AI-Native Databases & Vector Infra: The New Heart of Data Infrastructure

Databases (DBs) have long been the backbone of IT.
For decades, this seemed a “solved problem”

Oracle, MySQL, MongoDB, PostgreSQL, and others had already established stable ecosystems.

But by 2025, the landscape has changed.
Generative AI, multimodal search, and hyper-personalized services are reshaping how we store, retrieve, and leverage data.
It’s no longer enough to fetch numbers or strings quickly; contextual and semantic understanding has become critical.

This new paradigm is powered by AI-Native Databases & Vector Infrastructure.

What Is a Vector Database?

A vector database stores text, images, audio, and other data as numeric vectors, then calculates similarity for search.

Example:

Searching for “a dog playing in the park” doesn’t just match keywords; the database finds images closest in meaning using vectors.

In RAG (Retrieval-Augmented Generation) setups, like LLMs answering “trends in a startup VC landscape”,

the system pulls related vectorized reports to enhance responses.

Key Players:

Pinecone, Weaviate, Qdrant, Milvus, plus

traditional DBs extending with vector support : Postgres + pgvector, MongoDB Atlas Vector Search.

How Vectorization Works

2. Vectorization:

3. Transforming text, image, or audio into numeric arrays.

• Cat -> [0.12, -0.87, 0.45, …]

• Close to “dog”, far from “car” in vector space.

2. Embedding Models:

• - Text: OpenAI text-embedding-3-large, HuggingFace BERT

• - Images: CLIP, ResNet, Vision Transformer (ViT)

• - Audio/Video: wav2vec, Whisper, VideoCLIP

2. - Storage & Indexing:

   • Dense vector storage: High-precision, exact similarity

   • Compressed/Quantized vector: Memory-efficient, slightly approximate

• - Popular Indexing & Search Tech:

• FAISS (Facebook AI Similarity Search): Optimized for k-NN search

• HNSW (Hierarchical Navigable Small World Graph): Graph-based, fast on millions of vectors

• IVF, PQ: Compressed storage, memory & speed optimized

5. Search Flow:

6. • Query → vectorized → compared against stored vectors → top-K closest results

   • Distance metrics: cosine similarity or Euclidean (L2) distance

   • Formula (cosine similarity): similarity = (A · B) / (||A|| * ||B||)

Vector DB Workflow at a Glance

Comparing Leading Vector DB Solutions (2025)

Why AI-Native DBs Are Exploding Now

• - RAG Standardization

• : LLMs now rely on DBs to supplement missing knowledge.

• - Multimodal Search

• : Beyond text—images, audio, video all vectorized.

• - Cloud Vendor Push

• : AWS, Azure, GCP all offer vector support.

Traditional DB vs Vector DB

Not a battle—coexistence.

• Traditional DBs: Transactions, finance, inventory → precision-critical

• Vector DBs: Semantic search, recommendations, LLM augmentation → flexibility-critical

Many companies adopt a hybrid approach: Postgres + pgvector, Qdrant, etc.

Real-World Use Cases

• - Notion AI: Document search & answer augmentation

• - Spotify: Vectorized songs & lyrics → personalized recommendations

• - Shopify: Product image search & AI shopping assistants

• - Startups: Legal search, medical imaging prototyping with pgvector

Summary

AI-Native Databases & Vector Infrastructure are now the brain of AI systems.

For AI service planning or operations, choosing the right DB is no longer just a developer choice,

it’s a strategic decision that shapes user experience.