etc.)

• A unique ID for retrieval and update

Some databases allow you to combine vector similarity with filtering based on metadata (i.e. “search for documents similar to X but only in English”).

What is the difference between a vector database and a traditional database?

Some key differences between a traditional and vector database are shown in this table.

What are some popular vector databases?

Some widely used vector databases include:

• Pinecone

• Weaviate

• FAISS (by Meta)

• Milvus

• Qdrant

• Chroma

• Vespa

Each has different strengths, depending on your use case. Key features range from performance to integrations, and open-source availability.

While Fastly doesn’t offer vector databases directly, our edge cloud platform helps accelerate the delivery of data-intensive applications, including those powered by AI and vector search. Whether you're building next-gen search, recommendation systems, or real-time analytics, Fastly ensures your APIs and apps run fast, securely, and closer to users. Click here to learn more.