Redis Vector
Sub-millisecond vector search in memory
About Redis Vector
Redis Vector extends Redis with vector similarity search capabilities, enabling real-time semantic search with extremely low latency. Because Redis operates in memory, it provides fast query performance suitable for latency-sensitive applications such as AI assistants, recommendation engines, and real-time personalization. Redis Vector integrates vector search with Redis's existing data structures, allowing developers to build fast, scalable AI applications.
Key features
Pricing
Common use cases
Common questions about Redis Vector
How do I add vector search to Redis Vector?
Redis Vector includes vector search capabilities through extensions or built-in features. Check the official documentation for installation and configuration instructions.
Should I use Redis Vector for vector search?
If you're already using Redis Vector, adding vector search can be simpler than introducing a new database. However, dedicated vector databases may offer better performance and features at scale.
What are the main use cases for Redis Vector?
Redis Vector is commonly used for real-time recommendations, chatbot session memory, low-latency semantic search, and similar applications requiring semantic similarity search.
Does Redis Vector integrate with popular AI tools?
Most vector databases integrate with LangChain, LlamaIndex, and popular embedding providers. Check the Redis Vector documentation for specific integration guides and examples.
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Not sure if Redis Vector is right for you?
Compare it side-by-side with other vector databases to find the best fit for your project.