Vector Database vs Traditional Database: Key Differences

In 2022, a team building an internal knowledge base for a 500-person engineering organization faced a common architecture decision. They had PostgreSQL running in production with 200 GB of documentation, runbooks, and RFCs stored as plain text. They needed engineers to be able to ask natural language questions and get relevant answers. Their first instinct was to add full-text search with tsvector. It worked for exact keywords. It completely failed for intent-based queries.

The decision they faced, and the one most AI application teams face today, is not whether to replace PostgreSQL. It is understanding which problems each database type was designed to solve and where the boundary between them falls.

This article covers the fundamental differences between vector databases and traditional relational databases across data model, storage, indexing, query language, consistency, scalability, and operational complexity. It also covers pgvector as the middle path for teams already running PostgreSQL.

This is the fifth article in the Vector Database Fundamentals series. It builds on the vector database overview, the mathematics from vectors in machine learning and embeddings, the retrieval patterns from dense vs sparse vectors and semantic search, and connects to the Elasticsearch comparison that follows.

The Core Architectural Difference

The difference between a relational database and a vector database is not a matter of features or extensions. It is a difference in the fundamental question each database is designed to answer.

A relational database answers: does this record match these exact criteria?

A vector database answers: which stored items are most similar to this query?

Everything else follows from that distinction. The data model, the index structure, the query language, the consistency model, and the scaling approach are all consequences of which question the system is optimized to answer.

According to Yugabyte's vector database guide, differences between a vector database and a relational database stem from the data model, query types, and use cases they are optimized for. A relational database is a widely used data storage system optimized for clearly structured data. A vector database organizes information in an n-dimensional vector space, allowing comparison by distance or similarity.

Data Model

Relational Database: Tables, Rows, and Columns

A relational database stores data in tables. Each table has a fixed schema: a predefined set of columns with data types. Each row is a record. Relationships between tables are expressed through foreign keys and enforced through joins at query time.

-- A well-normalized relational schema
CREATE TABLE customers (
    id          SERIAL PRIMARY KEY,
    email       VARCHAR(255) UNIQUE NOT NULL,
    name        VARCHAR(100) NOT NULL,
    created_at  TIMESTAMP DEFAULT NOW()
);

CREATE TABLE orders (
    id           SERIAL PRIMARY KEY,
    customer_id  INTEGER REFERENCES customers(id),
    total_amount DECIMAL(10, 2) NOT NULL,
    status       VARCHAR(20) CHECK (status IN ('pending', 'shipped', 'delivered')),
    created_at   TIMESTAMP DEFAULT NOW()
);

CREATE TABLE order_items (
    id          SERIAL PRIMARY KEY,
    order_id    INTEGER REFERENCES orders(id),
    product_id  INTEGER NOT NULL,
    quantity    INTEGER NOT NULL,
    unit_price  DECIMAL(10, 2) NOT NULL
);

The schema enforces exactly what data can go into each column and what relationships are valid. This rigidity is a feature for transactional systems where data integrity is critical.

Vector Database: Vectors, IDs, and Metadata Payloads

A vector database stores records with three components: a unique ID, a fixed-length vector of floating-point numbers, and an optional metadata payload.

from qdrant_client import QdrantClient, models

client = QdrantClient(host="localhost", port=6333)

# Create a collection — the only schema decision is dimensionality
client.create_collection(
    collection_name="product-catalog",
    vectors_config=models.VectorParams(
        size=1536,                         # must match your embedding model
        distance=models.Distance.COSINE,
    ),
)

# Insert records
client.upsert(
    collection_name="product-catalog",
    points=[
        models.PointStruct(
            id=1,
            vector=[0.41, -0.22, 0.88, ...],    # 1536-dimensional embedding
            payload={                             # flexible key-value metadata
                "name":     "Wireless Noise-Cancelling Headphones",
                "category": "electronics",
                "price":    249.99,
                "in_stock": True,
                "tags":     ["audio", "bluetooth", "travel"],
            },
        ),
        models.PointStruct(
            id=2,
            vector=[-0.11, 0.73, -0.44, ...],
            payload={
                "name":     "Studio Monitor Speakers",
                "category": "electronics",
                "price":    399.00,
                "in_stock": False,
            },
        ),
    ],
)

There is no schema enforcement beyond the vector dimension. Two records can have completely different metadata keys. You cannot define foreign key relationships between collections. There are no joins.

This flexibility is appropriate because unstructured content such as documents, images, audio, and code does not map cleanly to a fixed table structure.

Query Language and Retrieval Pattern

SQL: Exact Matching and Relational Algebra

SQL is designed for deterministic queries. The answer to SELECT * FROM orders WHERE customer_id = 42 is always the same for a given database state. The B-tree index on customer_id makes this lookup near-instant regardless of how many other customers exist.

-- Exact match lookup
SELECT o.id, o.total_amount, o.status
FROM orders o
JOIN customers c ON c.id = o.customer_id
WHERE c.email = 'alice@example.com'
  AND o.status = 'pending'
  AND o.created_at > NOW() - INTERVAL '30 days'
ORDER BY o.created_at DESC;

SQL supports WHERE, JOIN, GROUP BY, ORDER BY, HAVING, window functions, subqueries, and aggregate operations. It is expressive, mature, and backed by 50 years of tooling. Every ORM, every BI tool, and every reporting framework speaks SQL.

Vector Query: Approximate Nearest Neighbor Search

A vector database query replaces the exact match predicate with a similarity search. You provide a query vector, specify how many results you want, and optionally filter by metadata.

import openai

client_oai = openai.OpenAI(api_key="your-key")

def embed(text: str) -> list[float]:
    resp = client_oai.embeddings.create(
        input=text,
        model="text-embedding-3-small"
    )
    return resp.data[0].embedding

# User query
query = "wireless headphones for travel"
query_vector = embed(query)

# Find semantically similar products, filtered by in-stock status
results = client.search(
    collection_name="product-catalog",
    query_vector=query_vector,
    query_filter=models.Filter(
        must=[models.FieldCondition(
            key="in_stock",
            match=models.MatchValue(value=True)
        )]
    ),
    limit=5,
    with_payload=True,
)

for hit in results:
    print(f"Score {hit.score:.4f}: {hit.payload['name']} (${hit.payload['price']})")

# Output:
# Score 0.8841: Wireless Noise-Cancelling Headphones ($249.99)
# Score 0.7203: Over-Ear Bluetooth Headphones ($179.99)
# Score 0.6955: True Wireless Earbuds with ANC ($149.99)

The query returns items whose embeddings are closest to the query embedding using HNSW approximate nearest neighbor search. "Wireless headphones for travel" matches "Wireless Noise-Cancelling Headphones" even though "travel" does not appear in the product name, because the embedding model learned that travel audio and noise cancellation appear in similar contexts.

The metadata filter (in_stock = True) applies before or during the ANN search, depending on the database implementation. This combination of semantic retrieval and structured filtering is the core pattern for most production AI search features.

Index Structures: B-Tree vs HNSW

The index is where the operational difference between the two database types is most concrete.

B-Tree: Sorted Order for Exact Queries

PostgreSQL, MySQL, and most relational databases use B-tree indexes by default. A B-tree maintains all indexed values in sorted order in a balanced tree structure. Finding a specific value requires traversing from the root to a leaf, which takes O(log n) time.

B-tree index on customer_id:
              [500]
           /         \
      [250]           [750]
      /   \           /   \
  [125] [375]     [625] [875]

For exact lookups (customer_id = 42) and range queries (customer_id BETWEEN 100 AND 200), the B-tree is extremely efficient. It scales to billions of rows with predictable, logarithmic lookup time.

B-trees cannot be used for vector search. A 1536-dimensional vector cannot be meaningfully sorted into a one-dimensional sequence that preserves geometric proximity. Two vectors that are similar in 1536-dimensional space may appear at completely different positions in any linear ordering. The concept of closeness in high-dimensional space has no mapping to sorted order.

This is the fundamental limitation explained in depth in the why traditional indexes fail for vector search article.

HNSW: Graph-Based ANN for High-Dimensional Vectors

HNSW (Hierarchical Navigable Small World) organizes vectors in a layered graph. Each vector becomes a node. During index construction, each node is connected to its nearest neighbors. The graph has multiple layers: the top layer has few nodes and long-range connections for fast navigation; the bottom layer has all nodes and short-range connections for precise retrieval.

HNSW structure (simplified):
Layer 2:  [A] ----------- [F]
              \             |
Layer 1:  [A]-[B]-[C]   [D]-[E]-[F]
              |               |
Layer 0:  [A]-[B]-[C]-[X]-[D]-[E]-[F]  (all vectors, short connections)

At query time, the algorithm enters at the top layer, greedily moves toward the query vector through long-range connections, then descends to progressively lower layers for refinement. This reaches the approximate nearest neighbors in O(log n) comparisons rather than the O(n) comparisons required by brute force.

# pgvector: creating an HNSW index in PostgreSQL
# This is what a traditional DB looks like with vector search capability added

import psycopg2

conn = psycopg2.connect("postgresql://localhost/mydb")
cur = conn.cursor()

# Enable pgvector extension
cur.execute("CREATE EXTENSION IF NOT EXISTS vector")

# Create a table with a vector column
cur.execute("""
    CREATE TABLE IF NOT EXISTS documents (
        id          SERIAL PRIMARY KEY,
        content     TEXT NOT NULL,
        embedding   vector(1536),           -- pgvector type
        category    VARCHAR(50),
        created_at  TIMESTAMP DEFAULT NOW()
    )
""")

# Create HNSW index for fast ANN search
cur.execute("""
    CREATE INDEX IF NOT EXISTS documents_embedding_hnsw
    ON documents
    USING hnsw (embedding vector_cosine_ops)
    WITH (m = 16, ef_construction = 64)
""")

conn.commit()

According to Google Cloud's pgvector documentation, pgvector enables semantic search using vector embeddings to find results based on meaning rather than keyword matches. The extension brings the HNSW index structure directly into PostgreSQL, turning the same database that stores your structured operational data into a semantic search engine for that data.

IVF: Cluster-Based ANN as an Alternative

IVF (Inverted File Index) partitions the vector space into clusters using k-means. Each vector is assigned to its nearest cluster centroid. At query time, only the most similar clusters are searched, reducing computation while maintaining reasonable recall.

IVF trades marginally lower recall for lower memory overhead compared to HNSW. Qdrant, Milvus, and pgvector all support both. HNSW is the default recommendation for most production workloads because of its better recall-to-latency tradeoff at typical dataset sizes.

Consistency, Transactions, and ACID Guarantees

This is one of the sharpest differences between the two database families.

Relational Databases: Full ACID Compliance

ACID stands for Atomicity, Consistency, Isolation, and Durability. These four properties guarantee that database transactions are processed reliably even in the presence of failures.

-- A bank transfer in PostgreSQL — all-or-nothing guarantee
BEGIN;
    UPDATE accounts SET balance = balance - 500 WHERE account_id = 101;
    UPDATE accounts SET balance = balance + 500 WHERE account_id = 202;
    INSERT INTO transaction_log (from_id, to_id, amount) VALUES (101, 202, 500);
COMMIT;   -- if anything fails, the entire block rolls back

If the second UPDATE fails, the entire transaction rolls back. The money never leaves account 101. This guarantee is non-negotiable for financial data, inventory management, and any system where incorrect state is worse than a failed operation.

Vector Databases: Eventual Consistency Optimized for Throughput

Purpose-built vector databases like Pinecone, Weaviate, and Qdrant are designed for high-throughput vector ingestion and retrieval. They trade strict transactional guarantees for throughput and scalability. An upserted vector may not be immediately visible to a concurrent query. Index rebuilding happens asynchronously.

For RAG applications where documents are batch-ingested and freshness requirements are measured in minutes rather than milliseconds, eventual consistency is acceptable. For systems where a newly written record must be immediately readable by all subsequent queries, the eventual consistency model requires careful design.

pgvector within PostgreSQL gives you HNSW-indexed vector search with full ACID compliance. According to pgvector's GitHub documentation, pgvector uses the write-ahead log (WAL), which allows for replication and point-in-time recovery. A vector upsert inside a PostgreSQL transaction is atomic: it either commits with full consistency guarantees or rolls back entirely.

Scalability

Relational Databases: Vertical Scaling and Careful Sharding

A single PostgreSQL node scales vertically well. With enough RAM to cache the working set, B-tree lookups remain fast up to hundreds of millions of rows. Horizontal scaling requires explicit sharding strategies through tools like Citus or read replicas for query offloading.

For most operational workloads, a single well-tuned PostgreSQL instance is sufficient. The challenge arises when write throughput exceeds what one node can absorb, or when dataset size exceeds available RAM.

Vector Databases: Horizontal Distribution for Billion-Scale Search

Purpose-built vector databases are designed to distribute vector indexes across many nodes. Milvus supports sharding across multiple nodes, GPU-accelerated index building, and distributed ANN search. Pinecone is a managed service that handles all distribution transparently.

According to Northflank's pgvector guide, Weaviate can search through 10 million product embeddings in milliseconds, while pgvector might take seconds for the same operation at that scale. At datasets beyond tens of millions of vectors per node, purpose-built vector databases begin to justify their operational overhead.

For most teams, the scale threshold is higher than they expect. The DEV Community's vector vs pgvector comparison notes that many organizations find pgvector sufficient up to tens of millions of vectors, covering a large fraction of real-world production use cases.

A Direct Comparison

Dimension              | Relational Database     | Vector Database
-----------------------+-------------------------+---------------------------
Primary use case       | Transactional systems,  | Semantic search, RAG,
                       | reporting, structured   | recommendations, similarity
                       | queries                 | retrieval
Data model             | Tables, rows, columns   | Vectors + metadata payload
Schema                 | Rigid, predefined       | Flexible, schemaless
Query type             | Exact match, range,     | Approximate nearest
                       | join, aggregate         | neighbor by similarity
Index structure        | B-tree, hash, GiST      | HNSW, IVF
Query language         | SQL (ISO standard)      | Proprietary API / SDK
ACID compliance        | Full                    | Eventual consistency
(purpose-built)
Horizontal scaling     | Explicit sharding       | Native distribution
                       | required                | built-in
Joins                  | Native, efficient       | Not supported
Interpretability       | High (exact match)      | Lower (similarity score)
Tooling maturity       | 50+ years of ecosystem  | Rapidly maturing (2021+)
Operational cost       | Established workflows   | New operational surface

Where Each Database Wins

Use a Relational Database When

You are managing financial transactions, user accounts, inventory, orders, or any data where relational integrity matters. If your queries look like WHERE user_id = 42, JOIN orders ON customer_id, or GROUP BY region, PostgreSQL is the right tool.

You need ACID guarantees. A bank transfer that requires two updates to succeed or fail atomically cannot be reliably implemented in an eventually-consistent store.

Your team already knows SQL and your infrastructure already runs PostgreSQL. The operational overhead of adding a second database system has real cost.

You need complex aggregates, reporting, or analytics. SUM, COUNT, GROUP BY, WINDOW, and analytics queries are what relational databases and data warehouses are built for.

Use a Vector Database When

You need to find content that is semantically similar to a query regardless of exact word overlap. Document retrieval for RAG, product recommendations, and image similarity search all fall into this category.

Your content is unstructured: PDFs, HTML, audio transcripts, images, code. These do not fit naturally into rows and columns.

Your query is "find me the 10 most similar things to this" rather than "find me the thing with this exact ID."

You are building a RAG pipeline where an LLM needs context from a large private knowledge base.

Use Both

Most production AI applications use both databases simultaneously. The PostgreSQL database stores user accounts, subscription tiers, document metadata, and access control rules. The vector database stores the embeddings of the documents those users are allowed to access.

import psycopg2
from qdrant_client import QdrantClient, models

pg_conn     = psycopg2.connect("postgresql://localhost/app")
vector_db   = QdrantClient(host="localhost", port=6333)

def search_for_user(user_id: int, query: str, top_k: int = 5) -> list[dict]:
    """
    Semantic search with access control:
    1. Look up user permissions in PostgreSQL
    2. Use those permissions as a filter in vector search
    """
    cur = pg_conn.cursor()
    cur.execute(
        "SELECT allowed_categories FROM user_permissions WHERE user_id = %s",
        (user_id,)
    )
    row = cur.fetchone()
    if not row:
        return []

    allowed_categories = row[0]   # e.g. ["engineering", "product"]

    # Embed the query
    query_vector = embed(query)   # your embed() function from earlier

    # Search with category filter from PostgreSQL
    results = vector_db.search(
        collection_name="company-docs",
        query_vector=query_vector,
        query_filter=models.Filter(
            must=[models.FieldCondition(
                key="category",
                match=models.MatchAny(any=allowed_categories),
            )]
        ),
        limit=top_k,
        with_payload=True,
    )

    return [
        {
            "text":     hit.payload["text"],
            "category": hit.payload["category"],
            "score":    hit.score,
        }
        for hit in results
    ]

The PostgreSQL query provides access control. The vector search provides semantic retrieval. Neither database can do the other's job in this pipeline.

pgvector: The Bridge Between Both Worlds

For teams already running PostgreSQL, the pgvector extension is the most pragmatic starting point. It adds a vector data type and HNSW and IVFFlat ANN indexes to a standard PostgreSQL instance. You store embeddings alongside relational data and query both using SQL.

-- pgvector: semantic search with a relational filter in one SQL query
SELECT
    d.id,
    d.title,
    d.content,
    d.embedding <=> $1 AS cosine_distance   -- <=> is cosine distance operator
FROM documents d
JOIN document_permissions dp ON dp.document_id = d.id
WHERE dp.user_id = $2
  AND d.category = ANY($3)
  AND d.created_at > NOW() - INTERVAL '90 days'
ORDER BY cosine_distance
LIMIT 5;

The <=> operator is pgvector's cosine distance operator. It uses the HNSW index when one is available, falling back to exact search otherwise. The JOIN and WHERE clauses apply standard relational filtering before the vector comparison.

According to postgresqlhtx.com's pgvector analysis, with pgvector you manage one stack with the same Postgres backups, monitoring, migrations, HA/failover, and access control you already use. Schema changes and application code stay simpler because vectors live in the same database and can be joined with everything else using standard SQL.

The practical recommendation: start with pgvector if you are already on PostgreSQL and your vector corpus is in the low-to-mid millions. Consider a dedicated vector database when vector queries dominate your workload, your dataset grows into the hundreds of millions, or you need features like GPU-accelerated indexing or multi-modal search.

import psycopg2
import numpy as np
from sentence_transformers import SentenceTransformer

embed_model = SentenceTransformer("all-MiniLM-L6-v2")
conn        = psycopg2.connect("postgresql://localhost/mydb")
cur         = conn.cursor()

# Setup (run once)
cur.execute("CREATE EXTENSION IF NOT EXISTS vector")
cur.execute("""
    CREATE TABLE IF NOT EXISTS knowledge_base (
        id        SERIAL PRIMARY KEY,
        content   TEXT NOT NULL,
        source    VARCHAR(200),
        embedding vector(384)
    )
""")
cur.execute("""
    CREATE INDEX IF NOT EXISTS kb_embedding_idx
    ON knowledge_base
    USING hnsw (embedding vector_cosine_ops)
""")
conn.commit()

# Ingest a document
def add_document(content: str, source: str):
    vec = embed_model.encode(content, normalize_embeddings=True).tolist()
    cur.execute(
        "INSERT INTO knowledge_base (content, source, embedding) VALUES (%s, %s, %s)",
        (content, source, vec)
    )
    conn.commit()

# Semantic search
def search(query: str, top_k: int = 5) -> list[dict]:
    vec = embed_model.encode(query, normalize_embeddings=True).tolist()
    cur.execute("""
        SELECT id, content, source,
               1 - (embedding <=> %s::vector) AS cosine_similarity
        FROM knowledge_base
        ORDER BY embedding <=> %s::vector
        LIMIT %s
    """, (vec, vec, top_k))

    return [
        {"id": r[0], "content": r[1], "source": r[2], "similarity": float(r[3])}
        for r in cur.fetchall()
    ]

# Demo
add_document(
    "Refund requests must be submitted within 30 days of purchase.",
    "refund-policy.pdf"
)
add_document(
    "Connection timeout errors occur on slow or unreliable networks.",
    "troubleshooting.md"
)

results = search("how do I get my money back")
for r in results:
    print(f"[{r['similarity']:.4f}] {r['content'][:60]}...")

This is a fully functional semantic search system built entirely inside PostgreSQL with no additional infrastructure. For a startup adding a knowledge base feature to an existing product, this represents the lowest-friction path to production.

When pgvector Is No Longer Enough

pgvector has documented limitations worth planning around.

Scale ceiling. A single PostgreSQL node with pgvector handles millions of vectors well. As the vector corpus grows beyond tens of millions and query throughput increases, the node will need vertical scaling. According to Instaclustr's pgvector guide, pgvector is still under active development and may exhibit performance instability in some environments under heavy concurrent vector query loads.

Separation of concerns. Running heavy vector workloads on the same node as your operational database creates resource contention. A slow vector query that consumes CPU and memory will affect your transactional workloads. At sufficient scale, separating the two workloads onto different infrastructure makes sense.

Specialized features. Multimodal search, GPU-accelerated index building, and advanced filtering features are available in purpose-built vector databases before they reach pgvector. If your product roadmap requires those capabilities, evaluating Milvus, Weaviate, or Qdrant early avoids a migration later.

The vector database vs Elasticsearch article applies similar analysis to the specific case of teams running Elasticsearch, which has a different set of trade-offs than PostgreSQL.

The Production Architecture Pattern

The most common architecture for a production AI application using both database types looks like this:

User Request
     ↓
API Layer (FastAPI / Next.js)
     ↓
┌─────────────────────────────────────────────────────┐
│                  Application Layer                  │
│                                                     │
│  ┌─────────────────────────┐  ┌──────────────────┐  │
│  │   PostgreSQL / MySQL    │  │  Vector Database │  │
│  │   (or pgvector)         │  │  (Pinecone /     │  │
│  │                         │  │   Weaviate /     │  │
│  │  User accounts          │  │   Qdrant /       │  │
│  │  Subscriptions          │  │   Milvus)        │  │
│  │  Orders, transactions   │  │                  │  │
│  │  Document metadata      │  │  Document chunks │  │
│  │  Access control rules   │  │  Embeddings      │  │
│  │  Audit logs             │  │  Vector index    │  │
│  └─────────────────────────┘  └──────────────────┘  │
└─────────────────────────────────────────────────────┘
     ↓
LLM (context assembled from both sources)
     ↓
Grounded Response

PostgreSQL provides relational structure, transactional integrity, and access control. The vector database provides semantic retrieval at scale. The application layer joins their outputs before passing context to the LLM.

Summary

A relational database and a vector database answer different questions. SQL databases find records by exact match, range filter, or join. Vector databases find records by similarity in high-dimensional embedding space. Neither can do the other's job well.

The key structural differences come down to four dimensions. Data model: relational tables versus flexible vector and metadata. Index type: B-tree for sorted exact queries versus HNSW for high-dimensional ANN search. Consistency: full ACID transactions versus eventual consistency optimized for throughput. Scalability: vertical scaling with explicit sharding versus native horizontal distribution.

pgvector bridges both worlds for teams already running PostgreSQL. It adds vector storage and HNSW indexing to a standard PostgreSQL instance, enabling semantic search alongside relational queries in a single unified stack. It covers the large majority of production use cases below tens of millions of vectors per node.

The broader context for all of these tools is the vector database fundamentals pillar. The comparison against Elasticsearch, which covers different trade-offs than PostgreSQL, is the next article in the series: vector database vs Elasticsearch.

Sources and Further Reading

1. Yugabyte. What Is a Vector Database? Examples and Use Cases. yugabyte.com/blog/what-is-a-vector-database
2. Yugabyte. Using PostgreSQL as a Vector Database. yugabyte.com/key-concepts/using-postgresql-as-a-vector-database
3. Google Cloud. What Is pgvector? cloud.google.com/discover/what-is-pgvector
4. pgvector. GitHub Repository. github.com/pgvector/pgvector
5. Instaclustr. Vector Database vs. Relational Database: 7 Key Differences. instaclustr.com/education/vector-database/vector-database-vs-relational-database-7-key-differences
6. Instaclustr. pgvector: Key Features, Tutorial, and Pros and Cons. instaclustr.com/education/vector-database/pgvector-key-features-tutorial-and-pros-and-cons-2026-guide
7. Northflank. PostgreSQL Vector Search Guide with pgvector. northflank.com/blog/postgresql-vector-search-guide-with-pgvector
8. postgresqlhtx.com. pgvector vs Vector Database: When to Use Each. postgresqlhtx.com/what-is-pgvector-and-when-you-should-use-it-instead-of-a-dedicated-vector-db
9. DEV Community. Vector Databases vs. PostgreSQL with pgvector for RAG Setups. dev.to/simplr_sh/vector-databases-vs-postgresql-with-pgvector-for-rag-setups-1lg2
10. CrateDB. Vector Databases vs Traditional Databases for AI Applications. cratedb.com/blog/vector-databases-vs-traditional-databases-for-ai-applications
11. Designveloper. Vector Databases vs Traditional Databases: Key Components Comparison. designveloper.com/blog/vector-database-vs-traditional-database
12. Zilliz. Vector Databases vs. Key-Value Databases. zilliz.com/blog/vector-database-vs-key-value-databases
13. IEEE Computer Society. Vector Databases vs. Traditional Databases: The Data Landscape. computer.org/publications/tech-news/trends/vector-databases-vs-traditional-databases
14. GeeksforGeeks. pgvector. geeksforgeeks.org/data-science/pgvector