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Redis for AI
Build AI apps with speed, memory, and accuracy
Redis for AI is our integrated package of features and services designed to get your GenAI apps into production faster with the fastest vector database, robust integrations, and worldwide scale.
Improve RAG & search with the world’s fastest vector database
Get more accurate answers using retrieval-augmented generation (RAG), get the fastest responses on the market, and work with top ecosystem partners like LangChain and LlamaIndex.
Personalize AI responses with long-term memory
LLMs don’t retain recent history, which can cause awkward interactions. We store all previous interactions between an LLM and a user to deliver personalized GenAI experiences.
Short-term memory for faster AI agents
As GenAI systems get more complex, they use multiple agents, data retrievals, and LLM calls to complete tasks. Every step adds lag. We make agents faster, so you get higher-performing apps.
Reduce redundant LLM calls with semantic caching
Store the semantic meaning of frequent calls to LLMs so apps can answer commonly asked questions more quickly and lower LLM inference costs.
Choose the right tool with semantic routing
Route queries based on meaning to provide precise, intent-driven results for chatbots, knowledge bases, and agents. Semantic routing classifies requests across multiple tools to quickly find the most relevant answers.
Faster predictions with ML feature store
We store ML features for fast data retrieval to power timely predictions. Our feature store connects seamlessly with offline feature stores like Tecton and Feast at the scale companies need for instant decisions worldwide.
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Frequently asked questions
Why use Redis over traditional databases for AI?
Traditional 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.
How does Redis compare to specialized vector databases for AI?
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.
What indexing methods does Redis use for vector search?
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.
How does Redis ensure data persistence for AI workloads?
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.
Where can I learn more about how to use Redis for AI?
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.