CodersSecret

Course guide

Vector Database Comparison: Qdrant vs Pinecone vs pgvector vs Weaviate

Compare the top vector databases for RAG systems: Qdrant, Pinecone, pgvector, Weaviate, Milvus, and ChromaDB. Performance, features, pricing, and when to use each.

Vector databases store and search high-dimensional embeddings for semantic similarity. Choosing the right one depends on your scale, deployment model, and feature requirements.

Quick Comparison

DatabaseTypeBest ForOpen Source
QdrantDedicated vector DBProduction, high performance, filteringYes
pgvectorPostgreSQL extensionExisting PG users, small-medium scaleYes
PineconeManaged cloudZero-ops, fast startNo
WeaviateMulti-modalText + images, GraphQL APIYes
MilvusDistributedBillion-scale datasetsYes
ChromaDBEmbeddedPrototyping, local devYes

Learn Vector Database Engineering

Module 4 covers vector database internals, indexing algorithms, and production deployment.

Learning path

Continue from concept to implementation

These course modules place this topic in context, connect it to adjacent decisions, and provide the practical next step.

Module 3

Embeddings Deep Dive

Embedding models, optimization strategies, and choosing the right model for your use case

3 hours | 4 learning objectives

Module 4

Vector Databases Engineering

ANN algorithms, indexing, metadata filtering, and production deployment with Qdrant and pgvector

4 hours | 4 learning objectives

Module 5

Document Processing & Chunking

Chunking strategies, data cleaning, metadata enrichment, and building ingestion pipelines

3.5 hours | 4 learning objectives

Practical outcomes

What the recommended module teaches

  • Understand ANN algorithms (HNSW, IVF) and their tradeoffs
  • Deploy and operate Qdrant for production vector search
  • Use pgvector for PostgreSQL-integrated vector search
  • Design metadata filtering and multi-tenancy strategies