Advantages and Disadvantages of pg_vector Compared to Specialized Vector Databases

Advantages and Disadvantages of pg_vector Compared to Specialized Vector Databases

Vector databases are becoming increasingly popular thanks to the growing interest in applications utilizing machine learning and natural language processing. pg_vector is an extension for PostgreSQL that enables storing and searching vector data directly within a traditional relational database. But how does it compare to specialized vector databases like Milvus, Pinecone, Weaviate, or Qdrant?

✅ Advantages of pg_vector (PostgreSQL + pgvector extension)

1. Integration with PostgreSQL:
   pg_vector is an extension for PostgreSQL, which means you can leverage existing PostgreSQL infrastructure and tools. Easy integration with relational data—enabling you to combine traditional relational queries with vector searches.

2. Simple Deployment:
   If you are already using PostgreSQL, you can add pg_vector without the need to install and manage additional software. It works natively in PostgreSQL, meaning you can use standard tools for backup, monitoring, and management.

3. ACID Transaction Support:
   As part of PostgreSQL, it supports transactions with full consistency and resilience against failures.

4. Flexibility in Combining Vector and Relational Search:
   Ability to combine vector search with classic SQL queries using JOIN, WHERE, etc. Easy combination of multiple attributes (e.g., filtering results by category while performing vector search).

5. Popularity and Support:
   PostgreSQL is an extremely popular and well-supported database system with a large community. pg_vector is becoming a popular solution especially for small and medium-sized projects.

❌ Disadvantages of pg_vector Compared to Specialized Vector Databases

1. Performance (Speed):
   PostgreSQL was not designed for vector searches. High latency when searching large datasets (millions of vectors or more). Not optimized for fast ANN (Approximate Nearest Neighbor) searches.

2. Scalability:
   Limited scalability compared to systems like Pinecone or Milvus, which were designed for horizontal scaling. PostgreSQL is not ideal for distributed indexing and searching.

3. Search Algorithms:
   Specialized vector databases support advanced ANN algorithms (e.g., HNSW, IVF-PQ), which are significantly faster than simple L2 or cosine similarity available in pg_vector.

4. Index Management:
   Indexing in pg_vector is limited. Specialized databases offer dynamic indexes optimized for fast searching.

5. Community Support and Ecosystem:
   pg_vector is still relatively new and does not have as broad community support as Pinecone or Milvus.

🔍 When to Use pg_vector vs. Specialized DBs

Scenario                                                                              Recommendation
Small and medium datasets (up to a million vectors) ✅ pg_vector – Easy integration with PostgreSQL, good performance.
Combining relational and vector data                           ✅ pg_vector – SQL JOINs allow effective combination of both data types.
Horizontal scaling or ANN                                               ❌ Use Milvus, Pinecone, Weaviate – Optimized for fast searches.
Need for hybrid search                                                   ✅ pg_vector (simple cases) / Pinecone, Weaviate (advanced cases).
Highly optimized ANN queries                                     ❌ Use specialized databases like Milvus or Qdrant.

Conclusion

pg_vector is a great solution for small and medium-sized projects that want to combine traditional relational data with vector search. However, for larger projects with high demands on performance and scalability, specialized vector databases like Milvus, Pinecone, or Weaviate offer significantly better results.