Vector Database

A vector database is a database that stores numerical representations of data – known as embeddings – and searches for similarities. Unlike traditional databases, it does not merely search for exact terms or values, but can find content that is similar in terms of subject matter or semantics.

This makes vector databases particularly relevant for AI applications: for example, for semantic search, recommendation engines and, above all, for Retrieval-Augmented Generation (RAG).

Key facts about vector databases at a glance

A vector database stores and searches embeddings.
It finds content based on semantic similarity rather than just keywords.
Key areas of application include RAG, semantic search and recommendation systems.
Techniques such as ANN and HNSW enable fast similarity searches across large datasets.
A separate vector database is not always necessary. PostgreSQL with pgvector or systems with integrated vector search may also suffice.
The right solution depends on data volume, performance, hosting, costs and operational overhead.

What is a vector database?

A vector database is a database system designed for storing and searching vectors. These vectors consist of numerical values and represent, for example, text, images, audio files or products.

The key difference from a traditional database lies in the nature of the query. A vector database searches for proximity within a mathematical vector space. This enables it to recognise content as similar, even when different terms are used.

For example, if a user searches for ‘shoes for jogging’, the database can also identify content relating to ‘running shoes’ or ‘running trainers’ as relevant.

What are embeddings?

Embeddings are numerical representations of content. An embedding model, for example, converts a text into a long sequence of numbers. Content with similar meanings is then positioned relatively close to one another in the vector space.

The vector database does not usually generate this meaning itself. It stores the generated embeddings, indexes them and makes them efficiently searchable.

In addition, metadata such as category, source, language, date or access rights is often stored. This allows semantic similarity to be combined with traditional filters.

How does a vector database work?

First, the source data is converted into embeddings. In the case of longer documents, the content is often broken down into smaller sections, known as ‘chunks’.

The vector database then stores and indexes these vectors. For large volumes of data, methods such as Approximate Nearest Neighbour Search (ANN) and index structures such as HNSW are used. These prevent the need to compare every stored vector in full for every search query.

The user’s query is also converted into an embedding. The database then searches for vectors with the highest possible similarity. Cosine similarity or Euclidean distance, for example, can be used for this calculation.

The result is not a pure keyword match, but an assessment of semantic proximity.

In real-world applications, Vector Search is frequently combined with keyword search or metadata filters. This hybrid search is important, for example, when product numbers, proper nouns or exact technical terms need to be taken into account.

Vector search does not replace keyword search; rather, it extends the search logic.

What are vector databases used for?

With RAG, the system first retrieves relevant information from a knowledge base and then passes it on to a large language model as context. A vector database can form the semantic retrieval layer in this process.

Users can search for meanings rather than exact phrases. This is particularly helpful for natural or vague search queries.

Products, content or user profiles can be represented as vectors. This enables systems to identify similar content or make appropriate recommendations.

Images, audio content and other media can also be represented as vectors and searched for similarities.

Why are vector databases important for RAG?

Large language models do not automatically have access to internal documents, up-to-date product information or specific company knowledge. RAG therefore supplements the model with a retrieval process.

A user query is converted into an embedding and compared with the stored documents. The most relevant text passages are then passed to the language model as context.

What a vector database is not A vector database solves a clearly defined problem: it makes semantic similarity efficiently searchable.

A vector database does not generate text or answers.

It does not automatically generate a semantic representation of the data.

Traditional SQL and NoSQL systems remain important for transactions, structured data and precise queries.

Poor embeddings, inappropriate chunking or incorrect data cannot be compensated for by a specialised database alone.

Vector database vs. traditional database

Criterion	Traditional Database	Vector Database
Search	exact values, filters, IDs	Similarity
Data	Tables, Documents, Fields	Embeddings and Metadata
Typical Query	SQL, Keyword, Filter	Nearest Neighbor Search
Strength	structured data	semantic relationships
Typical AI Role	Data Source	Retrieval Layer

In practice, the two approaches often complement one another. Product, customer or master data can continue to be stored in a traditional database, whilst embeddings are processed additionally to enable semantic search.

A vector database therefore does not usually replace existing databases, but rather complements them

Do I really need a vector database?

Not every AI application requires a separate vector database system. For small data sets, manageable requirements or existing PostgreSQL environments, pgvector may, for example, be sufficient.

A dedicated vector database becomes particularly interesting when searching through very large volumes of embeddings, when low latencies are required, or when vector search is a core function of the product.

The crucial question is therefore not: ‘Which vector database is the best?’, but rather: ‘Which architecture solves our retrieval use case with as little unnecessary complexity as possible?’

After all, every additional database entails operational overhead, monitoring, security requirements and costs.

PLEASE NOTE: A separate vector database is not always necessary

Anyone developing a proof of concept or a small-scale RAG application does not automatically need an additional specialised system. PostgreSQL with pgvector or an existing platform with integrated vector search may be sufficient, depending on the requirements.

Technical specialisation is only worthwhile if the use case actually requires it.

What vector database solutions are available?

Solution	Classification
	Managed Vector Database
	specialized vector database
	Vector Database and AI Search
	Open-Source Vector Database
	Vector Store, often used for development and prototyping
	Vector Extension for PostgreSQL

The market comprises both specialised vector databases and extensions to existing database systems.
There is no single ‘best’ solution. Data volume, query latency, metadata filters, hosting, data protection, existing infrastructure and operational overheads all determine which system is the most appropriate.

What criteria should you use when choosing a vector database?

When making a selection, it is not only the theoretical search speed that is decisive. Organisations should first clarify how many embeddings are stored, how rapidly the dataset is growing, and what the latency and availability requirements are.

Metadata filters and hybrid search are equally important. In production environments, semantic similarity alone is often not sufficient. It must also be possible to filter results by category, access rights, client or recency.

The deployment model also plays a role. Managed services reduce the organisation’s own infrastructure costs, whilst self-hosted solutions allow for greater control over hosting and data management.

The most cost-effective vector database is not necessarily the one with the most specialised technical features.

What does a vector database mean for SEO and GEO?

For traditional SEO, a vector database is not a ranking factor. The principle becomes particularly relevant in the context of AI systems, RAG and semantic retrieval.

In the context of GEO, the technology demonstrates why machine-readable semantic relationships are becoming increasingly important. Clear entities, structured statements, entity consistency and easily extractable key messages make it easier for AI systems to classify content semantically and use it as a grounding source.

However, a vector database does not generate AI visibility. GEO continues to be built on SEO, relevance, authority and trust.

Vector search solves the problem of retrieval. It does not solve the problem of a lack of digital authority.

Conclusion: Vector databases specialise in semantic retrieval

A vector database makes embeddings efficiently searchable, thereby enabling semantic search, RAG, recommendation engines and other AI applications.

Its benefit does not lie in replacing traditional databases. It solves a specific retrieval problem: quickly finding, from large volumes of data, the content that is most similar in terms of content to a query.

Whether a dedicated vector database is necessary for this depends on the use case. Often, an existing database with vector capabilities is the simpler way to get started.

The technology should therefore not be the starting point for the decision-making process . First comes the use case. Then the architecture. Then the specific system.

Frequently Asked Questions about Vector Databases

What is a vector database, explained simply?

A vector database stores numerical representations of data and can find similar content based on their mathematical proximity. This allows for searches based on meaning rather than just exact keywords.

Why does RAG need a vector database?

A vector database can help RAG identify relevant document sections related to a user's query and provide them as context for a large language model. It is often useful for this purpose, but not strictly necessary.

Is PostgreSQL with pgvector sufficient?

For many small and medium-sized applications, PostgreSQL with pgvector may be sufficient, especially if PostgreSQL is already in use. For very large or specialized vector workloads, dedicated systems may offer advantages.

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

“Vector store” is often used as a general term for systems that store and retrieve embeddings. A vector database typically offers additional features for indexing, scaling, filtering, and operations. In practice, however, the terms are not always clearly distinguished.

Can a vector database replace SQL?

No. Vector databases specialize in similarity searches. Traditional relational databases remain important for structured data, transactions, and exact queries.

Which vector database is the best?

That depends on the specific use case. Key factors include data volume, performance, filtering options, deployment, data protection, costs, and existing infrastructure.