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Join us at Redis Released

The fastest storage engine for your AI apps

Build real-time apps that exceed expectations for responsiveness and include complex processing with low latency.

The faster the app, the better the user experience. Happy users mean increased revenue. The speed and unparalleled flexibility of Redis allows businesses to adapt to constantly shifting technology needs, especially in the AI space. Redis vector search provides a foundation for AI applications ranging from recommendation systems to document chat.

Designed for your use cases

Chatbots with RAG

Ground chatbots in your data using Retrieval Augmented Generation (RAG) to enhance the quality of LLM responses.

Semantic caching

Identify and retrieve cached LLM outputs to reduce response times and the number of requests to your LLM provider, which saves time and money.

Recommendation systems

Power recommendation engines with fresh, relevant suggestions at low-latency, and point your users to the products they’re most likely to buy.

Document search

Make it easier to discover and retrieve information across documents and knowledge bases, using natural language and semantic search.

Why Redis vector search?


Redis’s superior speed and throughput improves the user experience and ROI, allowing for additional enrichments within the required response window.


Tech stacks constantly evolve as Gen AI advances. Rich support for integrations and diverse data structures allows devs to bring apps to production quickly across multi-cloud and hybrid deployments.


Reliable, secure systems reduce risk, accelerating adoption and innovation for companies and enabling production scale and high availability across regions.


Get started with easy-to-use code that gets you up and running quickly. See for yourself why Redis is the “Most Loved Database”.

Popular resources



Agentic RAG: Semantic caching with Redis and LlamaIndex

With Redis and LlamaIndex, customers can build faster, more accurate chatbots at scale while optimizing cost. Join this session to learn the latest best practices.



The Future of RAG: Exploring Advanced LLM Architectures with LangChain and Redis

See LangChain’s role in facilitating RAG-based applications, advanced agent orchestration techniques, and the critical role of Redis Enterprise in real-time applications.

Blog Post


Introducing the Redis Vector Library for Enhancing GenAI Development

Redis Vector Library simplifies the developer experience of our vector offering by providing a streamlined client experience that enhances Generative AI application development.

Ecosystem collaborators and integrations

Our customers tell good stories

langchain logo

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 Chase
Co-Founder and CEO

The best of VS

Vector indexing algorithms

We manage vectors in an index data structure with intelligent similarity search that balances search speed and quality. Choose from two popular techniques, FLAT (a brute force approach) and HNSW (a faster approximate approach).

Powerful hybrid filtering

You get the best of both worlds and enhance your workflows by combining the power of vector search with more traditional numeric, text, and tag filters. Incorporate more business logic into queries and simplify client app codes.

Vector range queries

While traditional vector search finds the “top K” most similar vectors, we extend the relevant data discovered through a predefined similarity range that provides a more flexible search experience.

Vector search distance metrics

We use a distance metric to measure the similarity between two vectors. Choose from three popular metrics – EuclideanInner Product, and Cosine Similarity – used to calculate how “close” or “far apart” two vectors are.

Real-time updates

We can keep your search and recommendation systems fresh, relevant, and cost effective with updates, insertions, deletions in your search index as your dataset changes over time.

Put your VS to work