Why can't we store data in a normal SQL database, and why do we need vector databases?

SQL databases are designed around relational models with fixed schemas, ACID transactions, and set‑based operations. They excel at structured, tabular data and transactional workloads, but they are not optimized for high‑dimensional vector similarity search, which is essential for tasks like nearest‑neighbor search in recommendation systems, image retrieval, or semantic search.

Key limitations of SQL for vector workloads:

1. Indexing – Traditional B‑tree or hash indexes cannot efficiently handle multi‑dimensional vectors; they require linear scans for similarity queries.
2. Scalability – As dimensionality and dataset size grow, the cost of computing distances (e.g., Euclidean, cosine) becomes prohibitive in a single‑node or even sharded SQL setup.
3. Latency – Approximate nearest neighbor (ANN) algorithms rely on specialized data structures (IVF, HNSW, PQ) that are not part of standard SQL engines.
4. Schema rigidity – Adding new vector fields or changing dimensionality often requires schema migrations, which are costly.

Vector databases (e.g., Pinecone, Milvus, Weaviate) address these gaps by:

• Using ANN indexes that partition the vector space and enable sub‑linear search.
• Supporting distributed architectures that scale horizontally.
• Providing native similarity metrics and dynamic schema for vectors.
• Offering integration with embeddings from ML models.

Thus, while a normal SQL database can store vectors as blobs, it cannot efficiently retrieve similar vectors at scale. Vector databases are specialized for that purpose, delivering low‑latency, high‑throughput similarity search that would be impractical with traditional relational systems.