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Do you want to develop AI, but do not know which database to choose? Or are you looking for the advantages and use cases of vector databases? No need to worry. All your questions are answered in this blog. Let’s explore.
What is a Vector Database?
High-dimensional vector data is stored, maintained, and indexed using a vector database.
Data points are kept in vector databases as arrays of numbers known as “vectors,” which can be compared and grouped according to similarity. This approach is perfect for artificial intelligence (AI) applications since it allows for low-latency queries.
Because they provide the speed and performance required to support generative AI use cases, vector databases are becoming more and more popular. In fact, a 2025 study found that the use of vector databases increased by 377% annually, the greatest growth of any technology connected to large language models (LLMs).
What is a Traditional Database?
Traditional databases are also known as relational databases.
They have been a major part of data management for decades. In these databases, the data is stored in a structured format with the help of tables with rows and columns.
In these databases, each table represents a distinct entity, and developers use keys and indexes to specify the relationships between these things. Because relational databases use Structured Query Language (SQL) for data processing and querying, they are extremely flexible and extensively used in many different industries.
Vector Databases for Modern AI
There is one of the most commercially significant questions that has been asked quite often in enterprise AI infrastructure today and that is:
“Why Choose Vector Databases for Modern AI?”
Large datasets are used to train LLMs like GPT-4 and Claude, however during training, their knowledge is locked.
Unless that information is provided to them at this time, they are unable to know what transpired last week, what is contained in your proprietary product catalog, or what is stated in your internal compliance documentation.
The Main Use Case for Retrieval-Augmented Generation (RAG)
RAG extracts the most contextually relevant documents at query time and injects them into the model context window instead of fine-tuning a model on every piece of proprietary data, which is costly, inefficient, and sometimes over-engineered.
The practical operation of a RAG pipeline:
- A query is sent by the user.
- Using the same embedding model as your document index, the query is transformed into a vector embedding.
- The top-k document chunks with the highest semantic similarity are retrieved from the vector database.
- Together with the initial inquiry, such sections are sent to the LLM as context.
- Based on your exclusive data, the LLM produces a precise and grounded response.
The increasing trend in the machine learning field is evident for teams comparing RAG systems with LLM fine-tuning data best practices for their applications: RAG, with good vector search, is typically more affordable and manageable than fine-tuning.
Traditional Databases for Modern AI
Some common traditional relational databases include PostgreSQL, MySQL, and Microsoft SQL. The fact is that they are good at what they do such as enforcing schema, storing structured data in tables, and executing exact queries using indexed scalar values.
A common question appears like this:
- Find all clients that have an account balance more than $10,000 and have logged in within the last 30 days.
This would be a scalar search which means the database assesses numerical thresholds to discover exact matches.
However, scalar values are not easily mapped to language, visuals, or human behaviour.
A typical database cannot conceptually match a user’s input of “affordable running shoes for flat feet” with product descriptions unless all potential variations have been indexed beforehand.
Semantic intent is not a column. Meaning was never intended for the data model.
This basic restriction turns into a significant bottleneck when businesses are:
- Using.NET Core to incorporate AI into legacy systems
- Transitioning business processes to AI-native architectures
- Constructing helpers with LLM capabilities over proprietary document libraries
- Semantically searching unstructured product catalogues or support records
It shows that traditional databases are more than good for what they were built for, but when it comes to modern AI, they fail to stand up to the mark.
This is where the importance of vector databases emerges.
Advantages of Vector Databases for Modern AI
Vector databases offer a wide range of advantages to the businesses using Artificial Intelligence. They enable efficient data retrieval for AI models by storing the embeddings derived from unstructured data. This is just the start, there are more benefits, and we have listed them below. Let’s explore.
Effective Storage and Embeddings Retrieval
Vector databases save embeddings in the form of high-dimensional vectors.
These numerical formats encapsulate the semantic essence of unstructured data, enabling rapid access.
Vector databases enhance similarity search through methods such as Hierarchical Navigable Small World (HNSW) graphs.
This enables AI models to access semantically related information, enhancing response quality and minimizing irrelevant outcomes.
Rapid Similarity Search for AI Model Efficiency
Vector databases execute quick similarity searches on saved embeddings. This feature improves the performance of AI models across various applications:
Recommendation Systems: Vector databases locate items that have embeddings closely matching user preferences.
Scalability for Increasing Embedding Quantities
Vector databases manage growing amounts of high-dimensional embeddings without losing performance. They accomplish this by:
Distributed Framework: Horizontal expansion across various nodes.
Activating Retrieval Augmented Generation (RAG)
Vector databases play a crucial role in RAG systems. They retain embeddings of document segments, enabling AI models to access pertinent context while generating.
This enhances output precision and minimizes hallucinations.
Preprocessing pipelines transform unstructured data into embeddings prior to their storage in vector databases.
These pipelines manage text extraction, metadata extraction, data partitioning, chunking, and the creation of embeddings.
Employing preprocessing pipelines to transform unstructured data into embeddings and saving them in vector databases enables companies to enhance their generative AI applications.
With the increase in unstructured data, vector databases will be essential for AI projects.
Use Cases of Vector Databases in AI
Vector databases organize and control high-dimensional vector representations and cater effective similarity searches and retrieval for diverse AI applications.
Applications of NLP (Natural Language Processing)
In specific NLP applications, vector databases keep and access embeddings for semantic search and retrieval.
They allow the discovery of pertinent documents based on significance instead of precise keyword matches.
Systems for Recommendations and Individualization
Vector databases drive tailored suggestions in e-commerce, content streaming, and social networks.
They discover products with embeddings comparable to a user’s tastes, facilitating tailored suggestions.
Vector databases also recognize comparable items through their vector representations, enabling “more like this” suggestions.
Videos and Image Similarity Search
Vector databases facilitate retrieval based on similarity for searching images and videos.
Visual data undergoes processing to create embeddings that are subsequently stored in vector databases. This enables:
Video Retrieval: It encodes video frames and segments into embeddings to enable efficient searching for specific moments in videos
Detection of Fraud and Anomaly
Vector databases facilitate fraud and anomaly detection by allowing real-time similarity searches on transactional data and sensor measurements.
They help in detecting patterns that differ from normal behavior by conducting effective similarity searches, as anomalies will have embeddings that are unlike standard patterns.
Vector databases can swiftly detect suspicious transactions by aligning their embeddings with established fraud patterns.
They can detect unusual sensor readings that diverge markedly from standard patterns.
Final Thoughts
Vector databases are more important for modern AI compared to traditional ones, however none of them is better. What most developers do is combine both and go for a hybrid approach. To handle modern AI and its roles, you do not only need databases, but also AI developers who understand how those databases work.
