Short answer. A vector database stores data as lists of numbers called embeddings, often 384 to 3,072 values per item, and finds results by mathematical closeness instead of exact keyword matches. This lets an AI system retrieve the 5 or 10 most relevant documents for a question in a few milliseconds, even across billions of records. In production AI, the vector database is the memory a language model reads from before it answers, which is what keeps answers grounded in your own data instead of the model’s guesswork.
Every piece of text, image, or record you want an AI system to search is first passed through an embedding model. That model turns the item into a vector, a fixed list of numbers that captures its meaning. Two documents about loan default risk land close together in this number space even if they share no common words. A question about “missed repayments” sits near a document about “arrears” because the meanings are close, not because the letters match.
The vector database holds all of those vectors and answers one core query fast: given this new vector, which stored vectors are nearest? That single operation powers most of the AI features companies ship today.
Ableneo shipped 34 production AI projects in 2025, and 94% of them use large language models. Most of those models sit on top of a retrieval layer, and that retrieval layer is a vector database.
For a bank or insurer, the vector database is not plumbing you can ignore. It is the store that decides which facts reach the model, so it is part of your data governance surface. Under the EU AI Act, high-risk AI systems must meet Article 10 obligations on data quality, relevance, and representativeness. If your retrieval store holds stale, biased, or mislabeled documents, the model inherits those flaws directly, and the accountability sits with you as the deployer.
There is a second obligation. A managed vector database running in a vendor’s cloud is an ICT third-party service under DORA, which applies to EU financial entities from 17 January 2025. That pulls it into your register of information, your concentration-risk assessment, and your exit planning. The choice between a self-hosted store and a managed one is a compliance decision, not only a technical one.
A vector database searches by meaning, ranking stored embeddings of 384 to 3,072 values by closeness to a query, returning the top matches in milliseconds.
It measures distance. Each stored item and each incoming query is a point in a space of several hundred to a few thousand dimensions. The database ranks stored points by how close they are to the query, using cosine similarity or a related distance measure, then returns the nearest few. Checking every point one by one would be too slow at scale, so vector databases use approximate nearest neighbor search. The most common method, Hierarchical Navigable Small World graphs, builds a layered graph that lets a query jump toward its neighborhood and refine, returning strong matches across billions of vectors in milliseconds.
The word “approximate” is deliberate. The database trades a small amount of exactness for a large gain in speed, and you tune that trade with index settings. For most retrieval tasks the top results are effectively identical to an exhaustive search, at a fraction of the cost.
A relational database answers questions with hard boundaries: find every account opened after 1 June with a balance over 10,000. It matches exact values and ranges. A vector database answers a softer question: find the records most similar in meaning to this one. It returns a ranked list by closeness, not a filtered set by rule.
The two are complements, not rivals. A well-built system uses metadata filters (customer segment, date, language) to narrow the candidate set, then vector similarity to rank what remains. Many teams run both in one engine, which is why the boundary is blurring rather than hardening.
No. If you already run PostgreSQL, the pgvector extension adds a vector type and approximate-nearest-neighbor indexes to a database your team already operates and already audits. For a first retrieval workload of a few million vectors, that is often the fastest safe route to production, and it keeps one fewer system in your compliance scope.
A dedicated engine such as Milvus, Qdrant, or Weaviate earns its place when scale, query volume, or filtering complexity outgrow what a general-purpose database handles comfortably. The right question is not “which vector database is best” but “what is the smallest system that meets this workload and stays inside our governance model.” Start with what you already control, and add a specialized store when the numbers demand it.
Four things decide whether a vector database is production-ready in a regulated setting. First, data residency: confirm where vectors and source text physically sit, because embeddings can be reverse-engineered toward the original content and count as sensitive data. Second, access control: retrieval must respect the same permissions as the source documents, so a user never receives a chunk they were not cleared to read. Third, auditability: every retrieval should be loggable, showing which documents fed which answer, which is what supports EU AI Act record-keeping and internal review. Fourth, deletion: when a customer exercises a GDPR erasure request, you must remove their vectors too, not only the original file.
A vector database is only as good as the retrieval design around it, and that design is where most projects succeed or stall. Ableneo builds the full pipeline: chunking, embedding, index tuning, permission-aware retrieval, and evaluation, so the store returns the right context and the model earns trust. Our work across FS&I clients in Slovakia, the Czech Republic, and Austria pairs this with the governance that regulated buyers require, which is why roughly 80% of our projects reach production rather than stalling as pilots. For how the model uses what the vector database returns, see our explainer on retrieval-augmented generation.
Key takeaways
Planning AI in a regulated business? Ableneo takes systems from classification to governed production.