Vector Database

Build intelligent, AI-powered applications with Redis Enterprise.

Use AI to reimagine search across unstructured data

Users expect search functionality in every application and website they encounter. Yet more than 80% of business data is unstructured, stored as text, images, audio, video, or other formats.

Organizations need to reimagine the ways to make every kind of data discoverable – not least of which is because users demand it. Powerful search features will fuel the next generation of applications.

What is a vector database?

A vector database is a type of database that stores data in the form of vectors or mathematical representations of data points. AI and machine learning is what is enabling this transformation of unstructured data into numeric representations (vectors) that capture meaning and context, benefiting from advances in natural language processing and computer vision.

Vector Similarity Search (VSS) is a key feature of a vector database. It is the process of finding data points that are similar to a given query vector in a vector database. Popular VSS uses include recommendation systems, image and video search, natural language processing, and anomaly detection. For example, if you build a recommendation system, you can use VSS to find (and suggest) products that are similar to a product in which a user previously showed interest. (Need a deep dive? This should do it.)

Why vector similarity search is a crucial component
for vector databases

Traditional keyword matching and filtering only takes you so far. Sure, ordinary search algorithms are useful for text and document use cases. However, search results are limited when they do not incorporate meaning or context. The proliferation of unstructured data created a huge gap in the effectiveness of traditional keyword matching and filtering. Every organization that stores non-textual data – and that’s just about everyone – can benefit from improving search functionality across unstructured data. But until recently, only a handful of large cloud-native tech companies had this capability.

New to Vector Similarity Search? Download our cheat sheet.

Redis Enterprise: the vector database
solution for every organization

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Real-time search performance

Search and recommendation systems have to run incredibly fast. The VSS functionality in Redis Enterprise guarantees low search latency, whether the data collection is tens of thousands or hundreds of millions of objects, distributed across a number of database nodes.

Built-in fault tolerance and resilience

To ensure your search applications never experience downtime, Redis Enterprise uses a shared-nothing cluster architecture. It is fault tolerant at all levels, with automated failover at the process level, for individual nodes, and across infrastructure availability zones. To ensure your unstructured data and vectors are never lost, Redis Enterprise includes tunable persistence and disaster recovery mechanisms.

Reduce architectural and application complexity

Most likely, your organization already benefits from Redis Enterprise for its caching needs. Instead of spinning up yet another costly single-point solution, extend your database to take advantage of VSS in your applications. Developers can store vectors just as easily as any other field in a Redis hash or JSON object.

Flexibility across clouds and geographies

Choose where your databases should run. Redis Enterprise can be deployed anywhere, on any cloud platform, on-premises, or in a multi-cloud or hybrid cloud architecture.

Vector similarity search features

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Vector indexing algorithms

Redis Enterprise manages vectors in an index data structure to enable intelligent similarity search that balances search speed and search quality. Choose from two popular techniques, FLAT (a brute force approach) and HNSW (a faster, and approximate approach), based on your data and use cases.

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Vector search distance metrics

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

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Powerful hybrid filtering

Take advantage of the full suite of search features available in Redis Enterprise query and search. Enhance your workflows by combining the power of vector similarity with more traditional numeric, text, and tag filters. Incorporate more business logic into queries and simplify client application code.

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Real-time updates

Real-time search and recommendation systems generate large volumes of changing data. New images, text, products, or metadata? Perform updates, insertions, and deletes to the search index seamlessly as your dataset changes overtime. Redis Enterprise reduces costly impacts of stagnant data.

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Vector range queries

Traditional vector search is performed by finding the “top K” most similar vectors. Redis Enterprise also enables the discovery of relevant content within a predefined similarity range or threshold for an alternative, and offers a more flexible search experience.

Use cases


Redis Enterprise helps recommendation engines deliver fresh, relevant suggestions to users at low-latency. It helps them find similar products to those that a shopper enjoys.

Document search

Redis Enterprise makes it easier to discover and retrieve information from a large corpus of documents, using natural language and semantic search.

Question answering

Redis Enterprise helps Q&A systems leverage semantic search and generative AI workflows in knowledge bases with popular models from OpenAI and Cohere.

Featured customers

Ecosystem collaborators and integrations

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