• Artificial Intelligence

Kannan Rajendiran

August 25, 2026

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4 Min read

Vector databases have become a near default part of the stack for anything involving embeddings, semantic search, or retrieval augmented generation, but the term still gets thrown around loosely enough that many engineers adopt one without fully understanding what problem it actually solves. A vector database is not a replacement for your existing relational or document database. It is a specialised tool for a specific kind of query that traditional databases handle poorly.

The Core Problem Vector Databases Solve

Traditional databases are built for exact match and range queries, find the row where user_id equals this value, or where price falls between two numbers. Embeddings, the numerical representations of text, images, or audio produced by machine learning models, do not work that way. A query embedding needs to be compared against millions of stored embeddings to find the ones that are most similar in meaning, not identical in value. That similarity search, typically measured with cosine similarity or Euclidean distance across hundreds or thousands of dimensions, is computationally expensive to do with a brute force scan once your dataset grows past a few thousand records.

Vector databases solve this with approximate nearest neighbour indexing, algorithms like HNSW or IVF that trade a small amount of accuracy for a large gain in query speed, letting you search millions of vectors in milliseconds instead of seconds or minutes.

Vector Database vs Traditional Database: The Real Distinction

It helps to think of these as complementary rather than competing tools. Your relational database still handles user accounts, transactions, and structured business data exactly as before. The vector database sits alongside it, handling the specific job of semantic similarity search, and the two are frequently used together in the same application, with the vector database storing an ID that maps back to a full record in the primary database.

When You Actually Need One

The clearest signal you need a vector database is building a feature around semantic search, where users search by meaning rather than exact keywords, or a retrieval augmented generation pipeline, where you need to fetch the most relevant document chunks to feed into a language model’s context. Recommendation systems based on content similarity, image similarity search, and deduplication of near identical records are other common use cases where a vector database earns its place in the stack.

• Building semantic or natural language search over documents, products, or support tickets
• Implementing retrieval augmented generation for an internal or customer facing AI assistant
• Content based recommendation systems using embedding similarity rather than rule based logic
• Detecting near duplicate content, images, or records at scale

When a Traditional Database Is Still Enough

If your dataset is small, under roughly 10,000 to 50,000 vectors depending on dimensionality, a brute force similarity search inside your existing database or even in application code can perform acceptably without the added infrastructure of a dedicated vector store. Several modern relational databases now support vector extensions directly, which can be a lighter weight option than standing up a separate system if your similarity search needs are modest and your team wants to avoid managing another piece of infrastructure.

Picking Between Options

Once the decision is made to adopt a dedicated vector database, the choice usually comes down to managed versus self-hosted, and how well the tool integrates with your existing embedding pipeline. Comparing index type support, filtering capability alongside vector search, and latency at your expected scale matters more at this stage than headline benchmark numbers, since real world query patterns rarely match the exact benchmark conditions vendors publish.

For teams evaluating this decision, AWS’s overview of vector search and embeddings is a solid vendor neutral starting point for the underlying concepts before comparing specific products. If you are building a semantic search or retrieval pipeline and want a second opinion on architecture, our engineering services team regularly works through exactly this kind of stack decision with clients moving from prototype to production.