Qdrant vs PG Vector
Compare deployment options, cost efficiency, and features to choose the right vector database for your application.
Database Comparison
Qdrant takes the lead.
Both Qdrant and PG Vector are powerful vector databases designed for efficient similarity search and storage. However, their deployment options and features differ in important ways.
Why Qdrant:
- Qdrant offers more deployment options
- PG Vector is more cost-effective
- Qdrant has 1 more strengths
Qdrant
Qdrant is an open-source vector database available as both a managed cloud service and a self-hosted solution. It offers strong HNSW performance, flexible deployment, and predictable cost structures, making it suitable for both startups and large-scale RAG workloads.
PG Vector
PG Vector is a popular Postgres extension that adds vector search capabilities directly inside a traditional relational database. It is ideal for teams that want to keep embeddings, metadata, and application data in one system without operating a separate vector database.
Feature Comparison
Infrastructure & Technical Details
| Feature | Qdrant | PG Vector |
|---|---|---|
| Deployment | Self-Hosted, Managed Cloud | Self-Hosted, Managed Cloud |
| Cost | Starts ~$0.014/hour for smallest node | Free (extension), infra cost only |
| License | Apache 2.0 | PostgreSQL |
| Index Types | HNSW, Sparse (dot similarity) | Flat, IVFFlat, HNSW |
| Cloud Providers | AWS, Azure, GCP | AWS RDS, Azure Postgres, GCP AlloyDB, Supabase, Timescale |
| Regional Flexibility | high | high |
| Strengths Count | 10 | 9 |
| Weaknesses Count | 5 | 7 |