Redis Vector Search vs Qdrant
Compare deployment options, cost efficiency, and features to choose the right vector database for your application.
Database Comparison
Qdrant takes the lead.
Both Redis Vector Search and Qdrant are powerful vector databases designed for efficient similarity search and storage. However, their deployment options and features differ in important ways.
Why Qdrant:
- Qdrant ranks higher overall
- Qdrant has more permissive licensing
- Redis Vector Search has 1 more strengths
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.
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.
Feature Comparison
Infrastructure & Technical Details
| Feature | Redis Vector Search | Qdrant |
|---|---|---|
| Deployment | Self-Hosted (Redis Stack), Redis Enterprise, Redis Cloud | Self-Hosted, Managed Cloud |
| Cost | Redis Stack: Free (self-host); Cloud: starts $5/mo; Enterprise: shard-based pricing; Redis Flex: hybrid RAM+SSD | Starts ~$0.014/hour for smallest node |
| License | RSALv2 / SSPLv1 / AGPLv3 | Apache 2.0 |
| Index Types | FLAT, HNSW | HNSW, Sparse (dot similarity) |
| Cloud Providers | AWS, Azure, GCP | AWS, Azure, GCP |
| Regional Flexibility | high | high |
| Strengths | 11 | 10 |
| Weaknesses | 12 | 5 |