PG Vector vs Redis Vector Search
Compare deployment options, cost efficiency, and features to choose the right vector database for your application.
Database Comparison
PG Vector takes the lead.
Both PG Vector and Redis Vector Search are powerful vector databases designed for efficient similarity search and storage. However, their deployment options and features differ in important ways.
Why PG Vector:
- PG Vector ranks higher overall
- Redis Vector Search offers more deployment options
- PG Vector is more cost-effective
- PG Vector has more permissive licensing
- Redis Vector Search has 2 more strengths
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.
Redis Vector Search
Redis provides vector similarity search through Redis Stack (RediSearch module), enabling low-latency semantic search and RAG applications. As an in-memory database, Redis excels at small-to-medium scale vector workloads requiring ultra-low latency. It integrates vector search with Redis's core data structures, making it ideal for real-time AI applications, semantic caching, and RAG systems.
Feature Comparison
Infrastructure & Technical Details
| Feature | PG Vector | Redis Vector Search |
|---|---|---|
| Deployment | Self-Hosted, Managed Cloud | Self-Hosted (Redis Stack), Redis Enterprise, Redis Cloud |
| Cost | Free (extension), infra cost only | Redis Stack: Free (self-host); Cloud: starts $5/mo; Enterprise: shard-based pricing; Redis Flex: hybrid RAM+SSD |
| License | PostgreSQL | RSALv2 / SSPLv1 / AGPLv3 |
| Index Types | Flat, IVFFlat, HNSW | FLAT, HNSW |
| Cloud Providers | AWS RDS, Azure Postgres, GCP AlloyDB, Supabase, Timescale | AWS, Azure, GCP |
| Regional Flexibility | high | high |
| Strengths | 9 | 11 |
| Weaknesses | 7 | 12 |