Redis : In-memory database with vector search capabilities
Redis: in summary
Redis is an open-source, in-memory data store widely used for caching, real-time analytics, and as a primary database for low-latency applications. With the addition of vector similarity search via Redis Vector (formerly part of Redisearch), Redis has evolved into a multi-purpose platform that supports modern AI and machine learning workloads, including semantic search, recommendation engines, and hybrid retrieval systems.
Redis combines its core strengths—speed, simplicity, and in-memory performance—with the ability to store and query high-dimensional vector embeddings, making it suitable for AI applications that require fast, real-time results over small to medium vector datasets.
Key benefits include:
Fast vector search using HNSW (Hierarchical Navigable Small World) indexing
Hybrid queries combining structured filters and vector similarity
Native integration with Redis ecosystem and real-time operations
What are the main features of Redis for vector search?
In-memory vector storage and retrieval
Redis supports storing dense vector embeddings as fields within documents, allowing ultra-fast access times.
Ideal for use cases needing low-latency inference (e.g., chatbots, semantic ranking)
Works well with datasets that fit into memory
Supports vectors in float32 or binary formats
Similarity search with HNSW indexing
Redis uses the HNSW algorithm for approximate nearest neighbor (ANN) vector search.
Efficient k-NN search in high-dimensional spaces
Configurable parameters to balance accuracy and performance
Scalable for small to medium-sized datasets (typically <100M vectors)
Hybrid queries with filtering and scoring
Redis enables hybrid search, combining vector similarity with traditional filters and metadata.
Query syntax supports numeric, tag, and geo filters alongside vector scores
Useful for e-commerce, recommendation, and personalized search
Results can be ranked using a combination of vector distance and field relevance
Real-time updates and low-latency performance
As an in-memory store, Redis delivers sub-millisecond response times for reads and writes.
Supports real-time vector ingestion, updates, and deletions
No need to rebuild indexes after every update
Suitable for applications that require fast feedback loops
Part of a modular and extensible ecosystem
Vector search in Redis is available via Redis Stack, which also includes search, JSON, time series, and graph modules.
Works seamlessly with Redis JSON to store structured data and embeddings together
Can be deployed on-premises or via Redis Cloud
Integrates with popular frameworks like LangChain, OpenAI, and Hugging Face
Why choose Redis for vector search?
Blazing-fast performance: Built for real-time, low-latency applications
Multi-model database: Store vectors alongside structured, time series, or document data
Open source and cloud-ready: Flexible deployment options
Hybrid search support: Combine structured filters with vector similarity
Strong ecosystem: Backed by a large community, tools, and language clients
Redis: its rates
Standard
Rate
On demand
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