Redis Vector Search vs Weaviate
Compare deployment options, cost efficiency, and features to choose the right vector database for your application.
Database Comparison
Redis Vector Search takes the lead.
Both Redis Vector Search and Weaviate are powerful vector databases designed for efficient similarity search and storage. However, their deployment options and features differ in important ways.
Why Redis Vector Search:
- Redis Vector Search offers more deployment options
- Redis Vector Search has 3 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.
Weaviate
Weaviate is a cloud and self-hosted vector database offering hybrid dense+sparse search, strong metadata filtering, and a modular storage layer. It is designed for enterprise and production RAG workloads that require flexibility and scalable cloud hosting.
Feature Comparison
Infrastructure & Technical Details
| Feature | Redis Vector Search | Weaviate |
|---|---|---|
| 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 | Free (self-host), cloud starts ~$25/mo |
| License | RSALv2 / SSPLv1 / AGPLv3 | BSD |
| Index Types | FLAT, HNSW | HNSW, Hybrid dense+sparse |
| Cloud Providers | AWS, Azure, GCP | AWS, GCP |
| Regional Flexibility | high | low |
| Strengths | 11 | 8 |
| Weaknesses | 12 | 6 |