You expect a database to return exactly what you ask for. Then you type “find documents about AI policy failures” and exact keywords are no longer the point. That is the moment vector databases stop being a curiosity and start being the helpful layer behind modern AI search.

4 related posts

Vector dimensions: hundreds to thousands · Search type: semantic similarity · Primary AI use: RAG workflows

At a glance

1Definition
2How it works
  • Workflow: embed data → store vectors → embed the query → query vectors with similarity search (Microsoft Fabric documentation).
  • Enables question answering over the data in a vector store (Microsoft Learn’s Cosmos DB vector docs).
3Different from relational
  • Relational databases model data as tables with rows and columns (Microsoft Azure’s cloud dictionary).
  • Vector databases model data as points in a high-dimensional space (Microsoft Learn’s AI vector-stores overview).
4Why it matters for AI
  • Especially useful in RAG pattern workflows with Azure OpenAI (Microsoft Learn’s AI vector-stores overview).
  • Supports semantic search, personalization, and anomaly detection (Microsoft Learn’s data engineering playbook).

The short version: vector databases trade one mental model for another — tables become points, exact matches become proximity.

Key facts at a glance

Across Microsoft’s technical documentation, the vector database story stays consistent: this is the database layer built for embeddings, similarity, and AI retrieval.

Fact Detail
Definition Designed to store, manage, and index high-dimensional vector embeddings efficiently (Microsoft Learn’s data engineering playbook).
Semantic query model Finds data by comparing meaning, not by exact matches on fields (Microsoft Learn’s AI vector-stores overview).
Relational difference Relational databases represent data points as rows and columns (Microsoft Learn’s data engineering playbook).
Standard workflow Embed data, store vectors, embed the query, and query vectors using similarity search (Microsoft Fabric documentation).
AI use case Especially useful in RAG pattern workflows with Azure OpenAI (Microsoft Learn’s AI vector-stores overview).
Question answering Can be used for question answering over the data stored as vectors (Microsoft Learn’s Cosmos DB vector docs).
Relational mental model A relational database is perceived as a collection of tables (PostgreSQL’s SQL documentation).

Why this matters: these facts explain why “vector” and “relational” are not interchangeable; each database answers a different kind of query.

What is a vector database?

At its simplest, a vector database is the storage layer that makes semantic search possible. It accepts vector embeddings — the numerical representation of words, images, or documents — and organizes them in a high-dimensional space. Microsoft Learn’s data engineering playbook describes this as a database designed to store, manage, and index high-dimensional vector embeddings efficiently.

“Similarity search is about comparing the meaning.” — Microsoft Learn’s AI vector-stores overview

That idea is central to Microsoft Learn’s AI vector-stores overview: the database retrieves by semantic characteristics, not exact field matches.

The takeaway

A vector database is not a fancier relational database. It is a different tool for a different question: not “where does this row match?” but “which data point is closest in meaning?”

How does a vector database work?

Microsoft Fabric’s documentation lays out the standard workflow clearly:

  1. Embed data into vectors using an embedding model.
  2. Store the vectors alongside the original content or reference.
  3. Embed the user query with the same model.
  4. Query the vector index using similarity search.

That four-step loop is especially useful in RAG pattern workflows with Azure OpenAI, according to Microsoft Learn’s AI vector-stores overview. Once the workflow is active, a vector database can also support question answering over the stored data, as Microsoft Learn’s Cosmos DB vector docs point out.

Why this works

The vector store turns retrieval into a matching problem: find the stored embeddings that sit closest to the query embedding. That nearness is why the database cares about meaning rather than syntax.

Workflow summary: embedding models convert data into vectors, the database indexes them, a similarity search returns the nearest neighbors — the system retrieves by meaning, not by keywords.

Vector database vs relational database: what’s the difference?

The difference is not about one being faster than the other; it is about how each system models the data.

Dimension Vector database Relational database
Data model High-dimensional vectors and embeddings (Microsoft Learn) Tables with rows and columns (Microsoft Azure)
Query logic Semantic similarity and meaning (Microsoft Learn) Exact matches and structured operations on tables (PostgreSQL)
Core workflow Embed → store → query vectors (Microsoft Fabric) Data points as rows and columns (Microsoft Learn)
The tension is a feature

A mature AI stack often uses a relational database for transactions and a vector database for context retrieval. They answer different questions, and a strong team uses both.

The implication: choosing a database type starts with the query type, not the brand or the storage format.

What are vector databases used for?

The most concrete job today is making retrieval-augmented generation practical. Microsoft Learn’s AI vector-stores overview says vector databases are especially useful in RAG pattern workflows with Azure OpenAI.

  • Semantic search over unstructured data
  • RAG and question answering over internal documents
  • Personalization and recommendation systems
  • Anomaly detection

Microsoft Learn’s data engineering playbook connects those outcomes to the database’s ability to store, organize, and search high-dimensional data points.

Why this matters

Vector databases exist because the most important search query in AI is not a keyword; it is a meaning: “find what is relevant to this question.”

The pattern: every RAG application relies on a vector store to ground the model in retrieved facts, not just model training data.

What are the top vector databases?

Microsoft does not publish one stable “top 10” list, so the most useful answer is a comparison of practical options. Azure Cosmos DB includes a vector store, according to Microsoft Learn’s Cosmos DB vector docs. Microsoft Fabric offers native vector-database workflow guidance in Microsoft Fabric’s documentation. And the data engineering playbook itself explores vector database solutions.

  • Purpose-built vector database: designed around embeddings and similarity search.
  • Database with vector support: relational or NoSQL systems that add vector search, such as Azure Cosmos DB.
  • Data platform integration: solutions inside cloud data platforms such as Microsoft Fabric.
The smart question

Do not ask “which is the top vector database?” Ask “which vector workflow is already close to the data I need to retrieve?”

How to choose a vector database

Choosing a vector database means making four decisions: choose an embedding model, generate vectors, select an index method, and deploy the database in the right place. Those steps keep the system about retrieval quality rather than a popularity contest.

Microsoft Fabric’s documentation describes the core loop in the same terms: embed data, store vectors, embed the query, and query vectors using similarity search.

Start with the workflow

The database should fit the way your embeddings are generated and the way your application asks questions. If the retrieval context is clear, the rest of the stack can follow.

Upsides

  • Enables semantic, meaning-based search
  • Essential for RAG and AI retrieval
  • Handles unstructured data at scale

Downsides

  • Requires embedding models and pipeline maintenance
  • Not a replacement for transactional databases
  • Performance depends on index choice and data dimensionality

The future role of vector databases as AI models evolve

As embedding models get better, the vector database becomes the layer that keeps AI grounded in retrieved facts. Microsoft Learn’s Cosmos DB vector docs describe a vector store as a place where data stays available for question answering, and Microsoft Learn’s AI vector-stores overview ties that directly to RAG pattern workflows with Azure OpenAI.

That does not mean relational databases disappear. Relational databases remain the best model for rows, columns, and structured transactions. Vector databases complement them by organizing the high-dimensional points that represent meaning.

What to watch

Watch how vector stores get embedded into larger data platforms: Cosmos DB, Fabric, and the overall Microsoft data stack all assume the vector index is part of the data plane.

The catch: as platforms integrate vector capabilities, the line between vector and relational databases will blur — but the use case for each will remain distinct.

Additional sources

learn.microsoft.com, learn.microsoft.com, ibm.com, cloud.google.com, ibm.com, aws.amazon.com, learn.microsoft.com

Don't miss these

Frequently asked questions

What is a vector database used for?

Vector databases are used for semantic search, RAG workflows, question answering over data, personalization, and anomaly detection. Microsoft Learn highlights the database’s role in RAG pattern workflows with Azure OpenAI.

How is a vector database different from a relational database?

A vector database organizes high-dimensional embeddings and retrieves by semantic similarity. A relational database organizes data into tables with rows and columns, as Microsoft Azure’s cloud dictionary explains.

What is RAG?

RAG stands for retrieval-augmented generation. In a RAG pattern, a vector store supplies relevant context that the model uses to generate an answer. Microsoft Learn identifies vector databases as especially useful for those workflows.

Do vector databases replace relational databases?

No. Relational databases are still the standard model for structured data and transactions. Vector databases solve a different problem: finding data by meaning, not by exact row or column match.

What is the best vector database?

There is no single best choice. The right answer depends on your cloud stack, embedding model, and retrieval workflow. Microsoft documentation highlights Azure Cosmos DB and Microsoft Fabric as vector-ready options.

What is a vector in a vector database?

A vector is an array of numbers that represents data — such as a word, sentence, or image — in a high-dimensional space. Vector databases use these numerical representations to compare how similar different pieces of data are.

The trade-off: vector databases add a new retrieval layer to AI systems, but they only succeed when the workflow is simple: embed, store, query, and retrieve what matters.

The final measure: teams that adopt vector databases gain the ability to search by meaning, but they also take on the operational cost of maintaining embedding pipelines and index hygiene.