"We would not have been able to scale ChatGPT without Redis."
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.
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"Using Redis, Bank of America has built fast, high-quality digital experiences for their clients at scale, from use cases like caching and session management, to event streaming and AI infrastructure."
"We’re using Redis Cloud for everything persistent in OpenGPTs, including as a vector store for retrieval and a database to store messages and agent configurations. The fact that you can do all of those in one database from Redis is really appealing.”
Harrison ChaseCEO
Read more"Better answers and more current real-time information with up to 2.35X better performance with the Xeon 6 and Redis."
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.
Companies that trust Redis for AI
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Frequently asked questions
More questions? See our Docs pageWhy 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.