Vector Database Comparison Matrix

Criteria \ Database	Qdrant	pgvector	Weaviate	Milvus	Elasticsearch
Performance	✅ Great performance with simple deployment. Single-node setup sufficient for your scale. Rust implementation handles concurrent queries well	✅ Sufficient for your current scale. Easy integration with existing PostgreSQL if you're using it	✅ Good performance for your scale. GraphQL makes querying intuitive	❗ Overkill for current scale. Distributed architecture adds unnecessary complexity	❗ Complex setup for pure vector search. Resource-heavy for your needs
Scalability	✅ Easy to start small and scale later. Simple cluster setup when needed	✅ Vertical scaling sufficient for your scale. Can handle 50k users on single instance	✅ Can grow with your needs. Simple replication setup	❗ Distributed architecture adds overhead for your current scale	❗ Complex cluster setup. Overkill for current needs
Cost	✅ Low resource needs. Efficient for single-node deployment.	✅ Minimal if already using PostgreSQL. Low resource footprint	⚠️ Moderate resource needs. Still manageable on single EC2	❗ Higher resource overhead. Requires more complex infrastructure	❗ Resource-intensive. Higher operational costs
Deployment	✅ A straightforward Docker Compose configuration or a Kubernetes deployment can be used for scalability. Both alternatives come with documentation tailored to your specific needs. Easy monitoring	✅ Simple extension installation. Works with existing DB tools	⚠️ Docker Compose available but more complex than Qdrant	❗ Kubernetes preferred. Docker Compose possible but not optimal	❗ Complex setup even with Docker Compose
Reliability	✅ Solid backup/restore. Good for single-node deployment	✅ Mature PostgreSQL reliability. Well-known backup tools	✅ Good reliability with simple setup	⚠️ More complex reliability features than needed	❗ Complex maintenance requirements
Accuracy	✅ Good accuracy with HNSW. Easy parameter tuning for ML team	✅ Basic but sufficient vector operations. Good for ML experimentation	✅ Good accuracy with intuitive configuration	✅ Excellent accuracy but complex tuning	✅ Good accuracy with complex tuning options
Security	✅ Simple API key auth. Sufficient for your scale	✅ Mature PostgreSQL security. Easy to implement	✅ Good security features. Easy to configure	⚠️ More complex than needed for your scale	❗ Complex security setup
Developer Experience	✅ Clean REST API. Perfect for small team. Good docs for ML integration	✅ Familiar SQL interface. Simple for basic operations	✅ GraphQL API. Good for UI integration	⚠️ Steeper learning curve. More docs to handle	❗ Steep learning curve. Complex for small team

Best Suited For Your Case

1. Recommended: Qdrant

   • Perfect fit for your Docker Compose setup
   • Simple deployment and maintenance
   • Good performance for your scale
   • Easy for ML team to work with
   • Room to grow when needed

2. Alternative: pgvector

   • If you're already using PostgreSQL
   • Simplest deployment option
   • Sufficient for your current scale
   • Easy integration with existing tools

3. Alternative: Weaviate

   • If you need more sophisticated querying
   • Good UI integration with GraphQL
   • Might be helpful for UI developer
   • Still manageable for your scale

Key Considerations for Your Setup

1. Deployment Priority

   • Qdrant > pgvector > Weaviate > Milvus > Elasticsearch
   • Docker Compose compatibility is best with Qdrant
   • pgvector is simplest if you have PostgreSQL

2. Team Expertise Fit

   • ML Team: All options work well, with Qdrant having good ML-focused documentation
   • DevOps: Qdrant or pgvector require minimal DevOps overhead
   • UI Developer: Weaviate's GraphQL or Qdrant's REST API are most frontend-friendly

3. Growth Path

   • Current scale (10k-50k users):
     • Single node deployment sufficient
     • All recommended options can handle this easily
   • Future growth:
     • Qdrant: Easy path to clustering when needed
     • pgvector: Vertical scaling, then potential migration
     • Weaviate: Straightforward horizontal scaling

4. Operational Considerations

   • Monitoring: All three top options work with standard Docker monitoring
   • Backup: All support Docker volume backups
   • Security: API keys sufficient for your scale
   • Updates: All have good Docker-based update paths

Implementation Recommendations

1. Start with single-node deployment
2. Use Docker Compose for orchestration
3. Implement regular volume backups
4. Set up basic monitoring with Prometheus/Grafana
5. Plan for vertical scaling first, then horizontal if needed

❗ Avoid for Your Case

• Milvus: Overkill for your scale, adds unnecessary complexity
• Elasticsearch: Too heavy and complex for your current needs and team size