The real-time context engine for AI
LLMs and AI apps need just the right data at the right time to provide quality responses. Search, gather, and serve the right context for LLMs with the unified platform you already know and love.
Get accurate responses with hybrid search
Enterprises need their search engines to combine filtering and exact matching with vector search in a high-performance, scalable way. Vector-only databases can’t keep up and result in bad answers and architectural redesigns.
Improve RAG with the fastest vector database
Give users fast answers with retrieval-augmented generation (RAG) from our benchmark-leading vector database and configure search the way you want.
Recall key memories for agents
Assembling the right context for LLMs takes a thoughtful approach to identifying, summarizing, and retrieving relevant memories to deliver useful outputs. We manage it for you and work with leading third-party frameworks.
Cut LLM cost calls with semantic caching
Store the semantic meaning of frequent calls to LLMs so apps can answer commonly asked questions faster with lower LLM inference costs.
Serve real-time ML features with feature store
Deliver live features, like user behavior or risk scores, to your models with sub-millisecond latency. Our feature store orchestrates batch, streaming, and on-demand pipelines.
Companies that trust Redis for AI
Built on Redis
Use the Redis you know and love. No additional contracts or security reviews.
Connects to GenAI ecosystem
Integrate with top GenAI tools so you can build how you want.
Pre-built libraries
Don’t start from scratch. RedisVL automates core functionality for you.
Sample notebooks
Explore our use cases with ecosystem integrations to start building faster.
Get started
Meet with an expert and start using Redis for AI today.
Frequently asked questions
More questions? See our Docs pageTraditional databases often introduce latency due to disk-based storage and complex indexing. Redis, being in-memory, drastically reduces query times and supports real-time AI apps by efficiently handling searches, caching results, and maintaining performance at scale.
Unlike dedicated vector databases, Redis offers multi-modal capabilities—handling vector search, real-time caching, feature storage, and pub/sub messaging in a single system. This eliminates the need for multiple tools, reducing complexity and cost.
Redis supports HNSW (Hierarchical Navigable Small World) for fast approximate nearest neighbor (ANN) search and Flat indexing for exact search. This flexibility allows AI applications to balance speed and accuracy based on their needs.
Redis offers RDB (snapshotting) and AOF (Append-Only File) persistence options, ensuring AI-related data remains available even after restarts. Redis on Flex further enables larger data sets to persist cost-effectively.
You can see AI training courses on Redis University. Our Docs page for AI explains concepts, resources, and includes many howtos for building GenAI apps like AI assistants with RAG and AI agents.